From 40ee98e82901c68f1d53fc1f925e1c594f895fba Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 16:13:29 -0400
Subject: [PATCH 01/23] feat: add LFM2-Audio / LFM2.5-Audio speech input
 support

LFM2-Audio pairs a FastConformer speech encoder with an LFM2 decoder.
Nothing here could read its checkpoints or turn a sound into something
the model could read.

- Adds audio handling for LFM2-Audio: turn the sound into log-mel frames
  the way the original does, hand them to the speech encoder, and splice
  what comes back into the prompt.
- Swaps each `<|audio|>` marker in the prompt for the right number of
  empty slots, so the sound and the text line up. The prompt has to carry
  exactly one marker per clip, in clip order; any other count is refused
  up front rather than guessed at. One prompt at a time: a one-item list
  works, longer lists are refused instead of silently losing the text.
- Reads these checkpoints in the model builder. They have no model type
  and no transformers class: the text half sits nested inside the config
  next to the encoder and the speech-output parts, so it is picked out by
  hand and everything else left alone.
- Registers the two new model names, so the loader knows how to run them:
  one for the speech model, one for its text half.
- Several clips in one prompt share the prompt but not the encoder run:
  the published encoder was exported for one clip at a time, so each clip
  is run on its own frames and the results joined in order.
- Adds a tutorial. Known gap: the model can also speak, and that is not
  supported here; it answers in text only.
---
 .gitignore                                    |   2 +
 README.md                                     |   2 +-
 examples/python/common.py                     |   5 +
 examples/python/lfm2-audio-tutorial.md        | 264 +++++++++
 src/config.cpp                                |   2 +
 src/config.h                                  |   2 +
 src/generator/generators.cpp                  |   2 +-
 src/models/model.cpp                          |   2 +
 src/models/model_type.h                       |  14 +-
 src/models/multi_modal.cpp                    | 141 ++++-
 src/models/multi_modal.h                      |  28 +-
 .../preprocessing/lfm2_audio_processor.cpp    | 320 +++++++++++
 .../preprocessing/lfm2_audio_processor.h      |  66 +++
 .../preprocessing/multi_modal_processor.cpp   |   2 +
 src/python/py/models/README.md                |   2 +-
 src/python/py/models/builder.py               |  11 +-
 src/python/py/models/builders/__init__.py     |   4 +-
 src/python/py/models/builders/lfm2.py         |  87 +++
 src/python/python.cpp                         |  23 +-
 test/CMakeLists.txt                           |   2 +
 test/cpp/lfm2_audio_frames_test.cpp           | 157 +++++
 test/models/lfm2-audio/dummy_embedding.onnx   | Bin 0 -> 132039 bytes
 test/models/lfm2-audio/dummy_speech.onnx      | Bin 0 -> 17148 bytes
 test/models/lfm2-audio/dummy_text.onnx        | Bin 0 -> 133917 bytes
 test/models/lfm2-audio/genai_config.json      |  88 +++
 test/models/lfm2-audio/tokenizer.zip          | Bin 0 -> 879624 bytes
 test/python/builder/test_lfm2_audio.py        | 179 ++++++
 test/python/builder/test_qmoe_weights.py      |   3 +
 .../create/create_dummy_lfm2_audio_models.py  | 223 +++++++
 test/python/models/test_lfm2_audio_models.py  | 542 ++++++++++++++++++
 test/python/test_onnxruntime_genai_api.py     |  20 +
 31 files changed, 2174 insertions(+), 19 deletions(-)
 create mode 100644 examples/python/lfm2-audio-tutorial.md
 create mode 100644 src/models/preprocessing/lfm2_audio_processor.cpp
 create mode 100644 src/models/preprocessing/lfm2_audio_processor.h
 create mode 100644 test/cpp/lfm2_audio_frames_test.cpp
 create mode 100644 test/models/lfm2-audio/dummy_embedding.onnx
 create mode 100644 test/models/lfm2-audio/dummy_speech.onnx
 create mode 100644 test/models/lfm2-audio/dummy_text.onnx
 create mode 100644 test/models/lfm2-audio/genai_config.json
 create mode 100644 test/models/lfm2-audio/tokenizer.zip
 create mode 100644 test/python/builder/test_lfm2_audio.py
 create mode 100644 test/python/create/create_dummy_lfm2_audio_models.py
 create mode 100644 test/python/models/test_lfm2_audio_models.py

diff --git a/.gitignore b/.gitignore
index 8ae4ae3b23..033ba3233b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -30,6 +30,8 @@ examples/python/hf_cache
 examples/csharp/ModelChat/models
 
 !test/models/gemma4/*
+!/test/models/lfm2-audio/
+!test/models/lfm2-audio/*
 !test/models/hf-internal-testing/*
 !test/models/lfm2-vl/*
 !test/models/marian-batch/*
diff --git a/README.md b/README.md
index a4b7251a1f..478b565734 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ See documentation at the [ONNX Runtime website](https://onnxruntime.ai/docs/gena
 
 | Support matrix | Supported now | Under development | On the roadmap|
 | -------------- | ------------- | ----------------- | -------------- |
-| Model architectures | AMD OLMo <br/> ChatGLM <br/> DeepSeek <br/> ERNIE 4.5 <br/> Fara <br/> Gemma <br/> gpt-oss <br/> Granite <br/> Granite MoE Hybrid <br/> HunYuan Dense V1 <br/> InternLM2 <br/> LFM2 (language + vision) <br/> Llama <br/> Mistral <br/> Nemotron <br/> Phi (language + vision) <br/> Qwen (language + vision) <br/> SmolLM3 <br/> Whisper | Stable diffusion | Multi-modal models |
+| Model architectures | AMD OLMo <br/> ChatGLM <br/> DeepSeek <br/> ERNIE 4.5 <br/> Fara <br/> Gemma <br/> gpt-oss <br/> Granite <br/> Granite MoE Hybrid <br/> HunYuan Dense V1 <br/> InternLM2 <br/> LFM2 (language + vision + speech input) <br/> Llama <br/> Mistral <br/> Nemotron <br/> Phi (language + vision) <br/> Qwen (language + vision) <br/> SmolLM3 <br/> Whisper | Stable diffusion | Multi-modal models |
 | API | Python <br/>C# <br/>C/C++ <br/> Java ^ | Objective-C ||
 | O/S | Linux <br/> Windows <br/>Mac  <br/>Android || iOS |||
 | Architecture | x86 <br/> x64 <br/> arm64 ||||
diff --git a/examples/python/common.py b/examples/python/common.py
index bf23a6914e..a5f408a0a6 100644
--- a/examples/python/common.py
+++ b/examples/python/common.py
@@ -322,6 +322,11 @@ def get_user_content(model_type: str, num_images: int, num_audios: int, prompt:
         # per projected vision feature, and <|image_end|>, based on the resized image's patch grid.
         image_tags = "".join(["<image>" for _ in range(num_images)])
         content = image_tags + prompt
+    elif model_type == "lfm2_audio":
+        # LFM2-Audio: the C++ audio processor replaces each <|audio|> with one placeholder per
+        # encoder frame (one per 80 ms of audio) and splices the encoder output in at those positions.
+        audio_tags = "".join(["<|audio|>" for _ in range(num_audios)])
+        content = audio_tags + prompt
     elif model_type == "mistral3":
         # Pixtral / Ministral-3 VLM: the C++ image processor expands each
         # [IMG] into the full token sequence based on image resolution.
diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
new file mode 100644
index 0000000000..edfbc5dbfc
--- /dev/null
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -0,0 +1,264 @@
+# Build your LFM2-Audio / LFM2.5-Audio ONNX models for ONNX Runtime GenAI
+
+LFM2-Audio pairs a FastConformer speech encoder (based on
+[nvidia/canary-180m-flash](https://huggingface.co/nvidia/canary-180m-flash)) with an LFM2 decoder
+(the hybrid conv/attention model described in
+[the LFM2 support PR](https://github.com/microsoft/onnxruntime-genai/pull/1979)). ONNX Runtime GenAI
+runs the speech-to-text half of it — ASR and spoken-prompt chat — as three ONNX models:
+
+| Model | Inputs | Outputs |
+| --- | --- | --- |
+| `speech.onnx` | `mel_spectrogram`, `mel_lengths` | `audio_embeddings`, `audio_lengths` |
+| `embeddings.onnx` | `input_ids`, `audio_features` | `inputs_embeds` |
+| `model.onnx` (decoder) | `inputs_embeds`, `attention_mask`, `position_ids`, KV + conv cache | `logits` |
+
+Only the decoder is produced by the model builder in this repository; the speech encoder is
+published as ONNX by LiquidAI, and the embedding model is a small graph you build once.
+
+**Audio output is not supported.** The model can also *speak*: it emits audio codes through an
+RQ-transformer ("depthformer") that ONNX Runtime GenAI has no generation loop for, and those codes
+then need the audio detokenizer and an inverse STFT to become a waveform. The export below drops the
+depthformer and the audio embeddings with it, so the model answers in text only. See
+[Known limitations](#5-known-limitations).
+
+## Steps
+
+1. [Build the decoder](#1-build-the-decoder)
+2. [Get the speech encoder and build the embedding model](#2-get-the-speech-encoder-and-build-the-embedding-model)
+3. [Write `genai_config.json`](#3-write-genai_configjson)
+4. [Run the model](#4-run-the-model)
+5. [Known limitations](#5-known-limitations)
+
+## 1. Build the decoder
+
+```bash
+# Download the PyTorch model
+$ huggingface-cli download LiquidAI/LFM2.5-Audio-1.5B --local-dir ./lfm2.5-audio/pytorch
+
+# Build the decoder as INT4 with FP32 inputs/outputs for CPU
+$ python3 -m onnxruntime_genai.models.builder \
+    -i ./lfm2.5-audio/pytorch \
+    -o ./lfm2.5-audio/cpu \
+    -p int4 \
+    -e cpu \
+    --extra_options exclude_embeds=true
+```
+
+The checkpoint has no `model_type` and no transformers model class: its `config.json` nests the LFM2
+decoder config under `"lfm"`, next to the speech encoder, depthformer and mel front-end settings, and
+the checkpoint stores the decoder under the `lfm.` prefix. The builder recognizes
+`Lfm2AudioForConditionalGeneration`, reads that nested config and loads only the decoder, whose
+logits are tied to the token embeddings.
+
+`exclude_embeds=true` is what makes this a speech pipeline stage: the decoder then takes
+`inputs_embeds` instead of `input_ids`, so the embedding model can splice the encoder output into the
+token embeddings before the decoder runs. The builder writes `"type": "lfm2_audio"` into
+`genai_config.json` for this case, and `"type": "lfm2_audio_text"` when the flag is omitted — that
+second form is a plain text-only LFM2 model that happens to come from an audio checkpoint, and it
+runs through the normal `AppendTokens` path with no speech or embedding model.
+
+## 2. Get the speech encoder and build the embedding model
+
+The speech encoder is `onnx/audio_encoder.onnx` in
+[LiquidAI/LFM2.5-Audio-1.5B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-ONNX)
+(`audio_encoder_fp16.onnx` and `audio_encoder_q4.onnx` are there too). Download the graph together
+with every `*.onnx_data*` file next to it. It has the signature ONNX Runtime GenAI expects:
+
+| Name | Shape | Type |
+| --- | --- | --- |
+| `mel_spectrogram` (input) | `[num_clips, num_frames, 128]` | float |
+| `mel_lengths` (input) | `[num_clips]` | int64 |
+| `audio_embeddings` (output) | `[num_clips, ceil(num_frames / 8), hidden_size]` | float |
+| `audio_lengths` (output) | `[num_clips]` | int64 |
+
+The encoder subsamples the mel frames by 8 (three stride-2 convolutions) and its adapter projects the
+result to the decoder's hidden size, so one output frame covers 80 ms of audio. The `audio_lengths`
+output is not read by the runtime — the runtime derives the same counts itself so it can size the
+prompt before the encoder runs — but the graph produces it and the input it shares with
+`mel_lengths` matters: the encoder masks the padding of a batched run with it.
+
+The embedding model looks up `input_ids` in the decoder's embedding table and scatters
+`audio_features` into the positions holding the audio placeholder token. Build it from
+`lfm.embed_tokens.weight` in the checkpoint:
+
+```python
+import json
+from pathlib import Path
+
+import numpy as np
+import onnx
+from onnx import TensorProto, helper, numpy_helper
+from safetensors.torch import load_file
+
+pytorch_dir = Path("./lfm2.5-audio/pytorch")
+output_dir = Path("./lfm2.5-audio/cpu")
+
+config = json.loads((pytorch_dir / "config.json").read_text())["lfm"]
+hidden_size, vocab_size = config["hidden_size"], config["vocab_size"]
+# Pick any id the text never uses; it must match model.audio_token_id in genai_config.json.
+audio_token_id = 133  # <|reserved_123|>
+
+embed_weight = load_file(pytorch_dir / "model.safetensors")["lfm.embed_tokens.weight"]
+table = numpy_helper.from_array(embed_weight.float().numpy(), name="embed_tokens.weight")
+
+graph = helper.make_graph(
+    [
+        helper.make_node("Gather", ["embed_tokens.weight", "input_ids"], ["text_embeds"], axis=0),
+        helper.make_node("Shape", ["text_embeds"], ["embeds_shape"]),
+        helper.make_node("Reshape", ["text_embeds", "flat_rows"], ["flat_embeds"]),
+        helper.make_node("Reshape", ["input_ids", "flat"], ["flat_ids"]),
+        helper.make_node("Equal", ["flat_ids", "audio_token_id"], ["is_audio"]),
+        helper.make_node("NonZero", ["is_audio"], ["audio_positions_t"]),
+        helper.make_node("Transpose", ["audio_positions_t"], ["audio_positions"], perm=[1, 0]),
+        helper.make_node("ScatterND", ["flat_embeds", "audio_positions", "audio_features"], ["merged"]),
+        helper.make_node("Reshape", ["merged", "embeds_shape"], ["inputs_embeds"]),
+    ],
+    "embedding",
+    [
+        helper.make_tensor_value_info("input_ids", TensorProto.INT64, ["batch_size", "sequence_length"]),
+        helper.make_tensor_value_info("audio_features", TensorProto.FLOAT, ["num_audio_tokens", hidden_size]),
+    ],
+    [helper.make_tensor_value_info("inputs_embeds", TensorProto.FLOAT, ["batch_size", "sequence_length", hidden_size])],
+    initializer=[
+        table,
+        numpy_helper.from_array(np.array([-1, hidden_size], np.int64), name="flat_rows"),
+        numpy_helper.from_array(np.array([-1], np.int64), name="flat"),
+        numpy_helper.from_array(np.array(audio_token_id, np.int64), name="audio_token_id"),
+    ],
+)
+model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 17)], ir_version=8)
+onnx.checker.check_model(model)
+onnx.save(model, output_dir / "embeddings.onnx", save_as_external_data=True, location="embeddings.onnx.data")
+```
+
+## 3. Write `genai_config.json`
+
+Add `embedding` and `speech` sections and an `audio_token_id` to the `genai_config.json` the builder
+produced. Leave the `decoder` and `search` sections it wrote alone:
+
+```json
+{
+    "model": {
+        "audio_token_id": 133,
+        "bos_token_id": 1,
+        "context_length": 32768,
+        "decoder": { "...": "written by the model builder" },
+        "embedding": {
+            "filename": "embeddings.onnx",
+            "inputs": {
+                "input_ids": "input_ids",
+                "audio_features": "audio_features"
+            },
+            "outputs": {
+                "inputs_embeds": "inputs_embeds"
+            },
+            "session_options": {
+                "log_id": "onnxruntime-genai",
+                "provider_options": []
+            }
+        },
+        "eos_token_id": 7,
+        "pad_token_id": 0,
+        "speech": {
+            "filename": "speech.onnx",
+            "inputs": {
+                "audio_embeds": "mel_spectrogram",
+                "audio_lengths": "mel_lengths",
+                "audio_sizes": "audio_sizes"
+            },
+            "outputs": {
+                "audio_features": "audio_embeddings"
+            },
+            "session_options": {
+                "log_id": "onnxruntime-genai",
+                "provider_options": []
+            }
+        },
+        "type": "lfm2_audio",
+        "vocab_size": 65536
+    },
+    "search": { "...": "written by the model builder" }
+}
+```
+
+`audio_sizes` is not an encoder input; the name only tells the runtime what to call the per-clip
+token counts it computes, so leave it as is unless it collides with a real input of your graph.
+`audio_token_id` must be the id the embedding model scatters over, and must not be an id the
+tokenizer can emit for ordinary text — the reserved range of the LFM2 tokenizer is the natural home
+for it.
+
+The audio front end needs no configuration file: it defaults to the settings every published
+LFM2-Audio checkpoint shares (`"preprocessor"` in `config.json`), which are NeMo's
+`AudioToMelSpectrogramPreprocessor`:
+
+| Setting | Value | `genai_config.json` override |
+| --- | --- | --- |
+| Sample rate | 16000 | `model.sample_rate` |
+| Mel bins | 128 | `model.num_mels` |
+| FFT size | 512 | `model.fft_size` |
+| Window | 400 samples (25 ms), symmetric Hann | `model.win_length` |
+| Hop | 160 samples (10 ms) | `model.hop_length` |
+| Pre-emphasis | 0.97 | `model.preemph` |
+| Log guard | 2⁻²⁴ | `model.log_eps` |
+| Normalization epsilon | 1e-5 | `model.norm_eps` |
+| Encoder subsampling | 8 | `model.subsampling_factor` |
+
+Set the overrides only for a fine-tune that changed them; the defaults already match all the
+published models.
+
+## 4. Run the model
+
+[`model-mm.py`](model-mm.py) drives any multi-modal model in this repository:
+
+```bash
+$ python3 model-mm.py -m ./lfm2.5-audio/cpu -e cpu --audio_paths ./question.wav
+```
+
+The prompt must hold exactly one `<|audio|>` marker per clip, in clip order; the processor rejects
+any other count rather than guessing, and it accepts one prompt at a time (a single-entry list is
+fine, batching is not). The C++ audio processor replaces each marker with one `audio_token_id` per
+encoder frame, so the number of placeholders always matches the number of features the encoder
+produced. Text on either side of a marker is tokenized on its own, which is what
+`liquid_audio.ChatState` does.
+
+LFM2-Audio has no audio token of its own and no chat-template entry for audio: `<|audio|>` is this
+runtime's marker, the same role `<image>` plays for LFM2-VL. The system prompt selects the task, as
+in the reference implementation:
+
+| Task | System prompt |
+| --- | --- |
+| Transcription | `Perform ASR.` |
+| Spoken question, text answer | `Respond with interleaved text and audio.` (the text half of the answer) |
+
+```
+<|startoftext|><|im_start|>system
+Perform ASR.<|im_end|>
+<|im_start|>user
+<|audio|><|im_end|>
+<|im_start|>assistant
+```
+
+## 5. Known limitations
+
+**Audio output is not supported.** Interleaved and TTS generation need the depthformer, which
+predicts 8 codebook entries per 80 ms audio frame in an inner autoregressive loop, plus the audio
+detokenizer and an inverse STFT to turn those codes into a waveform. ONNX Runtime GenAI's generation
+loop samples one token stream, so none of that has a home here yet. The model still answers in text
+when asked to; a prompt that asks it to speak makes it emit `<|audio_start|>` and then codes the
+runtime has nothing to do with. LiquidAI ships `vocoder_depthformer.onnx` and
+`audio_detokenizer.onnx` in the ONNX repository, and their
+[onnx-export](https://github.com/Liquid4All/onnx-export) repository drives them from Python.
+
+**One prompt at a time.** Several clips in one prompt work and share a single encoder run, but
+batched prompts are refused.
+
+**Audio is not chunked.** The whole clip goes through the encoder in one run, and one hour of audio
+is 450k mel frames, so memory grows with the clip length. The reference implementation has the same
+shape; for long-form transcription, split the audio yourself.
+
+**The mel front end differs from the reference by about 1e-4.** It computes the same NeMo pipeline in
+float32 where the reference uses float64 intermediates in places, and the FFT is a different
+implementation. The difference is far below the audio's own quantization noise.
+
+**Dither is off, as in the reference's eval mode.** NeMo adds 1e-5 of white noise to the samples
+during training only; the runtime never does.
diff --git a/src/config.cpp b/src/config.cpp
index dbf91c4420..8f2ea33af7 100644
--- a/src/config.cpp
+++ b/src/config.cpp
@@ -1473,6 +1473,8 @@ struct SpeechInputs_Element : JSON::Element {
       v_.attention_mask = JSON::Get<std::string_view>(value);
     } else if (name == "audio_sizes") {
       v_.audio_sizes = JSON::Get<std::string_view>(value);
+    } else if (name == "audio_lengths") {
+      v_.audio_lengths = JSON::Get<std::string_view>(value);
     } else if (name == "audio_projection_mode") {
       v_.audio_projection_mode = JSON::Get<std::string_view>(value);
     } else {
diff --git a/src/config.h b/src/config.h
index 6c5a53ae1a..21ef67fb66 100644
--- a/src/config.h
+++ b/src/config.h
@@ -48,6 +48,7 @@ struct Config {
     // Speech encoder names
     static constexpr std::string_view AudioAttentionMaskName = "audio_attention_mask";
     static constexpr std::string_view AudioSizesName = "audio_sizes";
+    static constexpr std::string_view AudioLengthsName = "audio_lengths";
     static constexpr std::string_view AudioProjectionModeName = "audio_projection_mode";
     static constexpr std::string_view AudioFeaturesName = "audio_features";
     static constexpr std::string_view NumAudioTokens = "num_audio_tokens";
@@ -317,6 +318,7 @@ struct Config {
         std::string audio_embeds{Defaults::AudioEmbedsName};
         std::string attention_mask{Defaults::AudioAttentionMaskName};
         std::string audio_sizes{Defaults::AudioSizesName};
+        std::string audio_lengths{Defaults::AudioLengthsName};  // per-clip valid frame count of audio_embeds (LFM2-Audio)
         std::string audio_projection_mode{Defaults::AudioProjectionModeName};
       } inputs;
 
diff --git a/src/generator/generators.cpp b/src/generator/generators.cpp
index 3aa35ec4af..26411799d1 100644
--- a/src/generator/generators.cpp
+++ b/src/generator/generators.cpp
@@ -964,7 +964,7 @@ SpeculativeStats Generator::GetSpeculativeStats() const {
 void Generator::RewindToLength(size_t new_length) {
   const auto& model_type = model_->config_->model.type;
   if (model_type == "whisper" || model_type == "phi3v" || model_type == "decoder-pipeline" ||
-      ModelType::IsLFM2(model_type) || model_type == "lfm2_vl")
+      ModelType::IsLFM2(model_type) || model_type == "lfm2_vl" || model_type == "lfm2_audio")
     throw std::runtime_error("RewindTo is currently not supported for " + model_type + ".");
   const size_t current_length = search_->GetSequenceLength();
   if (new_length > current_length)
diff --git a/src/models/model.cpp b/src/models/model.cpp
index 3c5ce16614..e11e28ccae 100644
--- a/src/models/model.cpp
+++ b/src/models/model.cpp
@@ -946,6 +946,8 @@ std::shared_ptr<Model> CreateModel(OrtEnv& ort_env, std::unique_ptr<Config> conf
     return std::make_shared<NemotronSpeechModel>(std::move(config), ort_env);
   if (ModelType::IsTDT(config->model.type))
     return std::make_shared<ParakeetTdtModel>(std::move(config), ort_env);
+  if (config->model.type == "lfm2_audio")
+    return std::make_shared<MultiModalLanguageModel>(std::move(config), ort_env, /*vision=*/false, /*speech=*/true);
   if (ModelType::IsALM(config->model.type))
     return std::make_shared<WhisperModel>(std::move(config), ort_env);
   if (ModelType::IsVLM(config->model.type))
diff --git a/src/models/model_type.h b/src/models/model_type.h
index 90fe98f7a9..ab489a8c43 100644
--- a/src/models/model_type.h
+++ b/src/models/model_type.h
@@ -17,7 +17,7 @@ namespace Generators {
 struct ModelType {
   inline static bool IsLLM(const std::string& model_type) {
     // Large-language model (LLM)
-    static constexpr std::array<std::string_view, 33> LLM = {"chatglm", "decoder", "ernie4_5", "gemma", "gemma2", "gemma3_text", "gemma3_vl_text", "gemma4_text", "gpt2", "gptoss", "granite", "granitemoehybrid", "hunyuandensev1", "internlm2", "lfm2", "lfm2_moe", "lfm2_vl_text", "llama", "mistral", "mistral3_text", "nemotron", "olmo", "phi", "phimoe", "phi3", "phi3small", "qwen2", "qwen2_5_vl_text", "qwen3", "qwen3_vl_text", "qwen3_5_moe_text", "qwen3_5_text", "smollm3"};
+    static constexpr std::array<std::string_view, 34> LLM = {"chatglm", "decoder", "ernie4_5", "gemma", "gemma2", "gemma3_text", "gemma3_vl_text", "gemma4_text", "gpt2", "gptoss", "granite", "granitemoehybrid", "hunyuandensev1", "internlm2", "lfm2", "lfm2_audio_text", "lfm2_moe", "lfm2_vl_text", "llama", "mistral", "mistral3_text", "nemotron", "olmo", "phi", "phimoe", "phi3", "phi3small", "qwen2", "qwen2_5_vl_text", "qwen3", "qwen3_vl_text", "qwen3_5_moe_text", "qwen3_5_text", "smollm3"};
     return std::find(LLM.begin(), LLM.end(), model_type) != LLM.end();
   }
 
@@ -28,8 +28,9 @@ struct ModelType {
   }
 
   inline static bool IsALM(const std::string& model_type) {
-    // Audio-language model (ALM)
-    static constexpr std::array<std::string_view, 1> ALM = {"whisper"};
+    // Audio-language model (ALM): audio in, text out. "lfm2_audio" runs the multi-modal pipeline
+    // (speech encoder -> embedding -> LFM2 decoder), "whisper" its own encoder-decoder model.
+    static constexpr std::array<std::string_view, 2> ALM = {"lfm2_audio", "whisper"};
     return std::find(ALM.begin(), ALM.end(), model_type) != ALM.end();
   }
 
@@ -76,9 +77,10 @@ struct ModelType {
   inline static bool IsLFM2(const std::string& model_type) {
     // Liquid Foundation Model 2: hybrid attention/conv architecture with conv state cache.
     // The MoE variant (LFM2-8B-A1B, LFM2.5-8B-A1B, LFM2-24B-A2B) has the same runtime shape, and
-    // "lfm2_vl_text" is an LFM2-VL checkpoint exported as a plain text decoder; both have the same
-    // conv/attention layer mix and need the same conv state cache.
-    return model_type == "lfm2" || model_type == "lfm2_moe" || model_type == "lfm2_vl_text";
+    // "lfm2_vl_text" / "lfm2_audio_text" are LFM2-VL / LFM2-Audio checkpoints exported as plain text
+    // decoders; all have the same conv/attention layer mix and need the same conv state cache.
+    return model_type == "lfm2" || model_type == "lfm2_moe" || model_type == "lfm2_vl_text" ||
+           model_type == "lfm2_audio_text";
   }
 };
 
diff --git a/src/models/multi_modal.cpp b/src/models/multi_modal.cpp
index 911f284b6f..0fcb6abeed 100644
--- a/src/models/multi_modal.cpp
+++ b/src/models/multi_modal.cpp
@@ -603,6 +603,145 @@ DeviceSpan<float> SpeechState::Run(int current_length, DeviceSpan<int32_t>& next
   return {};
 }
 
+void Lfm2AudioSpeechState::SetExtraInputs(const std::vector<ExtraInput>& extra_inputs, const int64_t num_audio_tokens) {
+  SpeechState::SetExtraInputs(extra_inputs, num_audio_tokens);
+
+  // audio_sizes holds the decoder tokens each clip contributes; its sum is num_audio_tokens.
+  tokens_per_clip_.clear();
+  for (const auto& input : extra_inputs) {
+    if (input.name == model_.config_->model.speech.inputs.audio_sizes) {
+      const auto info = input.tensor->ort_tensor_->GetTensorTypeAndShapeInfo();
+      const int64_t* sizes = input.tensor->ort_tensor_->GetTensorData<int64_t>();
+      tokens_per_clip_.assign(sizes, sizes + info->GetElementCount());
+      break;
+    }
+  }
+}
+
+DeviceSpan<float> Lfm2AudioSpeechState::Run(int current_length, DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices) {
+  if (model_.config_->model.speech.run_options.has_value()) {
+    State::SetRunOptions(model_.config_->model.speech.run_options.value());
+  }
+  // A single clip fills the whole mel tensor and the whole feature buffer; run it as it stands.
+  if (tokens_per_clip_.size() <= 1) {
+    State::Run(*model_.speech_session_);
+    return {};
+  }
+
+  const auto& mel_name = model_.config_->model.speech.inputs.audio_embeds;
+  const auto& lengths_name = model_.config_->model.speech.inputs.audio_lengths;
+  const size_t mel_index = FindInput(mel_name);
+  const size_t lengths_index = FindInput(lengths_name);
+  const size_t features_index = FindOutput(model_.config_->model.speech.outputs.audio_features);
+
+  OrtValue* mel_batch = inputs_[mel_index];
+  OrtValue* lengths = inputs_[lengths_index];
+  OrtValue* features = outputs_[features_index];
+
+  const auto mel_info = mel_batch->GetTensorTypeAndShapeInfo();
+  const auto mel_shape = mel_info->GetShape();  // [num_clips, longest_clip, num_mels]
+  if (mel_shape.size() != 3) {
+    throw std::runtime_error("Lfm2AudioSpeechState: expected a 3D [num_clips, num_frames, num_mels] mel tensor, got rank " +
+                             std::to_string(mel_shape.size()) + ".");
+  }
+  const int64_t num_clips = mel_shape[0];
+  const int64_t longest_clip = mel_shape[1];
+  const int64_t num_mels = mel_shape[2];
+  if (num_clips != static_cast<int64_t>(tokens_per_clip_.size())) {
+    throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(num_clips) +
+                             " clips but audio_sizes has " + std::to_string(tokens_per_clip_.size()) + " entries.");
+  }
+  const int64_t total = std::accumulate(tokens_per_clip_.begin(), tokens_per_clip_.end(), int64_t{0});
+  if (total != num_audio_tokens_) {
+    throw std::runtime_error("Lfm2AudioSpeechState: audio_sizes sums to " + std::to_string(total) + " tokens but " +
+                             std::to_string(num_audio_tokens_) + " were expected.");
+  }
+
+  const auto mel_type = mel_info->GetElementType();
+  const size_t mel_element_size = Ort::SizeOf(mel_type);
+  const auto lengths_info = lengths->GetTensorTypeAndShapeInfo();
+  if (static_cast<int64_t>(lengths_info->GetElementCount()) != num_clips) {
+    throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(num_clips) + " clips but " +
+                             lengths_name + " has " + std::to_string(lengths_info->GetElementCount()) + " entries.");
+  }
+  const int64_t* frames_per_clip = lengths->GetTensorData<int64_t>();
+
+  const auto features_info = features->GetTensorTypeAndShapeInfo();
+  const auto features_type = features_info->GetElementType();
+  const auto features_shape = features_info->GetShape();  // [batch, num_audio_tokens, hidden_size]
+  // Several clips are concatenated into one prompt, which the pipeline only ever builds for a single
+  // sequence; a wider feature buffer would need the whole run repeated per beam.
+  if (features_shape.size() == 3 && features_shape[0] != 1) {
+    throw std::runtime_error("Lfm2AudioSpeechState: several audio clips need a batch size of 1, got " +
+                             std::to_string(features_shape[0]) + ".");
+  }
+  const int64_t hidden_size = features_shape.back();
+  const size_t feature_row_bytes = static_cast<size_t>(hidden_size) * Ort::SizeOf(features_type);
+
+  auto features_bytes = ByteWrapTensor(*model_.p_device_, *features);
+  const auto* mel_data = static_cast<const uint8_t*>(mel_batch->GetTensorRawData());
+  const size_t clip_stride = static_cast<size_t>(longest_clip * num_mels) * mel_element_size;
+  size_t destination = 0;
+
+  // The published encoder export is traced for one clip (its subsampling mask cannot broadcast over
+  // a batch), so run it once per clip on that clip's own frames — which also keeps the padding out
+  // of the encoder entirely — and concatenate the results in clip order.
+  for (int64_t clip = 0; clip < num_clips; ++clip) {
+    const int64_t num_frames = frames_per_clip[clip];
+    if (num_frames <= 0 || num_frames > longest_clip) {
+      throw std::runtime_error("Lfm2AudioSpeechState: clip " + std::to_string(clip) + " reports " +
+                               std::to_string(num_frames) + " mel frames, outside the 1.." +
+                               std::to_string(longest_clip) + " the mel tensor holds.");
+    }
+    const std::vector<int64_t> clip_mel_shape{1, num_frames, num_mels};
+    auto clip_mel = OrtValue::CreateTensor(Ort::Allocator::GetWithDefaultOptions(), clip_mel_shape, mel_type);
+    std::memcpy(clip_mel->GetTensorMutableRawData(), mel_data + static_cast<size_t>(clip) * clip_stride,
+                static_cast<size_t>(num_frames * num_mels) * mel_element_size);
+
+    auto clip_length = OrtValue::CreateTensor<int64_t>(Ort::Allocator::GetWithDefaultOptions(), std::vector<int64_t>{1});
+    clip_length->GetTensorMutableData<int64_t>()[0] = num_frames;
+
+    const int64_t clip_tokens = tokens_per_clip_[static_cast<size_t>(clip)];
+    auto clip_features = OrtValue::CreateTensor(model_.p_device_->GetAllocator(),
+                                                std::vector<int64_t>{1, clip_tokens, hidden_size}, features_type);
+
+    inputs_[mel_index] = clip_mel.get();
+    inputs_[lengths_index] = clip_length.get();
+    outputs_[features_index] = clip_features.get();
+    State::Run(*model_.speech_session_);
+
+    const size_t clip_bytes = static_cast<size_t>(clip_tokens) * feature_row_bytes;
+    features_bytes.subspan(destination, clip_bytes).CopyFrom(ByteWrapTensor(*model_.p_device_, *clip_features));
+    destination += clip_bytes;
+  }
+
+  inputs_[mel_index] = mel_batch;
+  inputs_[lengths_index] = lengths;
+  outputs_[features_index] = features;
+  return {};
+}
+
+size_t Lfm2AudioSpeechState::FindInput(const std::string& name) const {
+  for (size_t i = 0; i < input_names_.size(); ++i) {
+    if (name == input_names_[i]) return i;
+  }
+  throw std::runtime_error("Lfm2AudioSpeechState: speech input \"" + name + "\" is not bound.");
+}
+
+size_t Lfm2AudioSpeechState::FindOutput(const std::string& name) const {
+  for (size_t i = 0; i < output_names_.size(); ++i) {
+    if (name == output_names_[i]) return i;
+  }
+  throw std::runtime_error("Lfm2AudioSpeechState: speech output \"" + name + "\" is not bound.");
+}
+
+std::unique_ptr<SpeechState> CreateSpeechState(const MultiModalLanguageModel& model, const GeneratorParams& params) {
+  if (model.config_->model.type == "lfm2_audio") {
+    return std::make_unique<Lfm2AudioSpeechState>(model, params);
+  }
+  return std::make_unique<SpeechState>(model, params);
+}
+
 EmbeddingState::EmbeddingState(const MultiModalLanguageModel& model, const GeneratorParams& params)
     : State{params, model},
       model_{model} {
@@ -818,7 +957,7 @@ MultiModalPipelineState::MultiModalPipelineState(const MultiModalLanguageModel&
     vision_state_ = CreateVisionState(model_, params);
   }
   if (model_.speech_session_) {
-    speech_state_ = std::make_unique<SpeechState>(model_, params);
+    speech_state_ = CreateSpeechState(model_, params);
   }
   embedding_state_ = std::make_unique<EmbeddingState>(model, params);
   decoder_state_ = std::make_unique<DecoderState>(model_, sequence_lengths, params);
diff --git a/src/models/multi_modal.h b/src/models/multi_modal.h
index 7726f292b8..5c50dfcb4e 100644
--- a/src/models/multi_modal.h
+++ b/src/models/multi_modal.h
@@ -133,10 +133,10 @@ struct SpeechState : State {
   SpeechState(const SpeechState&) = delete;
   SpeechState& operator=(const SpeechState&) = delete;
 
-  void SetExtraInputs(const std::vector<ExtraInput>& extra_inputs, const int64_t num_audio_tokens);
+  virtual void SetExtraInputs(const std::vector<ExtraInput>& extra_inputs, const int64_t num_audio_tokens);
   DeviceSpan<float> Run(int current_length, DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices = {}) override;
 
- private:
+ protected:
   friend struct MultiModalPipelineState;
 
   const MultiModalLanguageModel& model_;
@@ -145,6 +145,30 @@ struct SpeechState : State {
   std::unique_ptr<MultiModalFeatures> audio_features_;
 };
 
+// Lfm2AudioSpeechState: per-clip encoder loop for LFM2-Audio.
+//
+// The processor stacks the clips of one prompt into a zero-padded [N, T_max, num_mels] mel tensor
+// with their real frame counts alongside. The published encoder export is traced for a single clip
+// (its subsampling mask cannot broadcast over a batch), so with several clips this subclass slices
+// each clip's own frames out of that tensor, runs the encoder on [1, T_i, num_mels], and writes the
+// results one after another into the contiguous [1, total_tokens, hidden] feature buffer the
+// embedding model expects, in clip order.
+struct Lfm2AudioSpeechState : SpeechState {
+  using SpeechState::SpeechState;  // inherit constructor
+
+  void SetExtraInputs(const std::vector<ExtraInput>& extra_inputs, const int64_t num_audio_tokens) override;
+  DeviceSpan<float> Run(int current_length, DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices = {}) override;
+
+ private:
+  size_t FindInput(const std::string& name) const;
+  size_t FindOutput(const std::string& name) const;
+
+  std::vector<int64_t> tokens_per_clip_;
+};
+
+// Factory: pick the right SpeechState subclass based on model type.
+std::unique_ptr<SpeechState> CreateSpeechState(const MultiModalLanguageModel& model, const GeneratorParams& params);
+
 struct EmbeddingState : State {
   EmbeddingState(const MultiModalLanguageModel& model, const GeneratorParams& params);
   EmbeddingState(const EmbeddingState&) = delete;
diff --git a/src/models/preprocessing/lfm2_audio_processor.cpp b/src/models/preprocessing/lfm2_audio_processor.cpp
new file mode 100644
index 0000000000..c1f5fa9439
--- /dev/null
+++ b/src/models/preprocessing/lfm2_audio_processor.cpp
@@ -0,0 +1,320 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "generator/generators.h"
+#include "models/model.h"
+#include "models/preprocessing/genai_tokenizer.h"
+#include "models/preprocessing/lfm2_audio_processor.h"
+#include "nemo_mel_spectrogram.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstring>
+
+namespace Generators {
+
+namespace {
+
+// The list overload of MultiModalProcessor::Process fills payload.prompts and leaves payload.prompt
+// empty. Batching is not supported, so a single-entry list is the prompt and anything longer is an error
+// rather than silently dropped text.
+std::string ResolvePrompt(const Payload& payload) {
+  if (payload.prompts.empty()) {
+    return payload.prompt;
+  }
+  if (payload.prompts.size() != 1) {
+    throw std::runtime_error("Lfm2AudioProcessor: batched prompts are not supported; got " +
+                             std::to_string(payload.prompts.size()) + " prompts. Pass a single prompt string.");
+  }
+  return payload.prompts[0] ? std::string(payload.prompts[0]) : std::string{};
+}
+
+// The encoder graph's input names come from genai_config.json; a wrong name would otherwise be
+// dropped silently by ExtraInputs and only surface as ORT's "Missing Input" at run time.
+ONNXTensorElementDataType SpeechInputType(const SessionInfo& session_info, const std::string& name,
+                                          const char* config_field) {
+  if (!session_info.HasInput(name)) {
+    throw std::runtime_error("Lfm2AudioProcessor: the speech model has no input named \"" + name +
+                             "\". Point model.speech.inputs." + config_field +
+                             " in genai_config.json at the name the speech model uses.");
+  }
+  return session_info.GetInputDataType(name);
+}
+
+std::unique_ptr<OrtValue> MakeInt64Tensor(const std::vector<int64_t>& values, std::vector<int64_t> shape,
+                                          Ort::Allocator& allocator) {
+  auto tensor = OrtValue::CreateTensor<int64_t>(allocator, shape);
+  std::copy(values.begin(), values.end(), tensor->GetTensorMutableData<int64_t>());
+  return tensor;
+}
+
+std::unique_ptr<OrtValue> MakeInputIds(const std::vector<int32_t>& input_ids, Ort::Allocator& allocator) {
+  auto tensor = OrtValue::CreateTensor<int32_t>(allocator, std::vector<int64_t>{1, static_cast<int64_t>(input_ids.size())});
+  std::copy(input_ids.begin(), input_ids.end(), tensor->GetTensorMutableData<int32_t>());
+  return tensor;
+}
+
+// torch.hann_window(win_length, periodic=False), the window NeMo's FilterbankFeatures registers.
+std::vector<float> SymmetricHannWindow(int win_length) {
+  constexpr double kPi = 3.14159265358979323846;
+  std::vector<float> window(static_cast<size_t>(win_length));
+  for (int n = 0; n < win_length; ++n) {
+    window[static_cast<size_t>(n)] = static_cast<float>(0.5 * (1.0 - std::cos(2.0 * kPi * n / (win_length - 1))));
+  }
+  return window;
+}
+
+}  // namespace
+
+int64_t Lfm2AudioNumMelFrames(int64_t num_samples, const Lfm2AudioMelConfig& config) {
+  // torch.stft(center=True): fft_size / 2 zeros on both sides, one frame per hop_length.
+  const int64_t padded = num_samples + 2 * (config.fft_size / 2);
+  if (padded < config.fft_size) {
+    return 0;
+  }
+  return (padded - config.fft_size) / config.hop_length + 1;
+}
+
+int64_t Lfm2AudioNumValidMelFrames(int64_t num_samples, const Lfm2AudioMelConfig& config) {
+  // FilterbankFeatures.get_seq_len: (num_samples + 2 * (fft_size // 2) - fft_size) // hop_length.
+  const int64_t padded = num_samples + 2 * (config.fft_size / 2);
+  if (padded < config.fft_size) {
+    return 0;
+  }
+  return (padded - config.fft_size) / config.hop_length;
+}
+
+int64_t Lfm2AudioNumTokens(int64_t num_mel_frames, int64_t subsampling_factor) {
+  if (subsampling_factor <= 0) {
+    throw std::runtime_error("Lfm2AudioProcessor: subsampling_factor must be positive.");
+  }
+  return (num_mel_frames + subsampling_factor - 1) / subsampling_factor;
+}
+
+std::vector<float> ComputeLfm2AudioMel(const float* pcm, int64_t num_samples, const Lfm2AudioMelConfig& config,
+                                       int64_t& num_frames) {
+  if (config.num_mels <= 0 || config.fft_size <= 0 || config.hop_length <= 0 || config.win_length < 2 ||
+      config.win_length > config.fft_size) {
+    throw std::runtime_error("Lfm2AudioProcessor: invalid mel configuration (num_mels " + std::to_string(config.num_mels) +
+                             ", fft_size " + std::to_string(config.fft_size) + ", win_length " +
+                             std::to_string(config.win_length) + ", hop_length " + std::to_string(config.hop_length) + ").");
+  }
+
+  num_frames = Lfm2AudioNumMelFrames(num_samples, config);
+  const int64_t valid_frames = Lfm2AudioNumValidMelFrames(num_samples, config);
+  // The per-feature normalization divides by valid_frames - 1; NeMo raises on a single frame and
+  // produces NaNs on none, so refuse both up front with the clip length that would be needed.
+  if (valid_frames < 2) {
+    throw std::runtime_error("Lfm2AudioProcessor: audio clip is too short (" + std::to_string(num_samples) +
+                             " samples at " + std::to_string(config.sample_rate) + " Hz); at least " +
+                             std::to_string(2 * config.hop_length) + " samples are needed.");
+  }
+
+  const auto mel_filters = nemo_mel::CreateMelFilterbank(config.num_mels, config.fft_size, config.sample_rate);
+  const auto window = SymmetricHannWindow(config.win_length);
+
+  std::vector<float> preemphasized(static_cast<size_t>(num_samples));
+  nemo_mel::ApplyPreemphasis(pcm, static_cast<size_t>(num_samples), config.preemph, 0.0f, preemphasized.data());
+
+  // Constant (zero) padding on both sides: NeMo's stft uses pad_mode="constant".
+  const int64_t pad = config.fft_size / 2;
+  std::vector<float> padded(static_cast<size_t>(pad + num_samples + pad), 0.0f);
+  std::copy(preemphasized.begin(), preemphasized.end(), padded.begin() + pad);
+
+  // The win_length window sits centered inside the fft_size frame; the power spectrum does not
+  // depend on where the windowed samples land within the FFT buffer.
+  const int64_t win_offset = (config.fft_size - config.win_length) / 2;
+  const int num_bins = config.fft_size / 2 + 1;
+  const auto num_mels = static_cast<size_t>(config.num_mels);
+
+  std::vector<float> mel(static_cast<size_t>(num_frames) * num_mels);
+  std::vector<float> magnitudes;
+  for (int64_t t = 0; t < num_frames; ++t) {
+    const float* frame = padded.data() + t * config.hop_length + win_offset;
+    nemo_mel::ComputeSTFTFrame(frame, window.data(), config.win_length, config.fft_size, magnitudes);
+    float* row = mel.data() + static_cast<size_t>(t) * num_mels;
+    for (size_t m = 0; m < num_mels; ++m) {
+      float value = 0.0f;
+      for (int k = 0; k < num_bins; ++k) {
+        value += mel_filters[m][static_cast<size_t>(k)] * magnitudes[static_cast<size_t>(k)];
+      }
+      row[m] = std::log(value + config.log_eps);
+    }
+  }
+
+  // normalize_batch(normalize_type="per_feature"): mean and unbiased standard deviation over the
+  // valid frames only, then the frames past the valid length are set to pad_value (0).
+  for (size_t m = 0; m < num_mels; ++m) {
+    double sum = 0.0;
+    for (int64_t t = 0; t < valid_frames; ++t) {
+      sum += mel[static_cast<size_t>(t) * num_mels + m];
+    }
+    const float mean = static_cast<float>(sum / static_cast<double>(valid_frames));
+    double variance = 0.0;
+    for (int64_t t = 0; t < valid_frames; ++t) {
+      const double delta = mel[static_cast<size_t>(t) * num_mels + m] - mean;
+      variance += delta * delta;
+    }
+    const float std = static_cast<float>(std::sqrt(variance / static_cast<double>(valid_frames - 1))) + config.norm_eps;
+    for (int64_t t = 0; t < valid_frames; ++t) {
+      float& value = mel[static_cast<size_t>(t) * num_mels + m];
+      value = (value - mean) / std;
+    }
+  }
+  std::fill(mel.begin() + static_cast<size_t>(valid_frames) * num_mels, mel.end(), 0.0f);
+  return mel;
+}
+
+std::vector<std::string> SplitLfm2AudioPrompt(const std::string& prompt, size_t num_clips) {
+  const std::string marker{kLfm2AudioMarker};
+  std::vector<std::string> segments;
+  size_t position = 0;
+  for (size_t match = prompt.find(marker, position); match != std::string::npos; match = prompt.find(marker, position)) {
+    segments.push_back(prompt.substr(position, match - position));
+    position = match + marker.size();
+  }
+  segments.push_back(prompt.substr(position));
+
+  // Same rule as the LFM2-VL processor applies to <image>: one marker per clip, in clip order.
+  // Silently inventing or dropping placeholders would misalign the encoder features with the
+  // decoder positions.
+  const size_t num_markers = segments.size() - 1;
+  if (num_markers != num_clips) {
+    throw std::runtime_error("Lfm2AudioProcessor: the prompt contains " + std::to_string(num_markers) + " " + marker +
+                             " markers but " + std::to_string(num_clips) + " audio clips were provided. Put exactly one " +
+                             marker + " in the prompt per clip.");
+  }
+  return segments;
+}
+
+Lfm2AudioProcessor::Lfm2AudioProcessor(Config& config, const SessionInfo& session_info)
+    : mel_type_{SpeechInputType(session_info, config.model.speech.inputs.audio_embeds, "audio_embeds")},
+      subsampling_factor_{config.model.subsampling_factor > 0 ? config.model.subsampling_factor : 8},
+      audio_token_id_{config.model.audio_token_id} {
+  if (mel_type_ != ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT && mel_type_ != ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16) {
+    throw std::runtime_error("Lfm2AudioProcessor: speech input \"" + config.model.speech.inputs.audio_embeds +
+                             "\" must be float or float16, got " + TypeToString(mel_type_) + ".");
+  }
+  // The clip lengths are always emitted as int64, so fail at load rather than at the first clip if
+  // the graph was exported with another integer type.
+  const auto lengths_type = SpeechInputType(session_info, config.model.speech.inputs.audio_lengths, "audio_lengths");
+  if (lengths_type != ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64) {
+    throw std::runtime_error("Lfm2AudioProcessor: speech input \"" + config.model.speech.inputs.audio_lengths +
+                             "\" must be int64, got " + TypeToString(lengths_type) + ".");
+  }
+  if (audio_token_id_ <= 0) {
+    throw std::runtime_error(
+        "Lfm2AudioProcessor: model.audio_token_id must be set in genai_config.json to the id "
+        "the embedding model replaces with the audio features.");
+  }
+
+  // The mel front end defaults to the settings of the published checkpoints; a genai_config.json can
+  // override any of them with the same top-level fields the Parakeet and Nemotron models use.
+  const auto& m = config.model;
+  if (m.num_mels > 0) mel_config_.num_mels = m.num_mels;
+  if (m.fft_size > 0) mel_config_.fft_size = m.fft_size;
+  if (m.win_length > 0) mel_config_.win_length = m.win_length;
+  if (m.hop_length > 0) mel_config_.hop_length = m.hop_length;
+  if (m.sample_rate > 0) mel_config_.sample_rate = m.sample_rate;
+  if (m.preemph > 0.0f) mel_config_.preemph = m.preemph;
+  if (m.log_eps > 0.0f) mel_config_.log_eps = m.log_eps;
+  if (m.norm_eps > 0.0f) mel_config_.norm_eps = m.norm_eps;
+
+  config.AddMapping(std::string(Config::Defaults::InputIdsName), config.model.embedding.inputs.input_ids);
+  config.AddMapping(std::string(Config::Defaults::AudioEmbedsName), config.model.speech.inputs.audio_embeds);
+  config.AddMapping(std::string(Config::Defaults::AudioLengthsName), config.model.speech.inputs.audio_lengths);
+  config.AddMapping(std::string(Config::Defaults::AudioSizesName), config.model.speech.inputs.audio_sizes);
+}
+
+std::unique_ptr<NamedTensors> Lfm2AudioProcessor::Process(const Tokenizer& tokenizer, const Payload& payload) const {
+  const std::string prompt = ResolvePrompt(payload);
+  const Audios* audios = payload.audios;
+  const size_t num_clips = audios ? audios->num_audios_ : 0;
+  Ort::Allocator& allocator{Ort::Allocator::GetWithDefaultOptions()};
+  auto named_tensors = std::make_unique<NamedTensors>();
+
+  // With no clips the prompt must not ask for any; the split with zero clips enforces that.
+  const std::vector<std::string> segments = SplitLfm2AudioPrompt(prompt, num_clips);
+
+  // Mel spectrogram per clip, decoded to mono at the encoder's sample rate.
+  std::vector<std::vector<float>> mels;
+  std::vector<int64_t> mel_lengths;
+  std::vector<int64_t> tokens_per_clip;
+  int64_t longest = 0;
+  int64_t total_tokens = 0;
+  for (size_t i = 0; i < num_clips; ++i) {
+    ort_extensions::OrtxObjectPtr<OrtxTensorResult> decoded;
+    CheckResult(OrtxDecodeAudio(audios->audios_.get(), i, static_cast<int64_t>(mel_config_.sample_rate),
+                                /*stereo_to_mono=*/1, decoded.ToBeAssigned()));
+    ort_extensions::OrtxObjectPtr<OrtxTensor> pcm_tensor;
+    CheckResult(OrtxTensorResultGetAt(decoded.get(), 0, pcm_tensor.ToBeAssigned()));
+
+    const float* pcm{};
+    const int64_t* pcm_shape{};
+    size_t pcm_dims{};
+    CheckResult(OrtxGetTensorData(pcm_tensor.get(), reinterpret_cast<const void**>(&pcm), &pcm_shape, &pcm_dims));
+    int64_t num_samples = 0;
+    if (pcm_dims == 1) {
+      num_samples = pcm_shape[0];
+    } else if (pcm_dims == 2 && pcm_shape[0] == 1) {
+      num_samples = pcm_shape[1];
+    } else {
+      throw std::runtime_error("Lfm2AudioProcessor: expected mono PCM from the audio decoder for clip " +
+                               std::to_string(i) + ", got a rank " + std::to_string(pcm_dims) + " tensor.");
+    }
+
+    int64_t num_frames = 0;
+    mels.push_back(ComputeLfm2AudioMel(pcm, num_samples, mel_config_, num_frames));
+    mel_lengths.push_back(num_frames);
+    tokens_per_clip.push_back(Lfm2AudioNumTokens(num_frames, subsampling_factor_));
+    longest = std::max(longest, num_frames);
+    total_tokens += tokens_per_clip.back();
+  }
+
+  // Text segments are tokenized on their own, as the reference ChatState does, and each clip
+  // contributes one placeholder per encoder frame between them.
+  std::vector<int32_t> input_ids;
+  for (size_t i = 0; i < segments.size(); ++i) {
+    if (!segments[i].empty()) {
+      const std::vector<int32_t> ids = tokenizer.Encode(segments[i].c_str());
+      input_ids.insert(input_ids.end(), ids.begin(), ids.end());
+    }
+    if (i < tokens_per_clip.size()) {
+      input_ids.insert(input_ids.end(), static_cast<size_t>(tokens_per_clip[i]), audio_token_id_);
+    }
+  }
+  named_tensors->emplace(std::string(Config::Defaults::InputIdsName),
+                         std::make_shared<Tensor>(MakeInputIds(input_ids, allocator)));
+
+  if (num_clips == 0) {
+    // The pipeline reads audio_sizes to skip the speech run.
+    named_tensors->emplace(std::string(Config::Defaults::AudioSizesName),
+                           std::make_shared<Tensor>(MakeInt64Tensor({0}, {1}, allocator)));
+    return named_tensors;
+  }
+
+  // Clips share one encoder run as a zero-padded [num_clips, longest, num_mels] batch with their
+  // real lengths alongside, the same batching the reference applies before its encoder.
+  const int64_t num_mels = mel_config_.num_mels;
+  auto batch = OrtValue::CreateTensor<float>(allocator, std::vector<int64_t>{static_cast<int64_t>(num_clips), longest, num_mels});
+  float* batch_data = batch->GetTensorMutableData<float>();
+  std::fill_n(batch_data, static_cast<size_t>(num_clips) * static_cast<size_t>(longest * num_mels), 0.0f);
+  for (size_t i = 0; i < num_clips; ++i) {
+    std::copy(mels[i].begin(), mels[i].end(), batch_data + i * static_cast<size_t>(longest * num_mels));
+  }
+
+  if (mel_type_ == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
+    named_tensors->emplace(std::string(Config::Defaults::AudioEmbedsName), std::make_shared<Tensor>(std::move(batch)));
+  } else {
+    std::unique_ptr<OrtValue> converted;
+    Cast(*batch, converted, *GetDeviceInterface(DeviceType::CPU), mel_type_);
+    named_tensors->emplace(std::string(Config::Defaults::AudioEmbedsName), std::make_shared<Tensor>(std::move(converted)));
+  }
+  named_tensors->emplace(std::string(Config::Defaults::AudioLengthsName),
+                         std::make_shared<Tensor>(MakeInt64Tensor(mel_lengths, {static_cast<int64_t>(num_clips)}, allocator)));
+  named_tensors->emplace(std::string(Config::Defaults::AudioSizesName),
+                         std::make_shared<Tensor>(MakeInt64Tensor(tokens_per_clip, {static_cast<int64_t>(num_clips)}, allocator)));
+  return named_tensors;
+}
+
+}  // namespace Generators
diff --git a/src/models/preprocessing/lfm2_audio_processor.h b/src/models/preprocessing/lfm2_audio_processor.h
new file mode 100644
index 0000000000..dbcb051007
--- /dev/null
+++ b/src/models/preprocessing/lfm2_audio_processor.h
@@ -0,0 +1,66 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+#pragma once
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+#include "models/preprocessing/processor.h"
+
+namespace Generators {
+
+// Marker the prompt carries once per audio clip, in clip order. LFM2-Audio has no audio input token
+// of its own: the reference implementation (liquid-audio's ChatState) splices the encoder output
+// between the text tokens by position, so the marker is a convention of this runtime, and the
+// processor replaces it with model.audio_token_id repeated once per encoder frame.
+inline constexpr char kLfm2AudioMarker[] = "<|audio|>";
+
+// Log-mel front end of the FastConformer encoder, NeMo's AudioToMelSpectrogramPreprocessor with
+// the settings of the published checkpoints (config.json "preprocessor").
+struct Lfm2AudioMelConfig {
+  int num_mels{128};
+  int fft_size{512};
+  int win_length{400};  // window_size 0.025 s at 16 kHz
+  int hop_length{160};  // window_stride 0.01 s at 16 kHz
+  int sample_rate{16000};
+  float preemph{0.97f};
+  float log_eps{5.960464477539063e-08f};  // log_zero_guard_value 2^-24, added before the log
+  float norm_eps{1e-5f};                  // added to the per-feature standard deviation
+};
+
+// Frame accounting of the reference preprocessor. torch.stft with center=True yields one frame per
+// hop plus one, but NeMo's get_seq_len counts only num_samples / hop_length of them as valid: the
+// per-feature normalization uses the valid frames and the rest are zeroed, yet every frame is still
+// handed to the encoder and counted towards the decoder tokens.
+int64_t Lfm2AudioNumMelFrames(int64_t num_samples, const Lfm2AudioMelConfig& config);
+int64_t Lfm2AudioNumValidMelFrames(int64_t num_samples, const Lfm2AudioMelConfig& config);
+
+// Decoder tokens one clip contributes: the encoder subsamples the mel frames by subsampling_factor
+// with ceil rounding (three stride-2 convolutions with padding 1 for the shipped models).
+int64_t Lfm2AudioNumTokens(int64_t num_mel_frames, int64_t subsampling_factor);
+
+// Computes the normalized log-mel spectrogram of one mono clip, time-major [num_frames, num_mels],
+// the layout the encoder's mel_spectrogram input takes. Throws if the clip is too short to
+// normalize (fewer than two valid frames).
+std::vector<float> ComputeLfm2AudioMel(const float* pcm, int64_t num_samples, const Lfm2AudioMelConfig& config,
+                                       int64_t& num_frames);
+
+// Splits the prompt at each marker. Throws unless the prompt holds exactly one marker per clip, the
+// rule the vision processor applies to <image>, so the encoder features and the placeholders can
+// never be misaligned or silently dropped. Returns num_clips + 1 text segments.
+std::vector<std::string> SplitLfm2AudioPrompt(const std::string& prompt, size_t num_clips);
+
+struct Lfm2AudioProcessor : Processor {
+  Lfm2AudioProcessor(Config& config, const SessionInfo& session_info);
+
+  std::unique_ptr<NamedTensors> Process(const Tokenizer& tokenizer, const Payload& payload) const override;
+
+ private:
+  ONNXTensorElementDataType mel_type_;
+  Lfm2AudioMelConfig mel_config_;
+  int64_t subsampling_factor_{};
+  int32_t audio_token_id_{};
+};
+
+}  // namespace Generators
diff --git a/src/models/preprocessing/multi_modal_processor.cpp b/src/models/preprocessing/multi_modal_processor.cpp
index 45b31646e0..a0ae8443fd 100644
--- a/src/models/preprocessing/multi_modal_processor.cpp
+++ b/src/models/preprocessing/multi_modal_processor.cpp
@@ -7,6 +7,7 @@
 #include "models/preprocessing/gemma4_multimodal_processor.h"
 #include "models/preprocessing/gemma_image_processor.h"
 #include "models/preprocessing/genai_tokenizer.h"
+#include "models/preprocessing/lfm2_audio_processor.h"
 #include "models/preprocessing/lfm2_vl_image_processor.h"
 #include "models/preprocessing/mistral3_image_processor.h"
 #include "models/preprocessing/parakeet_processor.h"
@@ -32,6 +33,7 @@ MultiModalProcessor::MultiModalProcessor(Config& config, const SessionInfo& sess
           {"parakeet_tdt", Processor::Create<ParakeetTdtProcessor>},
           {"phi4mm", Processor::Create<PhiMultiModalProcessor>},
           {"gemma3", Processor::Create<GemmaImageProcessor>},
+          {"lfm2_audio", Processor::Create<Lfm2AudioProcessor>},
           {"lfm2_vl", Processor::Create<Lfm2VlImageProcessor>},
           {"gemma4", Processor::Create<Gemma4MultiModalProcessor>},
           {"mistral3", Processor::Create<Mistral3ImageProcessor>},
diff --git a/src/python/py/models/README.md b/src/python/py/models/README.md
index 1d8d081d5c..94d26904fc 100644
--- a/src/python/py/models/README.md
+++ b/src/python/py/models/README.md
@@ -74,7 +74,7 @@ The tool currently supports the following model architectures.
 - Granite MoE Hybrid
 - HunYuan Dense V1
 - InternLM2
-- LFM2 (text and the decoder of LFM2-VL / LFM2.5-VL)
+- LFM2 (text and the decoders of LFM2-VL / LFM2.5-VL and LFM2-Audio / LFM2.5-Audio)
 - LFM2 MoE
 - Llama
 - Mistral
diff --git a/src/python/py/models/builder.py b/src/python/py/models/builder.py
index 703ebb192a..5e23ad669f 100644
--- a/src/python/py/models/builder.py
+++ b/src/python/py/models/builder.py
@@ -28,8 +28,10 @@
     GraniteMoEHybridModel,
     HunyuanDenseV1Model,
     InternLM2Model,
+    LFM2AudioModel,
     LFM2Model,
     LFM2MoEModel,
+    load_lfm2_audio_config,
     LlamaModel,
     Mistral3TextModel,
     MistralModel,
@@ -101,7 +103,10 @@ def get_hf_details(model_name, input_path, cache_dir, extra_options):
     hf_token = extra_options.get("hf_token", True)
     hf_remote = extra_options.get("hf_remote", False)
 
-    config = AutoConfig.from_pretrained(hf_name, token=hf_token, trust_remote_code=hf_remote, **extra_kwargs)
+    # LFM2-Audio checkpoints have no model_type, so AutoConfig cannot read them: use their nested LFM2 decoder config.
+    config = load_lfm2_audio_config(hf_name, token=hf_token, **extra_kwargs)
+    if config is None:
+        config = AutoConfig.from_pretrained(hf_name, token=hf_token, trust_remote_code=hf_remote, **extra_kwargs)
     tokenizer = AutoTokenizer.from_pretrained(hf_name, token=hf_token, trust_remote_code=hf_remote, **extra_kwargs)
     add_special_token_ids(config, tokenizer)
     if extra_options.get("adapter_path", False):
@@ -628,6 +633,10 @@ def create_model(
         # With the embedding layer excluded the decoder is one stage of the LFM2-VL vision pipeline;
         # otherwise it is a standalone text model that happens to come from a VLM checkpoint.
         onnx_model.model_type = "lfm2_vl" if onnx_model.exclude_embeds else "lfm2_vl_text"
+    elif config.architectures[0] == "Lfm2AudioForConditionalGeneration":
+        onnx_model = LFM2AudioModel(config, io_dtype, onnx_dtype, execution_provider, cache_dir, extra_options)
+        # Same split as LFM2-VL: the decoder stage of the lfm2_audio pipeline, or a plain text model.
+        onnx_model.model_type = "lfm2_audio" if onnx_model.exclude_embeds else "lfm2_audio_text"
     elif config.architectures[0] == "LlamaForCausalLM":
         onnx_model = LlamaModel(config, io_dtype, onnx_dtype, execution_provider, cache_dir, extra_options)
     elif config.architectures[0] == "MistralForCausalLM":
diff --git a/src/python/py/models/builders/__init__.py b/src/python/py/models/builders/__init__.py
index 0925693898..90190f7e3a 100644
--- a/src/python/py/models/builders/__init__.py
+++ b/src/python/py/models/builders/__init__.py
@@ -14,7 +14,7 @@
 from .granite import GraniteModel, GraniteMoEHybridModel
 from .hunyuan import HunyuanDenseV1Model
 from .internlm import InternLM2Model
-from .lfm2 import LFM2Model, LFM2MoEModel
+from .lfm2 import LFM2AudioModel, LFM2Model, LFM2MoEModel, load_lfm2_audio_config
 from .llama import LlamaModel
 from .mistral import Mistral3TextModel, MistralModel
 from .mtp import MTPModel
@@ -57,8 +57,10 @@
     "GraniteModel",
     "HunyuanDenseV1Model",
     "InternLM2Model",
+    "LFM2AudioModel",
     "LFM2Model",
     "LFM2MoEModel",
+    "load_lfm2_audio_config",
     "LlamaModel",
     "MTPModel",
     "Mistral3TextModel",
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index c7e4dce6e0..1683bb7b38 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -4,10 +4,16 @@
 # license information.
 # Modifications Copyright(C) 2026 Advanced Micro Devices, Inc. All rights reserved.
 # --------------------------------------------------------------------------
+import json
+import os
+
 import onnx_ir as ir
+import torch
 
 from .base import Model
 
+LFM2_AUDIO_ARCHITECTURE = "Lfm2AudioForConditionalGeneration"
+
 
 class LFM2Model(Model):
     def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
@@ -190,6 +196,87 @@ def update_genai_config(self, genai_config):
         decoder["conv_cache_size"] = self.conv_L_cache - 1
 
 
+def load_lfm2_audio_config(model_name_or_path, **kwargs):
+    """The LFM2 decoder config of an LFM2-Audio checkpoint (LFM2-Audio-1.5B, LFM2.5-Audio-1.5B).
+
+    These checkpoints have no transformers model class and their config.json nests the decoder
+    config under "lfm" next to the audio encoder, depthformer and mel front-end settings, so
+    AutoConfig cannot read them. Returns None if the checkpoint is not an LFM2-Audio one.
+    """
+    from transformers import Lfm2Config, PretrainedConfig
+
+    try:
+        config_dict, _ = PretrainedConfig.get_config_dict(model_name_or_path, **kwargs)
+    except Exception:  # noqa: BLE001 - not an LFM2-Audio checkpoint (or not readable): AutoConfig reports the real error
+        return None
+    if config_dict.get("architectures") != [LFM2_AUDIO_ARCHITECTURE]:
+        return None
+    config = Lfm2Config(**config_dict["lfm"])
+    config.architectures = [LFM2_AUDIO_ARCHITECTURE]
+    config._name_or_path = model_name_or_path
+    return config
+
+
+class LFM2AudioModel(LFM2Model):
+    """The LFM2 decoder of an LFM2-Audio checkpoint, exported for text generation.
+
+    The checkpoint stores the decoder as an Lfm2Model under the "lfm." prefix, with the logits tied
+    to the token embeddings; the audio encoder, depthformer and audio embeddings that surround it in
+    the checkpoint are left out. With the embedding layer excluded the export is the decoder stage of
+    the lfm2_audio pipeline, whose embedding model splices the audio encoder output into the token
+    embeddings; otherwise it is a plain text model.
+    """
+
+    def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
+        super().__init__(config, io_dtype, onnx_dtype, ep, cache_dir, extra_options)
+        self.decoder_config = config
+
+    def load_weights(self, input_path):
+        if input_path.endswith(".gguf") or self.quant_type is not None:
+            return super().load_weights(input_path)
+
+        from transformers import Lfm2ForCausalLM
+
+        with torch.device("meta"):
+            model = Lfm2ForCausalLM(self.decoder_config)
+        state_dict = {}
+        for name, tensor in self.load_lfm2_audio_checkpoint(input_path).items():
+            if name.startswith("lfm."):
+                state_dict["model." + name[len("lfm.") :]] = tensor
+        missing, unexpected = model.load_state_dict(state_dict, strict=False, assign=True)
+        if unexpected or set(missing) - {"lm_head.weight"}:
+            raise ValueError(
+                f"The LFM2 decoder of {input_path} does not match its config: missing {sorted(missing)}, unexpected {sorted(unexpected)}."
+            )
+        model.tie_weights()
+        return model.eval()
+
+    def load_lfm2_audio_checkpoint(self, input_path):
+        """The checkpoint tensors, from a local directory or the Hugging Face Hub."""
+        from safetensors.torch import load_file
+
+        if os.path.isdir(input_path):
+            checkpoint_dir = input_path
+        else:
+            from huggingface_hub import snapshot_download
+
+            checkpoint_dir = snapshot_download(
+                input_path, cache_dir=self.cache_dir, token=self.hf_token, allow_patterns=["*.safetensors", "*.json"]
+            )
+
+        index_path = os.path.join(checkpoint_dir, "model.safetensors.index.json")
+        if os.path.exists(index_path):
+            with open(index_path) as f:
+                shards = sorted(set(json.load(f)["weight_map"].values()))
+        else:
+            shards = ["model.safetensors"]
+
+        tensors = {}
+        for shard in shards:
+            tensors.update(load_file(os.path.join(checkpoint_dir, shard)))
+        return tensors
+
+
 class LFM2MoEModel(LFM2Model):
     """LFM2-MoE builder (LFM2-8B-A1B, LFM2.5-8B-A1B, LFM2-24B-A2B).
 
diff --git a/src/python/python.cpp b/src/python/python.cpp
index 3f2885794a..c982cc2820 100644
--- a/src/python/python.cpp
+++ b/src/python/python.cpp
@@ -675,12 +675,19 @@ PYBIND11_MODULE(onnxruntime_genai, m) {
   pybind11::class_<OgaImages>(m, "Images")
       .def_static("open", [](pybind11::args image_paths) {
         std::vector<std::string> image_paths_string;
-        std::vector<const char*> image_paths_vector;
+        image_paths_string.reserve(image_paths.size());
         for (auto image_path : image_paths) {
           if (!pybind11::isinstance<pybind11::str>(image_path))
             throw std::runtime_error("Image paths must be strings.");
           image_paths_string.push_back(image_path.cast<std::string>());
-          image_paths_vector.push_back(image_paths_string.back().c_str());
+        }
+
+        // Take the pointers only once the strings have stopped moving: a short string keeps its
+        // characters inside the string object, so growing the vector would dangle them.
+        std::vector<const char*> image_paths_vector;
+        image_paths_vector.reserve(image_paths_string.size());
+        for (const auto& image_path : image_paths_string) {
+          image_paths_vector.push_back(image_path.c_str());
         }
 
         return OgaImages::Load(image_paths_vector);
@@ -703,13 +710,19 @@ PYBIND11_MODULE(onnxruntime_genai, m) {
   pybind11::class_<OgaAudios>(m, "Audios")
       .def_static("open", [](pybind11::args audio_paths) {
         std::vector<std::string> audio_paths_string;
-        std::vector<const char*> audio_paths_vector;
-
+        audio_paths_string.reserve(audio_paths.size());
         for (const auto& audio_path : audio_paths) {
           if (!pybind11::isinstance<pybind11::str>(audio_path))
             throw std::runtime_error("Audio paths must be strings.");
           audio_paths_string.push_back(audio_path.cast<std::string>());
-          audio_paths_vector.push_back(audio_paths_string.back().c_str());
+        }
+
+        // Take the pointers only once the strings have stopped moving: a short string keeps its
+        // characters inside the string object, so growing the vector would dangle them.
+        std::vector<const char*> audio_paths_vector;
+        audio_paths_vector.reserve(audio_paths_string.size());
+        for (const auto& audio_path : audio_paths_string) {
+          audio_paths_vector.push_back(audio_path.c_str());
         }
 
         return OgaAudios::Load(audio_paths_vector);
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index f5b5c5345c..65e468535f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -15,6 +15,7 @@ file(GLOB test_srcs CONFIGURE_DEPENDS
 # Keep tests with dedicated white-box executables out of the public-API unit_tests target.
 list(REMOVE_ITEM test_srcs "${CMAKE_CURRENT_SOURCE_DIR}/cpp/dflash2_config_test.cpp")
 list(REMOVE_ITEM test_srcs "${CMAKE_CURRENT_SOURCE_DIR}/cpp/kv_cache_tests.cpp")
+list(REMOVE_ITEM test_srcs "${CMAKE_CURRENT_SOURCE_DIR}/cpp/lfm2_audio_frames_test.cpp")
 list(REMOVE_ITEM test_srcs "${CMAKE_CURRENT_SOURCE_DIR}/cpp/lfm2_vl_image_geometry_test.cpp")
 list(REMOVE_ITEM test_srcs "${CMAKE_CURRENT_SOURCE_DIR}/cpp/reinit_tests.cpp")
 list(REMOVE_ITEM test_srcs "${CMAKE_CURRENT_SOURCE_DIR}/cpp/search_checkpoint_tests.cpp")
@@ -202,6 +203,7 @@ file(GLOB engine_unit_test_srcs CONFIGURE_DEPENDS
 )
 list(APPEND engine_unit_test_srcs
   "${CMAKE_CURRENT_SOURCE_DIR}/cpp/dflash2_config_test.cpp"
+  "${CMAKE_CURRENT_SOURCE_DIR}/cpp/lfm2_audio_frames_test.cpp"
   "${CMAKE_CURRENT_SOURCE_DIR}/cpp/lfm2_vl_image_geometry_test.cpp"
   "${CMAKE_CURRENT_SOURCE_DIR}/cpp/search_checkpoint_tests.cpp"
 )
diff --git a/test/cpp/lfm2_audio_frames_test.cpp b/test/cpp/lfm2_audio_frames_test.cpp
new file mode 100644
index 0000000000..a03978fb53
--- /dev/null
+++ b/test/cpp/lfm2_audio_frames_test.cpp
@@ -0,0 +1,157 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include <gtest/gtest.h>
+
+#include <cmath>
+#include <string>
+#include <vector>
+
+#include "models/preprocessing/lfm2_audio_processor.h"
+
+// LFM2-Audio turns a clip into log-mel frames, one per 10 ms hop, and the encoder subsamples those
+// by 8, so the decoder sees one token per 80 ms. These tests pin the three things that have to agree
+// for the pipeline to line up: how many frames a clip is worth, how many of them the reference
+// preprocessor counts as valid, and how the prompt is split around the audio markers.
+
+namespace Generators::test {
+namespace {
+
+constexpr Lfm2AudioMelConfig kMelConfig{};  // settings of the published checkpoints
+constexpr int64_t kSubsamplingFactor = 8;
+
+template <typename Fn>
+std::string CaptureThrowMessage(Fn&& fn) {
+  try {
+    fn();
+  } catch (const std::exception& e) {
+    return e.what();
+  }
+  return {};
+}
+
+}  // namespace
+
+TEST(Lfm2AudioFramesTest, OneSecondIsOneHundredFramesPlusTheCenterPadOne) {
+  // torch.stft(center=True) pads by fft_size / 2 on both sides, which buys exactly one extra frame.
+  EXPECT_EQ(Lfm2AudioNumMelFrames(16000, kMelConfig), 101);
+  // NeMo's get_seq_len counts num_samples / hop_length instead, one fewer.
+  EXPECT_EQ(Lfm2AudioNumValidMelFrames(16000, kMelConfig), 100);
+}
+
+TEST(Lfm2AudioFramesTest, PartialHopsAreTruncated) {
+  EXPECT_EQ(Lfm2AudioNumMelFrames(16159, kMelConfig), 101);
+  EXPECT_EQ(Lfm2AudioNumMelFrames(16160, kMelConfig), 102);
+  EXPECT_EQ(Lfm2AudioNumValidMelFrames(16159, kMelConfig), 100);
+  EXPECT_EQ(Lfm2AudioNumValidMelFrames(16160, kMelConfig), 101);
+}
+
+TEST(Lfm2AudioFramesTest, ClipShorterThanTheWindowStillHasFrames) {
+  // The center padding alone is fft_size samples wide, so even one sample produces frames; they are
+  // just not all valid.
+  EXPECT_EQ(Lfm2AudioNumMelFrames(1, kMelConfig), 1);
+  EXPECT_EQ(Lfm2AudioNumValidMelFrames(1, kMelConfig), 0);
+  EXPECT_EQ(Lfm2AudioNumMelFrames(0, kMelConfig), 1);
+}
+
+TEST(Lfm2AudioFramesTest, TokensRoundUpToWholeEncoderFrames) {
+  EXPECT_EQ(Lfm2AudioNumTokens(8, kSubsamplingFactor), 1);
+  EXPECT_EQ(Lfm2AudioNumTokens(9, kSubsamplingFactor), 2);
+  EXPECT_EQ(Lfm2AudioNumTokens(101, kSubsamplingFactor), 13);
+  EXPECT_EQ(Lfm2AudioNumTokens(0, kSubsamplingFactor), 0);
+}
+
+TEST(Lfm2AudioFramesTest, TokensRejectANonPositiveSubsamplingFactor) {
+  EXPECT_NE(CaptureThrowMessage([] { Lfm2AudioNumTokens(64, 0); }).find("subsampling_factor must be positive"),
+            std::string::npos);
+}
+
+TEST(Lfm2AudioMelTest, NormalizesEveryValidFrameAndZeroesTheRest) {
+  // Half a second of a tone: every mel bin should end up zero-mean over the valid frames, and the
+  // frames past the valid length are the pad_value NeMo writes.
+  constexpr int64_t kNumSamples = 8000;
+  std::vector<float> pcm(kNumSamples);
+  for (int64_t i = 0; i < kNumSamples; ++i) {
+    pcm[static_cast<size_t>(i)] = 0.25f * std::sin(2.0f * 3.14159265358979323846f * 440.0f * static_cast<float>(i) / 16000.0f);
+  }
+
+  int64_t num_frames = 0;
+  const auto mel = ComputeLfm2AudioMel(pcm.data(), kNumSamples, kMelConfig, num_frames);
+
+  const int64_t valid_frames = Lfm2AudioNumValidMelFrames(kNumSamples, kMelConfig);
+  ASSERT_EQ(num_frames, Lfm2AudioNumMelFrames(kNumSamples, kMelConfig));
+  ASSERT_EQ(num_frames, valid_frames + 1);
+  ASSERT_EQ(mel.size(), static_cast<size_t>(num_frames) * static_cast<size_t>(kMelConfig.num_mels));
+
+  for (int64_t m = 0; m < kMelConfig.num_mels; ++m) {
+    double sum = 0.0;
+    double square_sum = 0.0;
+    for (int64_t t = 0; t < valid_frames; ++t) {
+      const double value = mel[static_cast<size_t>(t) * static_cast<size_t>(kMelConfig.num_mels) + static_cast<size_t>(m)];
+      sum += value;
+      square_sum += value * value;
+    }
+    EXPECT_NEAR(sum / valid_frames, 0.0, 1e-4) << "mel bin " << m << " is not zero-mean";
+    // Unbiased standard deviation of 1, up to the epsilon added to the divisor.
+    EXPECT_NEAR(std::sqrt(square_sum / (valid_frames - 1)), 1.0, 1e-3) << "mel bin " << m << " is not unit-variance";
+  }
+  for (int64_t m = 0; m < kMelConfig.num_mels; ++m) {
+    EXPECT_EQ(mel[static_cast<size_t>(valid_frames) * static_cast<size_t>(kMelConfig.num_mels) + static_cast<size_t>(m)], 0.0f);
+  }
+}
+
+TEST(Lfm2AudioMelTest, RejectsAClipTooShortToNormalize) {
+  const std::vector<float> pcm(160, 0.1f);  // one valid frame; the standard deviation would divide by zero
+  int64_t num_frames = 0;
+  const auto message = CaptureThrowMessage([&] { ComputeLfm2AudioMel(pcm.data(), 160, kMelConfig, num_frames); });
+  EXPECT_NE(message.find("too short"), std::string::npos) << message;
+  EXPECT_NE(message.find("320 samples are needed"), std::string::npos) << message;
+}
+
+TEST(Lfm2AudioMelTest, RejectsAWindowLongerThanTheFftSize) {
+  Lfm2AudioMelConfig config = kMelConfig;
+  config.win_length = config.fft_size + 1;
+  int64_t num_frames = 0;
+  const std::vector<float> pcm(16000, 0.0f);
+  EXPECT_NE(CaptureThrowMessage([&] { ComputeLfm2AudioMel(pcm.data(), 16000, config, num_frames); })
+                .find("invalid mel configuration"),
+            std::string::npos);
+}
+
+TEST(Lfm2AudioPromptTest, SplitsAroundEveryMarker) {
+  const auto segments = SplitLfm2AudioPrompt("before <|audio|> middle <|audio|> after", 2);
+  ASSERT_EQ(segments.size(), 3u);
+  EXPECT_EQ(segments[0], "before ");
+  EXPECT_EQ(segments[1], " middle ");
+  EXPECT_EQ(segments[2], " after");
+}
+
+TEST(Lfm2AudioPromptTest, KeepsEmptySegmentsAroundAdjacentMarkers) {
+  // Adjacent markers and a prompt that is nothing but a marker still need their empty text segments,
+  // so the clips stay in order and nothing is dropped.
+  const auto segments = SplitLfm2AudioPrompt("<|audio|><|audio|>", 2);
+  ASSERT_EQ(segments.size(), 3u);
+  EXPECT_EQ(segments[0], "");
+  EXPECT_EQ(segments[1], "");
+  EXPECT_EQ(segments[2], "");
+}
+
+TEST(Lfm2AudioPromptTest, TextOnlyPromptWithNoClipsIsOneSegment) {
+  const auto segments = SplitLfm2AudioPrompt("no audio here", 0);
+  ASSERT_EQ(segments.size(), 1u);
+  EXPECT_EQ(segments[0], "no audio here");
+}
+
+TEST(Lfm2AudioPromptTest, RejectsAMarkerCountThatDoesNotMatchTheClips) {
+  EXPECT_NE(CaptureThrowMessage([] { SplitLfm2AudioPrompt("only text", 1); })
+                .find("contains 0 <|audio|> markers but 1 audio clips"),
+            std::string::npos);
+  EXPECT_NE(CaptureThrowMessage([] { SplitLfm2AudioPrompt("<|audio|> and <|audio|>", 1); })
+                .find("contains 2 <|audio|> markers but 1 audio clips"),
+            std::string::npos);
+  EXPECT_NE(CaptureThrowMessage([] { SplitLfm2AudioPrompt("<|audio|>", 0); })
+                .find("contains 1 <|audio|> markers but 0 audio clips"),
+            std::string::npos);
+}
+
+}  // namespace Generators::test
diff --git a/test/models/lfm2-audio/dummy_embedding.onnx b/test/models/lfm2-audio/dummy_embedding.onnx
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diff --git a/test/models/lfm2-audio/dummy_speech.onnx b/test/models/lfm2-audio/dummy_speech.onnx
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diff --git a/test/models/lfm2-audio/dummy_text.onnx b/test/models/lfm2-audio/dummy_text.onnx
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diff --git a/test/models/lfm2-audio/genai_config.json b/test/models/lfm2-audio/genai_config.json
new file mode 100644
index 0000000000..975860af66
--- /dev/null
+++ b/test/models/lfm2-audio/genai_config.json
@@ -0,0 +1,88 @@
+{
+    "model": {
+        "audio_token_id": 133,
+        "bos_token_id": 1,
+        "context_length": 4096,
+        "decoder": {
+            "session_options": {
+                "log_id": "onnxruntime-genai",
+                "provider_options": []
+            },
+            "filename": "dummy_text.onnx",
+            "head_size": 16,
+            "hidden_size": 32,
+            "inputs": {
+                "inputs_embeds": "inputs_embeds",
+                "attention_mask": "attention_mask",
+                "past_key_names": "past_key_values.%d.key",
+                "past_value_names": "past_key_values.%d.value",
+                "past_conv_names": "past.%d.conv"
+            },
+            "outputs": {
+                "logits": "logits",
+                "present_key_names": "present.%d.key",
+                "present_value_names": "present.%d.value",
+                "present_conv_names": "present.%d.conv"
+            },
+            "num_attention_heads": 2,
+            "num_hidden_layers": 4,
+            "num_key_value_heads": 2,
+            "layer_types": [
+                "conv",
+                "full_attention",
+                "conv",
+                "full_attention"
+            ],
+            "conv_cache_size": 2
+        },
+        "eos_token_id": 7,
+        "pad_token_id": 0,
+        "type": "lfm2_audio",
+        "vocab_size": 65536,
+        "embedding": {
+            "session_options": {
+                "log_id": "onnxruntime-genai",
+                "provider_options": []
+            },
+            "filename": "dummy_embedding.onnx",
+            "inputs": {
+                "input_ids": "input_ids",
+                "audio_features": "audio_features"
+            },
+            "outputs": {
+                "inputs_embeds": "inputs_embeds"
+            }
+        },
+        "speech": {
+            "session_options": {
+                "log_id": "onnxruntime-genai",
+                "provider_options": []
+            },
+            "filename": "dummy_speech.onnx",
+            "inputs": {
+                "audio_embeds": "mel_spectrogram",
+                "audio_lengths": "mel_lengths",
+                "audio_sizes": "audio_sizes"
+            },
+            "outputs": {
+                "audio_features": "audio_embeddings"
+            }
+        }
+    },
+    "search": {
+        "diversity_penalty": 0.0,
+        "do_sample": false,
+        "early_stopping": true,
+        "length_penalty": 1.0,
+        "max_length": 4096,
+        "min_length": 0,
+        "no_repeat_ngram_size": 0,
+        "num_beams": 1,
+        "num_return_sequences": 1,
+        "past_present_share_buffer": false,
+        "repetition_penalty": 1.0,
+        "temperature": 1.0,
+        "top_k": 1,
+        "top_p": 1.0
+    }
+}
diff --git a/test/models/lfm2-audio/tokenizer.zip b/test/models/lfm2-audio/tokenizer.zip
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diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
new file mode 100644
index 0000000000..b27b40b056
--- /dev/null
+++ b/test/python/builder/test_lfm2_audio.py
@@ -0,0 +1,179 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License
+
+"""Unit tests for the LFM2-Audio model builder wiring.
+
+LFM2-Audio checkpoints (LFM2-Audio-1.5B, LFM2.5-Audio-1.5B) have no transformers model class: their
+config.json nests the LFM2 decoder config under "lfm" and the checkpoint stores the decoder weights
+under the "lfm." prefix next to the audio encoder, depthformer and audio embeddings. The builder has
+to read that nested config, load only the decoder with its logits tied to the token embeddings, and
+label the export `lfm2_audio` (a pipeline stage) or `lfm2_audio_text` (a text-only decoder).
+"""
+
+from __future__ import annotations
+
+import json
+import types
+
+import pytest
+from _builder_test_utils import load_builder_module
+from test_lfm2_vl import builder_module
+
+base_module = load_builder_module("base")
+lfm2_module = load_builder_module("lfm2")
+LFM2AudioModel = lfm2_module.LFM2AudioModel
+
+torch = pytest.importorskip("torch")
+transformers = pytest.importorskip("transformers")
+save_file = pytest.importorskip("safetensors.torch").save_file
+# `_builder_test_utils` substitutes a stub for transformers when it is missing, which has none of the
+# LFM2 classes these tests build a checkpoint from.
+pytestmark = pytest.mark.skipif(
+    not hasattr(transformers, "Lfm2ForCausalLM"), reason="transformers does not provide the LFM2 model classes"
+)
+
+DECODER_CONFIG = {
+    "architectures": ["Lfm2ForCausalLM"],
+    "block_auto_adjust_ff_dim": True,
+    "block_ff_dim": 96,
+    "block_ffn_dim_multiplier": 1.0,
+    "block_multiple_of": 16,
+    "conv_L_cache": 3,
+    "conv_bias": False,
+    "hidden_size": 32,
+    "intermediate_size": 96,
+    "layer_types": ["conv", "full_attention", "conv", "full_attention"],
+    "model_type": "lfm2",
+    "norm_eps": 1e-05,
+    "num_attention_heads": 4,
+    "num_hidden_layers": 4,
+    "num_key_value_heads": 2,
+    "rope_theta": 1000000,
+    "vocab_size": 512,
+}
+
+
+def _write_checkpoint(path, decoder_weights=None):
+    """An LFM2-Audio checkpoint directory: nested config.json plus lfm.* and audio tensors."""
+    path.mkdir(parents=True, exist_ok=True)
+    config = {
+        "architectures": ["Lfm2AudioForConditionalGeneration"],
+        "codebooks": 8,
+        "preprocessor": {"sample_rate": 16000, "features": 128, "n_fft": 512},
+        "encoder": {"feat_in": 128, "d_model": 512, "n_layers": 17},
+        "lfm": DECODER_CONFIG,
+        "depthformer": {"layers": 6, "dim": 1024, "tie": True},
+    }
+    (path / "config.json").write_text(json.dumps(config))
+
+    if decoder_weights is None:
+        torch.manual_seed(0)
+        model = transformers.Lfm2ForCausalLM(transformers.Lfm2Config(**DECODER_CONFIG))
+        decoder_weights = {
+            "lfm." + name[len("model.") :]: tensor
+            for name, tensor in model.state_dict().items()
+            if name.startswith("model.")
+        }
+    tensors = dict(decoder_weights)
+    tensors["conformer.pre_encode.out.weight"] = torch.zeros(4, 4)
+    tensors["audio_adapter.model.1.weight"] = torch.zeros(32, 8)
+    tensors["audio_embedding.embedding.weight"] = torch.zeros(16, 32)
+    tensors["depth_linear.weight"] = torch.zeros(8, 32)
+    tensors["codebook_offsets"] = torch.arange(8) * 2049
+    save_file({name: tensor.contiguous() for name, tensor in tensors.items()}, str(path / "model.safetensors"))
+    return decoder_weights
+
+
+def test_load_lfm2_audio_config_reads_the_nested_decoder_config(tmp_path):
+    _write_checkpoint(tmp_path)
+
+    config = lfm2_module.load_lfm2_audio_config(str(tmp_path))
+
+    assert config.architectures == ["Lfm2AudioForConditionalGeneration"]
+    assert config._name_or_path == str(tmp_path)
+    assert config.hidden_size == DECODER_CONFIG["hidden_size"]
+    assert config.layer_types == DECODER_CONFIG["layer_types"]
+    assert config.conv_L_cache == DECODER_CONFIG["conv_L_cache"]
+    assert config.tie_word_embeddings
+
+
+def test_load_lfm2_audio_config_leaves_other_checkpoints_to_autoconfig(tmp_path):
+    (tmp_path / "config.json").write_text(json.dumps(DECODER_CONFIG))
+    assert lfm2_module.load_lfm2_audio_config(str(tmp_path)) is None
+    assert lfm2_module.load_lfm2_audio_config(str(tmp_path / "missing")) is None
+
+
+def _audio_builder(tmp_path):
+    model = LFM2AudioModel.__new__(LFM2AudioModel)
+    model.decoder_config = lfm2_module.load_lfm2_audio_config(str(tmp_path))
+    model.quant_type = None
+    model.cache_dir = str(tmp_path / "cache")
+    model.hf_token = True
+    return model
+
+
+def test_lfm2_audio_load_weights_loads_the_decoder_with_tied_logits(tmp_path):
+    decoder_weights = _write_checkpoint(tmp_path)
+
+    loaded = _audio_builder(tmp_path).load_weights(str(tmp_path))
+
+    assert type(loaded).__name__ == "Lfm2ForCausalLM"
+    torch.testing.assert_close(
+        loaded.model.layers[0].conv.in_proj.weight, decoder_weights["lfm.layers.0.conv.in_proj.weight"]
+    )
+    torch.testing.assert_close(loaded.model.embedding_norm.weight, decoder_weights["lfm.embedding_norm.weight"])
+    assert loaded.lm_head.weight.data_ptr() == loaded.model.embed_tokens.weight.data_ptr()
+    assert not any("conformer" in name or "depth" in name or "audio" in name for name, _ in loaded.named_parameters())
+    assert not any(parameter.is_meta for parameter in loaded.parameters())
+
+
+def test_lfm2_audio_load_weights_rejects_a_checkpoint_missing_decoder_tensors(tmp_path):
+    torch.manual_seed(0)
+    model = transformers.Lfm2ForCausalLM(transformers.Lfm2Config(**DECODER_CONFIG))
+    decoder_weights = {
+        "lfm." + name[len("model.") :]: tensor
+        for name, tensor in model.state_dict().items()
+        if name.startswith("model.")
+    }
+    del decoder_weights["lfm.layers.1.self_attn.q_proj.weight"]
+    _write_checkpoint(tmp_path, decoder_weights)
+
+    with pytest.raises(
+        ValueError, match=r"does not match its config: missing .*'model\.layers\.1\.self_attn\.q_proj\.weight'"
+    ):
+        _audio_builder(tmp_path).load_weights(str(tmp_path))
+
+
+@pytest.mark.parametrize("exclude_embeds,expected_type", [(True, "lfm2_audio"), (False, "lfm2_audio_text")])
+def test_lfm2_audio_architecture_labels_the_export(monkeypatch, tmp_path, exclude_embeds, expected_type):
+    captured = {}
+
+    class FakeLFM2AudioModel:
+        model_type = "lfm2"
+
+        def __init__(self, *args):
+            captured["args"] = args
+            self.exclude_embeds = exclude_embeds
+
+        def make_genai_config(self, *args):
+            captured["model_type"] = self.model_type
+
+        def save_processing(self, *args):
+            pass
+
+    config = types.SimpleNamespace(architectures=["Lfm2AudioForConditionalGeneration"])
+    monkeypatch.setattr(builder_module, "LFM2AudioModel", FakeLFM2AudioModel)
+
+    builder_module.create_model(
+        "LiquidAI/LFM2.5-Audio-1.5B",
+        str(tmp_path / "input"),
+        str(tmp_path / "output"),
+        "int4",
+        "cpu",
+        str(tmp_path / "cache"),
+        config_only=True,
+        hf_details={"extra_kwargs": {}, "hf_name": "LiquidAI/LFM2.5-Audio-1.5B", "hf_config": config},
+    )
+
+    assert captured["args"][0] is config
+    assert captured["model_type"] == expected_type
diff --git a/test/python/builder/test_qmoe_weights.py b/test/python/builder/test_qmoe_weights.py
index 7e09a01405..d77a36f6f6 100644
--- a/test/python/builder/test_qmoe_weights.py
+++ b/test/python/builder/test_qmoe_weights.py
@@ -131,6 +131,7 @@ def _load_builder_cli_module(monkeypatch):
         "GraniteModel",
         "HunyuanDenseV1Model",
         "InternLM2Model",
+        "LFM2AudioModel",
         "LFM2Model",
         "LFM2MoEModel",
         "LlamaModel",
@@ -158,6 +159,8 @@ def _load_builder_cli_module(monkeypatch):
         "WhisperModel",
     ):
         setattr(builders_module, class_name, type(class_name, (), {}))
+    # Not a model class: the LFM2-Audio config reader builder.py calls before the dispatch.
+    builders_module.load_lfm2_audio_config = lambda *args, **kwargs: None
     # Submodule imports (e.g. `from quantization import ...`) must resolve to the
     # real, dependency-free modules rather than the class stubs above.
     builders_module.__path__ = [str(BUILDERS_DIR)]
diff --git a/test/python/create/create_dummy_lfm2_audio_models.py b/test/python/create/create_dummy_lfm2_audio_models.py
new file mode 100644
index 0000000000..31321ea825
--- /dev/null
+++ b/test/python/create/create_dummy_lfm2_audio_models.py
@@ -0,0 +1,223 @@
+#!/usr/bin/env python3
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License
+
+"""
+Generate tiny ONNX models for LFM2-Audio pipeline testing.
+
+Writes the three models the lfm2_audio pipeline runs, with the input/output contract of the published
+LFM2-Audio / LFM2.5-Audio encoder export (LiquidAI/LFM2.5-Audio-1.5B-ONNX), plus the genai_config.json
+that wires them together:
+
+  dummy_speech.onnx     mel_spectrogram [N, T, 128], mel_lengths [N] (int64)
+                        -> audio_embeddings [N, ceil(T / 8), hidden], audio_lengths [N] (int64)
+                        Every 8 mel frames are averaged and projected, so the features depend on the
+                        audio and the frame count matches the placeholders the processor writes.
+  dummy_embedding.onnx  input_ids, audio_features -> inputs_embeds
+                        Token lookup with audio_features scattered over the positions holding
+                        AUDIO_TOKEN_ID, the graph the tutorial describes for the real model.
+  dummy_text.onnx       The LFM2 decoder of the LFM2-VL fixture (see create_dummy_lfm2_vl_models.py):
+                        each logit depends on the current token and the two before it through a causal
+                        convolution over past.%d.conv, so generation only matches between runs when the
+                        conv state is carried correctly.
+
+The tokenizer is checked in separately, and zipped: tokenizer.zip holds tokenizer.json and
+tokenizer_config.json from LiquidAI/LFM2.5-Audio-1.5B (LFM2-Audio-1.5B ships the same files), whose
+4.8 MB compresses to under 1 MB. test_lfm2_audio_models.py unpacks it into a temporary directory
+next to these models.
+
+Usage:
+    python create_dummy_lfm2_audio_models.py --output test/models/lfm2-audio
+"""
+
+import argparse
+import json
+import os
+import sys
+
+import numpy as np
+from onnx import TensorProto, helper, numpy_helper
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from create_dummy_lfm2_vl_models import (
+    CONV_CACHE_SIZE,
+    HEAD_SIZE,
+    HIDDEN_SIZE,
+    LAYER_TYPES,
+    LOGIT_VOCAB,
+    NUM_KV_HEADS,
+    VOCAB_SIZE,
+    _const,
+    _save,
+    create_dummy_decoder_model,
+)
+
+NUM_MELS = 128
+SUBSAMPLING_FACTOR = 8
+# <|reserved_123|>: the placeholder the processor writes once per encoder frame. LFM2-Audio has no
+# audio input token of its own, so any single token that never occurs in text will do.
+AUDIO_TOKEN_ID = 133
+
+
+def create_dummy_speech_model(output_path: str, rng: np.random.Generator):
+    """mel_spectrogram, mel_lengths -> audio_embeddings [N, ceil(T / 8), hidden], audio_lengths"""
+    inputs = [
+        helper.make_tensor_value_info("mel_spectrogram", TensorProto.FLOAT, ["batch_size", "time_steps", NUM_MELS]),
+        helper.make_tensor_value_info("mel_lengths", TensorProto.INT64, ["batch_size"]),
+    ]
+    outputs = [
+        helper.make_tensor_value_info(
+            "audio_embeddings", TensorProto.FLOAT, ["batch_size", "reduced_time", HIDDEN_SIZE]
+        ),
+        helper.make_tensor_value_info("audio_lengths", TensorProto.INT64, ["batch_size"]),
+    ]
+    projection = numpy_helper.from_array(
+        (rng.standard_normal((NUM_MELS, HIDDEN_SIZE)) / np.sqrt(NUM_MELS)).astype(np.float32), name="projection"
+    )
+    nodes = [
+        _const("subsampling_minus_one", [SUBSAMPLING_FACTOR - 1]),
+        _const("subsampling", [SUBSAMPLING_FACTOR]),
+        # [N, T, mels] -> [N, mels, T] so the pool runs over time; ceil_mode keeps the partial last window
+        helper.make_node("Transpose", ["mel_spectrogram"], ["mel_nct"], perm=[0, 2, 1]),
+        helper.make_node(
+            "AveragePool",
+            ["mel_nct"],
+            ["pooled_nct"],
+            kernel_shape=[SUBSAMPLING_FACTOR],
+            strides=[SUBSAMPLING_FACTOR],
+            ceil_mode=1,
+        ),
+        helper.make_node("Transpose", ["pooled_nct"], ["pooled"], perm=[0, 2, 1]),
+        helper.make_node("MatMul", ["pooled", "projection"], ["audio_embeddings"]),
+        # audio_lengths = ceil(mel_lengths / subsampling_factor)
+        helper.make_node("Add", ["mel_lengths", "subsampling_minus_one"], ["lengths_rounded"]),
+        helper.make_node("Div", ["lengths_rounded", "subsampling"], ["audio_lengths"]),
+    ]
+    _save(helper.make_graph(nodes, "speech", inputs, outputs, initializer=[projection]), output_path)
+
+
+def create_dummy_embedding_model(output_path: str, rng: np.random.Generator):
+    """input_ids, audio_features -> inputs_embeds, with the audio features scattered over the placeholders"""
+    inputs = [
+        helper.make_tensor_value_info("input_ids", TensorProto.INT64, ["batch_size", "sequence_length"]),
+        helper.make_tensor_value_info("audio_features", TensorProto.FLOAT, ["num_audio_tokens", HIDDEN_SIZE]),
+    ]
+    outputs = [
+        helper.make_tensor_value_info(
+            "inputs_embeds", TensorProto.FLOAT, ["batch_size", "sequence_length", HIDDEN_SIZE]
+        )
+    ]
+    table = numpy_helper.from_array(
+        rng.standard_normal((LOGIT_VOCAB, HIDDEN_SIZE)).astype(np.float32), name="embed_table"
+    )
+    nodes = [
+        _const("table_rows", [LOGIT_VOCAB]),
+        _const("audio_token_id", [AUDIO_TOKEN_ID]),
+        _const("flat_rows", [-1, HIDDEN_SIZE]),
+        _const("flat", [-1]),
+        helper.make_node("Mod", ["input_ids", "table_rows"], ["row_ids"]),
+        helper.make_node("Gather", ["embed_table", "row_ids"], ["text_embeds"], axis=0),
+        # Positions of the placeholders in the flattened [batch * sequence] token stream, in order,
+        # receive the audio features row by row.
+        helper.make_node("Shape", ["text_embeds"], ["embeds_shape"]),
+        helper.make_node("Reshape", ["text_embeds", "flat_rows"], ["flat_embeds"]),
+        helper.make_node("Reshape", ["input_ids", "flat"], ["flat_ids"]),
+        helper.make_node("Equal", ["flat_ids", "audio_token_id"], ["is_audio"]),
+        helper.make_node("NonZero", ["is_audio"], ["audio_positions_t"]),
+        helper.make_node("Transpose", ["audio_positions_t"], ["audio_positions"], perm=[1, 0]),
+        helper.make_node("ScatterND", ["flat_embeds", "audio_positions", "audio_features"], ["merged"]),
+        helper.make_node("Reshape", ["merged", "embeds_shape"], ["inputs_embeds"]),
+    ]
+    _save(helper.make_graph(nodes, "embedding", inputs, outputs, initializer=[table]), output_path)
+
+
+def create_genai_config(output_path: str):
+    session_options = {"log_id": "onnxruntime-genai", "provider_options": []}
+    config = {
+        "model": {
+            "audio_token_id": AUDIO_TOKEN_ID,
+            "bos_token_id": 1,
+            "context_length": 4096,
+            "decoder": {
+                "session_options": session_options,
+                "filename": "dummy_text.onnx",
+                "head_size": HEAD_SIZE,
+                "hidden_size": HIDDEN_SIZE,
+                "inputs": {
+                    "inputs_embeds": "inputs_embeds",
+                    "attention_mask": "attention_mask",
+                    "past_key_names": "past_key_values.%d.key",
+                    "past_value_names": "past_key_values.%d.value",
+                    "past_conv_names": "past.%d.conv",
+                },
+                "outputs": {
+                    "logits": "logits",
+                    "present_key_names": "present.%d.key",
+                    "present_value_names": "present.%d.value",
+                    "present_conv_names": "present.%d.conv",
+                },
+                "num_attention_heads": NUM_KV_HEADS,
+                "num_hidden_layers": len(LAYER_TYPES),
+                "num_key_value_heads": NUM_KV_HEADS,
+                "layer_types": LAYER_TYPES,
+                "conv_cache_size": CONV_CACHE_SIZE,
+            },
+            "eos_token_id": 7,
+            "pad_token_id": 0,
+            "type": "lfm2_audio",
+            "vocab_size": VOCAB_SIZE,
+            "embedding": {
+                "session_options": session_options,
+                "filename": "dummy_embedding.onnx",
+                "inputs": {"input_ids": "input_ids", "audio_features": "audio_features"},
+                "outputs": {"inputs_embeds": "inputs_embeds"},
+            },
+            "speech": {
+                "session_options": session_options,
+                "filename": "dummy_speech.onnx",
+                "inputs": {
+                    "audio_embeds": "mel_spectrogram",
+                    "audio_lengths": "mel_lengths",
+                    "audio_sizes": "audio_sizes",
+                },
+                "outputs": {"audio_features": "audio_embeddings"},
+            },
+        },
+        "search": {
+            "diversity_penalty": 0.0,
+            "do_sample": False,
+            "early_stopping": True,
+            "length_penalty": 1.0,
+            "max_length": 4096,
+            "min_length": 0,
+            "no_repeat_ngram_size": 0,
+            "num_beams": 1,
+            "num_return_sequences": 1,
+            "past_present_share_buffer": False,
+            "repetition_penalty": 1.0,
+            "temperature": 1.0,
+            "top_k": 1,
+            "top_p": 1.0,
+        },
+    }
+    with open(output_path, "w") as f:
+        json.dump(config, f, indent=4)
+        f.write("\n")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate tiny ONNX models for LFM2-Audio pipeline testing")
+    parser.add_argument("--output", type=str, default="test/models/lfm2-audio", help="Output directory")
+    args = parser.parse_args()
+
+    os.makedirs(args.output, exist_ok=True)
+    rng = np.random.default_rng(42)
+    create_dummy_speech_model(os.path.join(args.output, "dummy_speech.onnx"), rng)
+    create_dummy_embedding_model(os.path.join(args.output, "dummy_embedding.onnx"), rng)
+    create_dummy_decoder_model(os.path.join(args.output, "dummy_text.onnx"), rng)
+    create_genai_config(os.path.join(args.output, "genai_config.json"))
+    print(f"Wrote dummy LFM2-Audio models to {args.output}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
new file mode 100644
index 0000000000..e7d63d34a8
--- /dev/null
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -0,0 +1,542 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License
+
+"""
+Tests for the LFM2-Audio pipeline against the tiny models in test/models/lfm2-audio.
+
+The models are generated by test/python/create/create_dummy_lfm2_audio_models.py, and the tokenizer
+is checked in zipped alongside them, so `_model_path` assembles a runnable model directory in a
+temporary directory once per session. The speech model
+emits one feature per 8 mel frames, the embedding model scatters those features over the placeholders
+the processor writes, and the decoder's logits depend on the conv state, so generation only succeeds,
+and only matches between runs, when the processor, the speech/embedding hand-off and the conv state
+cache agree.
+
+Run with:
+    python -m pytest test/python/models/test_lfm2_audio_models.py --test_models test/models
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import os
+import shutil
+import tempfile
+import wave
+import zipfile
+from pathlib import Path
+
+import numpy as np
+import onnxruntime_genai as og
+import pytest
+
+AUDIO_MARKER = "<|audio|>"
+AUDIO_TOKEN_ID = 133  # <|reserved_123|>, model.audio_token_id in the fixture's genai_config.json
+NUM_MELS = 128
+SAMPLE_RATE = 16000
+FFT_SIZE = 512
+WIN_LENGTH = 400
+HOP_LENGTH = 160
+SUBSAMPLING_FACTOR = 8
+LOG_EPS = 2.0**-24
+NORM_EPS = 1e-5
+# Settings of LiquidAI/LFM2-Audio-1.5B and LFM2.5-Audio-1.5B (config.json "preprocessor").
+PREPROCESSOR_CONFIG = {
+    "sample_rate": SAMPLE_RATE,
+    "normalize": "per_feature",
+    "window_size": 0.025,
+    "window_stride": 0.01,
+    "window": "hann",
+    "features": NUM_MELS,
+    "n_fft": FFT_SIZE,
+    "log": True,
+    "frame_splicing": 1,
+    "dither": 1.0e-05,
+    "pad_to": 0,
+    "pad_value": 0.0,
+}
+
+
+# The tokenizer is checked in zipped: its 4.8 MB of vocabulary compresses to under 1 MB, and no test
+# reads it as text. Refresh it with, from the model directory,
+#   python -c "import zipfile; \
+#              [zipfile.ZipFile('tokenizer.zip', 'w').writestr(n, open(n, 'rb').read()) for n in NAMES]"
+# or take a fresh copy from LiquidAI/LFM2.5-Audio-1.5B.
+TOKENIZER_ARCHIVE = "tokenizer.zip"
+TOKENIZER_FILES = ("tokenizer.json", "tokenizer_config.json")
+
+# One assembled directory per source, kept alive (and so undeleted) for the whole session.
+_MODEL_DIRECTORIES: dict[str, tempfile.TemporaryDirectory] = {}
+
+
+def _model_path(test_data_path) -> str:
+    """The fixture model directory, with the tokenizer unpacked, assembled once per session.
+
+    Everything the runtime loads has to sit in one directory, so the ONNX models and the config are
+    copied next to the unpacked tokenizer rather than the archive being unpacked over the checkout.
+    """
+    source = Path(test_data_path) / "lfm2-audio"
+    if not (source / "genai_config.json").exists():
+        pytest.skip(f"LFM2-Audio test model not found at {source}")
+
+    key = os.fspath(source)
+    if key not in _MODEL_DIRECTORIES:
+        holder = tempfile.TemporaryDirectory(prefix="lfm2-audio-")
+        destination = Path(holder.name)
+        for item in source.iterdir():
+            if item.is_file() and item.name != TOKENIZER_ARCHIVE:
+                shutil.copy(item, destination)
+        with zipfile.ZipFile(source / TOKENIZER_ARCHIVE) as archive:
+            # Named rather than extractall: the archive holds exactly these two files, and naming
+            # them keeps a stray path in the archive from landing outside the directory.
+            for name in TOKENIZER_FILES:
+                (destination / name).write_bytes(archive.read(name))
+        _MODEL_DIRECTORIES[key] = holder
+    return _MODEL_DIRECTORIES[key].name
+
+
+def _copy_model(test_data_path, tmp_path) -> Path:
+    destination = tmp_path / "lfm2-audio"
+    shutil.copytree(_model_path(test_data_path), destination)
+    return destination
+
+
+def _edit_json(path: Path, edit):
+    config = json.loads(path.read_text())
+    edit(config)
+    path.write_text(json.dumps(config))
+
+
+def _synthetic_signal(seconds: float, seed: int = 0) -> np.ndarray:
+    """A few tones plus noise, quantized to 16-bit like the WAV that carries it."""
+    rng = np.random.default_rng(seed)
+    t = np.arange(int(seconds * SAMPLE_RATE)) / SAMPLE_RATE
+    signal = 0.3 * np.sin(2 * np.pi * 440 * t) + 0.1 * np.sin(2 * np.pi * 3000 * t) + 0.05 * rng.standard_normal(t.size)
+    return np.round(np.clip(signal, -1, 1) * 32767).astype(np.int16).astype(np.float32) / 32768.0
+
+
+def _write_wav(path: Path, samples: np.ndarray, sample_rate: int = SAMPLE_RATE, channels: int = 1) -> str:
+    pcm = np.round(samples * 32768.0).clip(-32768, 32767).astype("<i2")
+    if channels > 1:
+        pcm = np.repeat(pcm[:, None], channels, axis=1)
+    with wave.open(os.fspath(path), "wb") as handle:
+        handle.setnchannels(channels)
+        handle.setsampwidth(2)
+        handle.setframerate(sample_rate)
+        handle.writeframes(pcm.tobytes())
+    return os.fspath(path)
+
+
+def _num_frames(num_samples: int) -> int:
+    # torch.stft(center=True): one frame per hop plus one.
+    return num_samples // HOP_LENGTH + 1
+
+
+def _num_tokens(num_samples: int) -> int:
+    return math.ceil(_num_frames(num_samples) / SUBSAMPLING_FACTOR)
+
+
+def _slaney_mel_filterbank() -> np.ndarray:
+    """librosa.filters.mel(sr, n_fft, n_mels, fmin=0, fmax=sr/2, norm="slaney") without librosa."""
+
+    def hz_to_mel(hz):
+        hz = np.asarray(hz, dtype=np.float64)
+        mel = hz / (200.0 / 3)
+        log_region = hz >= 1000.0
+        mel[log_region] = 15.0 + np.log(hz[log_region] / 1000.0) / (np.log(6.4) / 27.0)
+        return mel
+
+    def mel_to_hz(mel):
+        mel = np.asarray(mel, dtype=np.float64)
+        hz = mel * (200.0 / 3)
+        log_region = mel >= 15.0
+        hz[log_region] = 1000.0 * np.exp((np.log(6.4) / 27.0) * (mel[log_region] - 15.0))
+        return hz
+
+    fft_freqs = np.linspace(0, SAMPLE_RATE / 2, FFT_SIZE // 2 + 1)
+    mel_points = mel_to_hz(np.linspace(hz_to_mel([0.0])[0], hz_to_mel([SAMPLE_RATE / 2])[0], NUM_MELS + 2))
+    fdiff = np.diff(mel_points)
+    ramps = mel_points[:, None] - fft_freqs[None, :]
+    weights = np.maximum(0, np.minimum(-ramps[:-2] / fdiff[:-1, None], ramps[2:] / fdiff[1:, None]))
+    weights *= (2.0 / (mel_points[2 : NUM_MELS + 2] - mel_points[:NUM_MELS]))[:, None]
+    return weights.astype(np.float32)
+
+
+def _reference_mel(samples: np.ndarray) -> np.ndarray:
+    """NeMo's AudioToMelSpectrogramPreprocessor in eval mode, as liquid-audio ships it, in numpy.
+
+    Pre-emphasis, constant center padding, symmetric Hann window, power spectrum, Slaney mel filter
+    bank, log with the 2^-24 guard, then per-feature normalization over the num_samples // hop
+    frames get_seq_len counts as valid, with the frames past that zeroed.
+    """
+    preemphasized = np.concatenate([samples[:1], samples[1:] - 0.97 * samples[:-1]])
+    padded = np.pad(preemphasized, FFT_SIZE // 2)
+    num_frames = _num_frames(len(samples))
+    window = np.zeros(FFT_SIZE, np.float64)
+    window[(FFT_SIZE - WIN_LENGTH) // 2 :][:WIN_LENGTH] = 0.5 * (
+        1 - np.cos(2 * np.pi * np.arange(WIN_LENGTH) / (WIN_LENGTH - 1))
+    )
+    frames = np.stack([padded[t * HOP_LENGTH : t * HOP_LENGTH + FFT_SIZE] * window for t in range(num_frames)])
+    power = np.abs(np.fft.rfft(frames, axis=1)) ** 2
+    log_mel = np.log(power @ _slaney_mel_filterbank().T.astype(np.float64) + LOG_EPS)
+
+    valid = len(samples) // HOP_LENGTH
+    mean = log_mel[:valid].mean(axis=0)
+    std = np.sqrt(((log_mel[:valid] - mean) ** 2).sum(axis=0) / (valid - 1)) + NORM_EPS
+    normalized = (log_mel - mean) / std
+    normalized[valid:] = 0.0
+    return normalized.astype(np.float32)
+
+
+def _process(model_path: str, prompt: str, audios):
+    model = og.Model(model_path)
+    inputs = model.create_multimodal_processor()(prompt, audios=audios)
+    return model, inputs
+
+
+def _placeholder_runs(input_ids: np.ndarray) -> list[int]:
+    """Lengths of the consecutive AUDIO_TOKEN_ID runs, in order."""
+    runs = []
+    run = 0
+    for token in input_ids:
+        if token == AUDIO_TOKEN_ID:
+            run += 1
+        elif run:
+            runs.append(run)
+            run = 0
+    if run:
+        runs.append(run)
+    return runs
+
+
+def test_lfm2_audio_processor_single_clip(test_data_path, tmp_path):
+    samples = _synthetic_signal(1.3)
+    clip = _write_wav(tmp_path / "clip.wav", samples)
+    prompt = f"<|startoftext|><|im_start|>user\n{AUDIO_MARKER}<|im_end|>\n<|im_start|>assistant\n"
+    model, inputs = _process(_model_path(test_data_path), prompt, og.Audios.open(clip))
+    tokenizer = og.Tokenizer(model)
+
+    # Same token stream as liquid-audio's ChatState: the text on either side of the clip is tokenized
+    # on its own and one placeholder per encoder frame sits between.
+    num_frames = _num_frames(len(samples))
+    expected_ids = (
+        tokenizer.encode("<|startoftext|><|im_start|>user\n").tolist()
+        + [AUDIO_TOKEN_ID] * _num_tokens(len(samples))
+        + tokenizer.encode("<|im_end|>\n<|im_start|>assistant\n").tolist()
+    )
+    input_ids = inputs["input_ids"].as_numpy()
+    assert input_ids.shape == (1, len(expected_ids))
+    assert input_ids[0].tolist() == expected_ids
+
+    mel = inputs["audio_embeds"].as_numpy()
+    assert mel.shape == (1, num_frames, NUM_MELS)
+    assert mel.dtype == np.float32
+    np.testing.assert_array_equal(inputs["audio_lengths"].as_numpy(), [num_frames])
+    np.testing.assert_array_equal(inputs["audio_sizes"].as_numpy(), [_num_tokens(len(samples))])
+
+
+def test_lfm2_audio_processor_text_only(test_data_path):
+    _, inputs = _process(_model_path(test_data_path), "<|startoftext|>Just text.", None)
+    assert AUDIO_TOKEN_ID not in inputs["input_ids"].as_numpy()
+    np.testing.assert_array_equal(inputs["audio_sizes"].as_numpy(), [0])
+    with pytest.raises(RuntimeError, match="audio_embeds"):
+        inputs["audio_embeds"]
+
+
+def test_lfm2_audio_processor_two_clips_of_different_lengths(test_data_path, tmp_path):
+    short = _synthetic_signal(0.5, seed=1)
+    long = _synthetic_signal(1.7, seed=2)
+    clips = [_write_wav(tmp_path / "short.wav", short), _write_wav(tmp_path / "long.wav", long)]
+    prompt = f"First {AUDIO_MARKER} then {AUDIO_MARKER} done"
+    _, inputs = _process(_model_path(test_data_path), prompt, og.Audios.open(*clips))
+
+    # Clips are zero-padded into one batch for a single encoder run, each with its real length, in
+    # prompt order; the placeholders follow the same order.
+    assert _placeholder_runs(inputs["input_ids"].as_numpy()[0]) == [_num_tokens(len(short)), _num_tokens(len(long))]
+    mel = inputs["audio_embeds"].as_numpy()
+    assert mel.shape == (2, _num_frames(len(long)), NUM_MELS)
+    np.testing.assert_array_equal(inputs["audio_lengths"].as_numpy(), [_num_frames(len(short)), _num_frames(len(long))])
+    np.testing.assert_array_equal(inputs["audio_sizes"].as_numpy(), [_num_tokens(len(short)), _num_tokens(len(long))])
+    assert np.all(mel[0, _num_frames(len(short)) :] == 0)
+    np.testing.assert_allclose(mel[0, : _num_frames(len(short))], _reference_mel(short), atol=2e-3)
+    np.testing.assert_allclose(mel[1], _reference_mel(long), atol=2e-3)
+
+
+@pytest.mark.parametrize(
+    "prompt,num_clips,expected",
+    [
+        ("No marker here", 1, "contains 0 <|audio|> markers but 1 audio clips"),
+        (f"{AUDIO_MARKER} twice {AUDIO_MARKER}", 1, "contains 2 <|audio|> markers but 1 audio clips"),
+        (f"marker without a clip {AUDIO_MARKER}", 0, "contains 1 <|audio|> markers but 0 audio clips"),
+    ],
+)
+def test_lfm2_audio_rejects_marker_count_mismatch(test_data_path, tmp_path, prompt, num_clips, expected):
+    clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(0.4, seed=i)) for i in range(num_clips)]
+    audios = og.Audios.open(*clips) if clips else None
+    with pytest.raises(RuntimeError, match=expected):
+        _process(_model_path(test_data_path), prompt, audios)
+
+
+def test_lfm2_audio_rejects_a_clip_too_short_to_normalize(test_data_path, tmp_path):
+    # Per-feature normalization needs two valid frames (num_samples // hop >= 2).
+    clip = _write_wav(tmp_path / "blip.wav", _synthetic_signal(0.015))
+    with pytest.raises(RuntimeError, match="too short"):
+        _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
+
+
+def test_lfm2_audio_single_prompt_list_matches_the_string_form(test_data_path, tmp_path):
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.6))
+    model = og.Model(_model_path(test_data_path))
+    processor = model.create_multimodal_processor()
+    prompt = f"Transcribe {AUDIO_MARKER}"
+    from_string = processor(prompt, audios=og.Audios.open(clip))
+    from_list = processor([prompt], audios=og.Audios.open(clip))
+    np.testing.assert_array_equal(from_list["input_ids"].as_numpy(), from_string["input_ids"].as_numpy())
+    np.testing.assert_array_equal(from_list["audio_embeds"].as_numpy(), from_string["audio_embeds"].as_numpy())
+
+
+def test_lfm2_audio_rejects_batched_prompts(test_data_path):
+    model = og.Model(_model_path(test_data_path))
+    with pytest.raises(RuntimeError, match="batched prompts are not supported"):
+        model.create_multimodal_processor()(["one", "two"], audios=None)
+
+
+def test_lfm2_audio_mel_matches_reference(test_data_path, tmp_path):
+    samples = _synthetic_signal(2.0, seed=3)
+    clip = _write_wav(tmp_path / "clip.wav", samples)
+    _, inputs = _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
+    mel = inputs["audio_embeds"].as_numpy()[0]
+
+    reference = _reference_mel(samples)
+    assert mel.shape == reference.shape
+    np.testing.assert_allclose(mel, reference, atol=2e-3)
+    # NeMo counts one frame fewer than the STFT produces; that last frame is zeroed but still fed.
+    assert np.all(mel[-1] == 0)
+    assert np.any(mel[-2] != 0)
+
+
+def test_lfm2_audio_downmixes_stereo_to_mono(test_data_path, tmp_path):
+    samples = _synthetic_signal(0.8, seed=4)
+    mono = _write_wav(tmp_path / "mono.wav", samples)
+    stereo = _write_wav(tmp_path / "stereo.wav", samples, channels=2)
+    model_path = _model_path(test_data_path)
+    _, from_mono = _process(model_path, AUDIO_MARKER, og.Audios.open(mono))
+    _, from_stereo = _process(model_path, AUDIO_MARKER, og.Audios.open(stereo))
+    np.testing.assert_allclose(from_stereo["audio_embeds"].as_numpy(), from_mono["audio_embeds"].as_numpy(), atol=1e-4)
+
+
+def test_lfm2_audio_mel_matches_liquid_audio(test_data_path, tmp_path):
+    """The reference implementation itself, when the liquid-audio package is installed."""
+    torch = pytest.importorskip("torch")
+    processor_module = pytest.importorskip("liquid_audio.model.conformer.processor")
+    reference_processor = processor_module.AudioToMelSpectrogramPreprocessor(**PREPROCESSOR_CONFIG).eval()
+
+    samples = _synthetic_signal(1.5, seed=5)
+    clip = _write_wav(tmp_path / "clip.wav", samples)
+    _, inputs = _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
+    mel = inputs["audio_embeds"].as_numpy()[0]
+
+    with torch.no_grad():
+        wave_tensor = torch.from_numpy(samples)[None, :]
+        reference, _ = reference_processor(wave_tensor, torch.tensor([wave_tensor.shape[1]]))
+    np.testing.assert_allclose(mel, reference[0].T.numpy(), atol=2e-3)
+
+
+def test_lfm2_audio_casts_mel_to_the_speech_input_type(test_data_path, tmp_path):
+    onnx = pytest.importorskip("onnx")
+    model_path = _copy_model(test_data_path, tmp_path)
+    speech_path = model_path / "dummy_speech.onnx"
+    speech = onnx.load(speech_path)
+    # Take mel_spectrogram as float16 and cast it back to float32 for the rest of the graph.
+    speech.graph.input[0].type.tensor_type.elem_type = onnx.TensorProto.FLOAT16
+    for node in speech.graph.node:
+        node.input[:] = ["mel_spectrogram_fp16" if name == "mel_spectrogram" else name for name in node.input]
+    speech.graph.node.insert(
+        0, onnx.helper.make_node("Cast", ["mel_spectrogram"], ["mel_spectrogram_fp16"], to=onnx.TensorProto.FLOAT)
+    )
+    onnx.save(speech, speech_path)
+
+    samples = _synthetic_signal(0.7, seed=6)
+    clip = _write_wav(tmp_path / "clip.wav", samples)
+    _, inputs = _process(os.fspath(model_path), AUDIO_MARKER, og.Audios.open(clip))
+    mel = inputs["audio_embeds"].as_numpy()
+    assert mel.dtype == np.float16
+    np.testing.assert_allclose(mel[0].astype(np.float32), _reference_mel(samples), atol=2e-2)
+
+    # And the fp16 features still flow through generation.
+    generator, _ = _generate(os.fspath(model_path), AUDIO_MARKER, og.Audios.open(clip), num_tokens=2)
+    assert len(generator.get_sequence(0)) == mel.shape[1] // SUBSAMPLING_FACTOR + 1 + 2
+
+
+@pytest.mark.parametrize("field", ["audio_embeds", "audio_lengths"])
+def test_lfm2_audio_rejects_speech_input_name_missing_from_the_graph(test_data_path, tmp_path, field):
+    model_path = _copy_model(test_data_path, tmp_path)
+    _edit_json(
+        model_path / "genai_config.json", lambda config: config["model"]["speech"]["inputs"].update({field: "nope"})
+    )
+    with pytest.raises(RuntimeError, match=f'no input named "nope".*model.speech.inputs.{field}'):
+        og.Model(os.fspath(model_path)).create_multimodal_processor()
+
+
+def test_lfm2_audio_rejects_non_int64_lengths(test_data_path, tmp_path):
+    onnx = pytest.importorskip("onnx")
+    model_path = _copy_model(test_data_path, tmp_path)
+    speech_path = model_path / "dummy_speech.onnx"
+    speech = onnx.load(speech_path)
+    # Take mel_lengths as int32 and widen it for the rest of the graph.
+    speech.graph.input[1].type.tensor_type.elem_type = onnx.TensorProto.INT32
+    for node in speech.graph.node:
+        node.input[:] = ["mel_lengths_i64" if name == "mel_lengths" else name for name in node.input]
+    speech.graph.node.insert(
+        0, onnx.helper.make_node("Cast", ["mel_lengths"], ["mel_lengths_i64"], to=onnx.TensorProto.INT64)
+    )
+    onnx.save(speech, speech_path)
+    with pytest.raises(RuntimeError, match='"mel_lengths" must be int64, got int32'):
+        og.Model(os.fspath(model_path)).create_multimodal_processor()
+
+
+def test_lfm2_audio_requires_audio_token_id(test_data_path, tmp_path):
+    model_path = _copy_model(test_data_path, tmp_path)
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"].pop("audio_token_id"))
+    with pytest.raises(RuntimeError, match="audio_token_id must be set"):
+        og.Model(os.fspath(model_path)).create_multimodal_processor()
+
+
+def _generate(model_path: str, prompt: str, audios, num_tokens: int, chunk_size: int | None = None):
+    model = og.Model(model_path)
+    processor = model.create_multimodal_processor()
+    params = og.GeneratorParams(model)
+    options = {"do_sample": False, "max_length": 4096}
+    if chunk_size is not None:
+        options["chunk_size"] = chunk_size
+    params.set_search_options(**options)
+
+    inputs = processor(prompt, audios=audios)
+    generator = og.Generator(model, params)
+    generator.set_inputs(inputs)
+    for _ in range(num_tokens):
+        generator.generate_next_token()
+    return generator, inputs
+
+
+def _reference_generation(model_path: str, inputs, num_tokens: int) -> np.ndarray:
+    """Greedy generation replayed with plain ONNX Runtime.
+
+    Runs the speech model once per clip on that clip's own frames — the padding never reaches the
+    encoder, which is what the runtime does and what the published encoder export requires — then
+    scatters the concatenated features through the embedding model, and finally one decoder run per
+    token carrying the KV cache and conv state by hand.
+    """
+    ort = pytest.importorskip("onnxruntime")
+    config = json.loads((Path(model_path) / "genai_config.json").read_text())["model"]
+    decoder_config = config["decoder"]
+    speech = ort.InferenceSession(os.path.join(model_path, config["speech"]["filename"]))
+    embedding = ort.InferenceSession(os.path.join(model_path, config["embedding"]["filename"]))
+    decoder = ort.InferenceSession(os.path.join(model_path, decoder_config["filename"]))
+    output_names = [output.name for output in decoder.get_outputs()]
+    hidden_size = decoder_config["hidden_size"]
+
+    input_ids = inputs["input_ids"].as_numpy()[0]
+    tokens_per_clip = inputs["audio_sizes"].as_numpy()
+    if tokens_per_clip.sum() > 0:
+        mel = inputs["audio_embeds"].as_numpy()
+        frames_per_clip = inputs["audio_lengths"].as_numpy()
+        per_clip = [
+            speech.run(None, {"mel_spectrogram": mel[i : i + 1, :frames], "mel_lengths": frames_per_clip[i : i + 1]})[
+                0
+            ][0]
+            for i, frames in enumerate(frames_per_clip)
+        ]
+        audio_features = np.concatenate(
+            [features[:count] for features, count in zip(per_clip, tokens_per_clip, strict=True)]
+        )
+    else:
+        audio_features = np.zeros((0, hidden_size), np.float32)
+
+    state = {}
+    for layer, layer_type in enumerate(decoder_config["layer_types"]):
+        if layer_type == "conv":
+            state[f"past.{layer}.conv"] = np.zeros((1, hidden_size, decoder_config["conv_cache_size"]), np.float32)
+        else:
+            for kind in ("key", "value"):
+                state[f"past_key_values.{layer}.{kind}"] = np.zeros(
+                    (1, decoder_config["num_key_value_heads"], 0, decoder_config["head_size"]), np.float32
+                )
+
+    sequence = [int(token) for token in input_ids]
+    step_ids = sequence
+    for _ in range(num_tokens):
+        embeds = embedding.run(
+            None, {"input_ids": np.array([step_ids], dtype=np.int64), "audio_features": audio_features}
+        )[0]
+        feeds = {"inputs_embeds": embeds, "attention_mask": np.ones((1, len(sequence)), np.int64), **state}
+        outputs = dict(zip(output_names, decoder.run(None, feeds), strict=True))
+        for name in state:
+            state[name] = outputs[name.replace("past_key_values.", "present.").replace("past.", "present.")]
+        next_token = int(outputs["logits"][0, -1].argmax())
+        sequence.append(next_token)
+        step_ids = [next_token]
+        audio_features = np.zeros((0, hidden_size), np.float32)
+    return np.array(sequence, dtype=np.int32)
+
+
+@pytest.mark.parametrize(
+    "num_clips,chunk_size",
+    [(0, None), (0, 3), (1, None), (1, 3), (1, 10), (2, None), (2, 5)],
+    ids=["text", "text-chunk3", "audio", "audio-chunk3", "audio-chunk10", "two-clips", "two-clips-chunk5"],
+)
+def test_lfm2_audio_generation_matches_onnxruntime_reference(test_data_path, tmp_path, num_clips, chunk_size):
+    # The decoder's logits read the conv state over the last few embeddings, so the prompt ends with
+    # the audio: the first generated tokens depend on the scattered features, and the runtime only
+    # matches the hand-carried reference if it passes both the features and the conv state along,
+    # including across prefill chunk boundaries. The clips run longest first, so the last one is the
+    # one the mel batch pads: feeding the encoder that padding instead of the clip's own frames
+    # changes its final feature, and with it the first token generated.
+    model_path = _model_path(test_data_path)
+    clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(1.7 - 0.7 * i, seed=10 + i)) for i in range(num_clips)]
+    prompt = "<|startoftext|>Transcribe the following. " + " and ".join([AUDIO_MARKER] * num_clips)
+    num_tokens = 6
+
+    generator, inputs = _generate(model_path, prompt, og.Audios.open(*clips) if clips else None, num_tokens, chunk_size)
+    input_ids = inputs["input_ids"].as_numpy()[0]
+    assert chunk_size is None or len(input_ids) > chunk_size, "the prompt must span several chunks"
+
+    np.testing.assert_array_equal(generator.get_sequence(0), _reference_generation(model_path, inputs, num_tokens))
+
+
+def test_lfm2_audio_generation_depends_on_the_audio(test_data_path, tmp_path):
+    model_path = _model_path(test_data_path)
+    outputs = []
+    for seed in (20, 21):
+        clip = _write_wav(tmp_path / f"{seed}.wav", _synthetic_signal(0.5, seed=seed))
+        generator, inputs = _generate(model_path, f"<|startoftext|>{AUDIO_MARKER}", og.Audios.open(clip), num_tokens=4)
+        outputs.append(generator.get_sequence(0)[len(inputs["input_ids"].as_numpy()[0]) :].tolist())
+    assert outputs[0] != outputs[1], "two different clips must not decode to the same tokens"
+
+
+def test_lfm2_audio_features_follow_the_clip_order(test_data_path, tmp_path):
+    # Two clips of the same length, so a swapped concatenation would still have the right shape.
+    model_path = _model_path(test_data_path)
+    clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(1.0, seed=30 + i)) for i in range(2)]
+    prompt = "<|startoftext|>Compare {} and {}"
+
+    outputs = []
+    for order in (clips, clips[::-1]):
+        generator, inputs = _generate(
+            model_path, prompt.format(AUDIO_MARKER, AUDIO_MARKER), og.Audios.open(*order), num_tokens=4
+        )
+        start = len(inputs["input_ids"].as_numpy()[0])
+        outputs.append(generator.get_sequence(0)[start:].tolist())
+    assert outputs[0] != outputs[1], "swapping the clips must change what the decoder sees"
+
+
+def test_lfm2_audio_rejects_rewind(test_data_path, tmp_path):
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5))
+    generator, _ = _generate(_model_path(test_data_path), f"{AUDIO_MARKER}Hi", og.Audios.open(clip), num_tokens=1)
+    sequence = generator.get_sequence(0).copy()
+
+    # The conv state cannot be rewound; the generator must refuse before touching the sequence.
+    with pytest.raises(RuntimeError, match="RewindTo is currently not supported for lfm2_audio"):
+        generator.rewind_to(0)
+    np.testing.assert_array_equal(generator.get_sequence(0), sequence)
diff --git a/test/python/test_onnxruntime_genai_api.py b/test/python/test_onnxruntime_genai_api.py
index e2577ddec5..4f0f851f78 100644
--- a/test/python/test_onnxruntime_genai_api.py
+++ b/test/python/test_onnxruntime_genai_api.py
@@ -979,6 +979,26 @@ def test_whisper_preprocessing_multiple_audios(test_data_path, relative_model_pa
     _ = processor(prompts, audios=audios)
 
 
+@pytest.mark.parametrize("opener", ["images", "audios"])
+def test_open_keeps_short_paths_alive(test_data_path, tmp_path, opener):
+    # The paths are copied into a vector first and only then turned into char pointers: a short
+    # string keeps its characters inside the string object, so taking the pointer as the vector
+    # grows leaves the earlier entries pointing at moved-from memory.
+    source = Path(test_data_path) / ("images/cars.jpg" if opener == "images" else "audios/jfk.flac")
+    suffix = source.suffix
+    short_dir = tmp_path / "s"
+    short_dir.mkdir()
+    paths = []
+    for index in range(4):
+        path = short_dir / f"{index}{suffix}"
+        shutil.copy(source, path)
+        paths.append(os.fspath(path))
+
+    opened = (og.Images if opener == "images" else og.Audios).open(*paths)
+
+    assert opened is not None
+
+
 def test_streaming_asr_create(nemotron_speech_model_path):
     """Test that Generator + StreamingProcessor can be created from a nemotron_speech model."""
     model = og.Model(nemotron_speech_model_path)

From c81cbf2fb22ee43807a1456f1cad6c8740a1ab8f Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 18:50:00 -0400
Subject: [PATCH 02/23] fix: address review on LFM2-Audio support, and widen
 its tests

Review fixes:
- Apply the LoRA adapter named by `adapter_path` when loading an
  LFM2-Audio decoder. The override returned before the base loader's
  adapter step, so the adapter was dropped without a word. The step is
  now a method the base loader and this one share.
- Move the nested-config reader onto the model class and drop the
  module-level architecture name, per the model-builder rules.
- Correct the comments and the tutorial: the clips of one prompt are
  staged in one padded tensor, but the encoder runs once per clip, on
  that clip's own frames. Nothing says they share a run any more.
- Bind the subscript CodeQL read as a statement with no effect.

Wider tests, to match what the vision model has:
- A text-only audio export runs through the LFM2 runtime, alongside the
  vision and MoE cases already covered there.
- Frame and token accounting as a standalone arithmetic file, checked
  against the reference implementation when it is installed, mirroring
  the vision token tests.
- Opening a clip from bytes matches opening it from a path.
- Clips above the encoder's rate are resampled down to it.
- Every documented front-end override is honoured, including the one
  that changes how many placeholders a clip is worth.
- A clip recorded below the encoder's rate is refused with a message
  naming the clip and saying to resample it first: the decoder only
  resamples downwards, which nothing said before.
---
 examples/python/lfm2-audio-tutorial.md        |  28 +--
 .../preprocessing/lfm2_audio_processor.cpp    |  19 +-
 src/python/py/models/builder.py               |   3 +-
 src/python/py/models/builders/__init__.py     |   3 +-
 src/python/py/models/builders/base.py         |   7 +
 src/python/py/models/builders/lfm2.py         |  49 ++---
 test/cpp/lfm2_cache_layer_types_test.cpp      |   7 +
 test/python/builder/test_lfm2_audio.py        |  48 ++++-
 test/python/builder/test_qmoe_weights.py      |   4 +-
 test/python/models/test_lfm2_audio_frames.py  | 182 ++++++++++++++++++
 test/python/models/test_lfm2_audio_models.py  |  87 ++++++++-
 11 files changed, 382 insertions(+), 55 deletions(-)
 create mode 100644 test/python/models/test_lfm2_audio_frames.py

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index edfbc5dbfc..d9f7bf5ba4 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -62,20 +62,25 @@ runs through the normal `AppendTokens` path with no speech or embedding model.
 The speech encoder is `onnx/audio_encoder.onnx` in
 [LiquidAI/LFM2.5-Audio-1.5B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-ONNX)
 (`audio_encoder_fp16.onnx` and `audio_encoder_q4.onnx` are there too). Download the graph together
-with every `*.onnx_data*` file next to it. It has the signature ONNX Runtime GenAI expects:
+with every `*.onnx_data*` file next to it. It has the signature ONNX Runtime GenAI expects, for a
+single clip at a time:
 
 | Name | Shape | Type |
 | --- | --- | --- |
-| `mel_spectrogram` (input) | `[num_clips, num_frames, 128]` | float |
-| `mel_lengths` (input) | `[num_clips]` | int64 |
-| `audio_embeddings` (output) | `[num_clips, ceil(num_frames / 8), hidden_size]` | float |
-| `audio_lengths` (output) | `[num_clips]` | int64 |
+| `mel_spectrogram` (input) | `[1, num_frames, 128]` | float |
+| `mel_lengths` (input) | `[1]` | int64 |
+| `audio_embeddings` (output) | `[1, ceil(num_frames / 8), hidden_size]` | float |
+| `audio_lengths` (output) | `[1]` | int64 |
 
 The encoder subsamples the mel frames by 8 (three stride-2 convolutions) and its adapter projects the
-result to the decoder's hidden size, so one output frame covers 80 ms of audio. The `audio_lengths`
-output is not read by the runtime — the runtime derives the same counts itself so it can size the
-prompt before the encoder runs — but the graph produces it and the input it shares with
-`mel_lengths` matters: the encoder masks the padding of a batched run with it.
+result to the decoder's hidden size, so one output frame covers 80 ms of audio. `mel_lengths` gives
+it the clip's real length; the runtime always passes the clip's own frames, so nothing is padded.
+The `audio_lengths` output is not read — the runtime derives the same counts itself, before the
+encoder runs, so that it can size the prompt — but the graph produces it.
+
+The published export only handles one clip per run: its subsampling mask is traced for a batch of
+one, and a wider `mel_spectrogram` fails inside the graph. The runtime therefore runs it once per
+clip. An encoder exported to take a real batch would work here too, one clip at a time.
 
 The embedding model looks up `input_ids` in the decoder's embedding table and scatters
 `audio_features` into the positions holding the audio placeholder token. Build it from
@@ -249,8 +254,9 @@ runtime has nothing to do with. LiquidAI ships `vocoder_depthformer.onnx` and
 `audio_detokenizer.onnx` in the ONNX repository, and their
 [onnx-export](https://github.com/Liquid4All/onnx-export) repository drives them from Python.
 
-**One prompt at a time.** Several clips in one prompt work and share a single encoder run, but
-batched prompts are refused.
+**One prompt at a time.** Several clips in one prompt work, but batched prompts are refused. The
+clips do not share an encoder run: the published encoder export is traced for a single clip, so each
+one is encoded on its own frames and the results are concatenated in prompt order.
 
 **Audio is not chunked.** The whole clip goes through the encoder in one run, and one hour of audio
 is 450k mel frames, so memory grows with the clip length. The reference implementation has the same
diff --git a/src/models/preprocessing/lfm2_audio_processor.cpp b/src/models/preprocessing/lfm2_audio_processor.cpp
index c1f5fa9439..bd59c524eb 100644
--- a/src/models/preprocessing/lfm2_audio_processor.cpp
+++ b/src/models/preprocessing/lfm2_audio_processor.cpp
@@ -244,8 +244,18 @@ std::unique_ptr<NamedTensors> Lfm2AudioProcessor::Process(const Tokenizer& token
   int64_t total_tokens = 0;
   for (size_t i = 0; i < num_clips; ++i) {
     ort_extensions::OrtxObjectPtr<OrtxTensorResult> decoded;
-    CheckResult(OrtxDecodeAudio(audios->audios_.get(), i, static_cast<int64_t>(mel_config_.sample_rate),
-                                /*stereo_to_mono=*/1, decoded.ToBeAssigned()));
+    // The decoder resamples down to the encoder's rate and mixes to mono, but it cannot upsample:
+    // say which clip it was and what to do about it, rather than passing its bare message through.
+    try {
+      CheckResult(OrtxDecodeAudio(audios->audios_.get(), i, static_cast<int64_t>(mel_config_.sample_rate),
+                                  /*stereo_to_mono=*/1, decoded.ToBeAssigned()));
+    } catch (const std::exception& e) {
+      throw std::runtime_error("Lfm2AudioProcessor: could not decode audio clip " + std::to_string(i) + " at " +
+                               std::to_string(mel_config_.sample_rate) +
+                               " Hz. A clip recorded below that rate has to be resampled up to it first, as the "
+                               "decoder only resamples downwards. The decoder reported: " +
+                               e.what());
+    }
     ort_extensions::OrtxObjectPtr<OrtxTensor> pcm_tensor;
     CheckResult(OrtxTensorResultGetAt(decoded.get(), 0, pcm_tensor.ToBeAssigned()));
 
@@ -293,8 +303,9 @@ std::unique_ptr<NamedTensors> Lfm2AudioProcessor::Process(const Tokenizer& token
     return named_tensors;
   }
 
-  // Clips share one encoder run as a zero-padded [num_clips, longest, num_mels] batch with their
-  // real lengths alongside, the same batching the reference applies before its encoder.
+  // The clips are staged in one zero-padded [num_clips, longest, num_mels] tensor with their real
+  // lengths alongside. It is a container, not a batched encoder run: Lfm2AudioSpeechState slices
+  // each clip's own frames back out and runs the encoder on them one clip at a time.
   const int64_t num_mels = mel_config_.num_mels;
   auto batch = OrtValue::CreateTensor<float>(allocator, std::vector<int64_t>{static_cast<int64_t>(num_clips), longest, num_mels});
   float* batch_data = batch->GetTensorMutableData<float>();
diff --git a/src/python/py/models/builder.py b/src/python/py/models/builder.py
index 5e23ad669f..6b2bc0c95d 100644
--- a/src/python/py/models/builder.py
+++ b/src/python/py/models/builder.py
@@ -31,7 +31,6 @@
     LFM2AudioModel,
     LFM2Model,
     LFM2MoEModel,
-    load_lfm2_audio_config,
     LlamaModel,
     Mistral3TextModel,
     MistralModel,
@@ -104,7 +103,7 @@ def get_hf_details(model_name, input_path, cache_dir, extra_options):
     hf_remote = extra_options.get("hf_remote", False)
 
     # LFM2-Audio checkpoints have no model_type, so AutoConfig cannot read them: use their nested LFM2 decoder config.
-    config = load_lfm2_audio_config(hf_name, token=hf_token, **extra_kwargs)
+    config = LFM2AudioModel.load_config(hf_name, token=hf_token, **extra_kwargs)
     if config is None:
         config = AutoConfig.from_pretrained(hf_name, token=hf_token, trust_remote_code=hf_remote, **extra_kwargs)
     tokenizer = AutoTokenizer.from_pretrained(hf_name, token=hf_token, trust_remote_code=hf_remote, **extra_kwargs)
diff --git a/src/python/py/models/builders/__init__.py b/src/python/py/models/builders/__init__.py
index 90190f7e3a..b5bf8df3a6 100644
--- a/src/python/py/models/builders/__init__.py
+++ b/src/python/py/models/builders/__init__.py
@@ -14,7 +14,7 @@
 from .granite import GraniteModel, GraniteMoEHybridModel
 from .hunyuan import HunyuanDenseV1Model
 from .internlm import InternLM2Model
-from .lfm2 import LFM2AudioModel, LFM2Model, LFM2MoEModel, load_lfm2_audio_config
+from .lfm2 import LFM2AudioModel, LFM2Model, LFM2MoEModel
 from .llama import LlamaModel
 from .mistral import Mistral3TextModel, MistralModel
 from .mtp import MTPModel
@@ -60,7 +60,6 @@
     "LFM2AudioModel",
     "LFM2Model",
     "LFM2MoEModel",
-    "load_lfm2_audio_config",
     "LlamaModel",
     "MTPModel",
     "Mistral3TextModel",
diff --git a/src/python/py/models/builders/base.py b/src/python/py/models/builders/base.py
index 9763ca12c6..26515544df 100644
--- a/src/python/py/models/builders/base.py
+++ b/src/python/py/models/builders/base.py
@@ -5958,6 +5958,13 @@ def load_weights(self, input_path):
                 **extra_kwargs,
             )
 
+        return self.load_adapter(model)
+
+    def load_adapter(self, model):
+        """Wraps a loaded PyTorch model with the LoRA adapter named by `adapter_path`, if any.
+
+        Every `load_weights` override has to end with this, or the adapter is silently dropped.
+        """
         if "adapter_path" in self.extra_options:
             from peft import PeftModel
 
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index 1683bb7b38..cb4a8aaa5d 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -12,8 +12,6 @@
 
 from .base import Model
 
-LFM2_AUDIO_ARCHITECTURE = "Lfm2AudioForConditionalGeneration"
-
 
 class LFM2Model(Model):
     def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
@@ -196,27 +194,6 @@ def update_genai_config(self, genai_config):
         decoder["conv_cache_size"] = self.conv_L_cache - 1
 
 
-def load_lfm2_audio_config(model_name_or_path, **kwargs):
-    """The LFM2 decoder config of an LFM2-Audio checkpoint (LFM2-Audio-1.5B, LFM2.5-Audio-1.5B).
-
-    These checkpoints have no transformers model class and their config.json nests the decoder
-    config under "lfm" next to the audio encoder, depthformer and mel front-end settings, so
-    AutoConfig cannot read them. Returns None if the checkpoint is not an LFM2-Audio one.
-    """
-    from transformers import Lfm2Config, PretrainedConfig
-
-    try:
-        config_dict, _ = PretrainedConfig.get_config_dict(model_name_or_path, **kwargs)
-    except Exception:  # noqa: BLE001 - not an LFM2-Audio checkpoint (or not readable): AutoConfig reports the real error
-        return None
-    if config_dict.get("architectures") != [LFM2_AUDIO_ARCHITECTURE]:
-        return None
-    config = Lfm2Config(**config_dict["lfm"])
-    config.architectures = [LFM2_AUDIO_ARCHITECTURE]
-    config._name_or_path = model_name_or_path
-    return config
-
-
 class LFM2AudioModel(LFM2Model):
     """The LFM2 decoder of an LFM2-Audio checkpoint, exported for text generation.
 
@@ -227,6 +204,30 @@ class LFM2AudioModel(LFM2Model):
     embeddings; otherwise it is a plain text model.
     """
 
+    @classmethod
+    def load_config(cls, model_name_or_path, **kwargs):
+        """The LFM2 decoder config of an LFM2-Audio checkpoint, or None if it is not one.
+
+        These checkpoints have no model type and no transformers model class, and their config.json
+        nests the decoder config under "lfm" next to the audio encoder, depthformer and mel front-end
+        settings, so AutoConfig cannot read them. The builder calls this before it knows which model
+        class to use, which is why it cannot be an instance method.
+        """
+        from transformers import Lfm2Config, PretrainedConfig
+
+        architecture = "Lfm2AudioForConditionalGeneration"
+        try:
+            config_dict, _ = PretrainedConfig.get_config_dict(model_name_or_path, **kwargs)
+        except Exception:  # noqa: BLE001 - not an LFM2-Audio checkpoint, or unreadable: AutoConfig reports the real error
+            return None
+        if config_dict.get("architectures") != [architecture]:
+            return None
+
+        config = Lfm2Config(**config_dict["lfm"])
+        config.architectures = [architecture]
+        config._name_or_path = model_name_or_path
+        return config
+
     def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
         super().__init__(config, io_dtype, onnx_dtype, ep, cache_dir, extra_options)
         self.decoder_config = config
@@ -249,7 +250,7 @@ def load_weights(self, input_path):
                 f"The LFM2 decoder of {input_path} does not match its config: missing {sorted(missing)}, unexpected {sorted(unexpected)}."
             )
         model.tie_weights()
-        return model.eval()
+        return self.load_adapter(model.eval())
 
     def load_lfm2_audio_checkpoint(self, input_path):
         """The checkpoint tensors, from a local directory or the Hugging Face Hub."""
diff --git a/test/cpp/lfm2_cache_layer_types_test.cpp b/test/cpp/lfm2_cache_layer_types_test.cpp
index fb146c6cb4..2491471147 100644
--- a/test/cpp/lfm2_cache_layer_types_test.cpp
+++ b/test/cpp/lfm2_cache_layer_types_test.cpp
@@ -183,3 +183,10 @@ TEST(LFM2CacheLayerTypesValidationTest, Lfm2VlTextTypeUsesLfm2Runtime) {
   SkipIfModelUnavailable();
   ExpectLfm2Runtime("lfm2_vl_text", "vl_text_type");
 }
+
+// An LFM2-Audio checkpoint exported without exclude_embeds is labeled "lfm2_audio_text": a plain
+// LFM2 decoder rather than a stage of the speech pipeline.
+TEST(LFM2CacheLayerTypesValidationTest, Lfm2AudioTextTypeUsesLfm2Runtime) {
+  SkipIfModelUnavailable();
+  ExpectLfm2Runtime("lfm2_audio_text", "audio_text_type");
+}
diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
index b27b40b056..a9c1d115c9 100644
--- a/test/python/builder/test_lfm2_audio.py
+++ b/test/python/builder/test_lfm2_audio.py
@@ -13,6 +13,7 @@
 from __future__ import annotations
 
 import json
+import sys
 import types
 
 import pytest
@@ -84,10 +85,10 @@ def _write_checkpoint(path, decoder_weights=None):
     return decoder_weights
 
 
-def test_load_lfm2_audio_config_reads_the_nested_decoder_config(tmp_path):
+def test_lfm2_audio_load_config_reads_the_nested_decoder_config(tmp_path):
     _write_checkpoint(tmp_path)
 
-    config = lfm2_module.load_lfm2_audio_config(str(tmp_path))
+    config = LFM2AudioModel.load_config(str(tmp_path))
 
     assert config.architectures == ["Lfm2AudioForConditionalGeneration"]
     assert config._name_or_path == str(tmp_path)
@@ -97,18 +98,19 @@ def test_load_lfm2_audio_config_reads_the_nested_decoder_config(tmp_path):
     assert config.tie_word_embeddings
 
 
-def test_load_lfm2_audio_config_leaves_other_checkpoints_to_autoconfig(tmp_path):
+def test_lfm2_audio_load_config_leaves_other_checkpoints_to_autoconfig(tmp_path):
     (tmp_path / "config.json").write_text(json.dumps(DECODER_CONFIG))
-    assert lfm2_module.load_lfm2_audio_config(str(tmp_path)) is None
-    assert lfm2_module.load_lfm2_audio_config(str(tmp_path / "missing")) is None
+    assert LFM2AudioModel.load_config(str(tmp_path)) is None
+    assert LFM2AudioModel.load_config(str(tmp_path / "missing")) is None
 
 
-def _audio_builder(tmp_path):
+def _audio_builder(tmp_path, extra_options=None):
     model = LFM2AudioModel.__new__(LFM2AudioModel)
-    model.decoder_config = lfm2_module.load_lfm2_audio_config(str(tmp_path))
+    model.decoder_config = LFM2AudioModel.load_config(str(tmp_path))
     model.quant_type = None
     model.cache_dir = str(tmp_path / "cache")
     model.hf_token = True
+    model.extra_options = extra_options or {}
     return model
 
 
@@ -144,6 +146,38 @@ def test_lfm2_audio_load_weights_rejects_a_checkpoint_missing_decoder_tensors(tm
         _audio_builder(tmp_path).load_weights(str(tmp_path))
 
 
+def test_lfm2_audio_load_weights_applies_the_lora_adapter(monkeypatch, tmp_path):
+    # `load_weights` is overridden for these checkpoints, so it has to apply `adapter_path` itself;
+    # returning the bare decoder would drop the adapter without a word.
+    _write_checkpoint(tmp_path)
+    wrapped = []
+
+    class FakePeftModel:
+        @classmethod
+        def from_pretrained(cls, model, adapter_path, **kwargs):
+            wrapped.append((model, adapter_path, kwargs))
+            return "adapted"
+
+    peft = types.ModuleType("peft")
+    peft.PeftModel = FakePeftModel
+    monkeypatch.setitem(sys.modules, "peft", peft)
+
+    builder = _audio_builder(tmp_path, {"adapter_path": "some/adapter"})
+    loaded = builder.load_weights(str(tmp_path))
+
+    assert loaded == "adapted"
+    model, adapter_path, kwargs = wrapped[0]
+    assert type(model).__name__ == "Lfm2ForCausalLM"
+    assert adapter_path == "some/adapter"
+    assert kwargs == {"cache_dir": builder.cache_dir, "token": True}
+
+
+def test_lfm2_audio_load_weights_without_an_adapter_returns_the_decoder(tmp_path):
+    _write_checkpoint(tmp_path)
+    loaded = _audio_builder(tmp_path).load_weights(str(tmp_path))
+    assert type(loaded).__name__ == "Lfm2ForCausalLM"
+
+
 @pytest.mark.parametrize("exclude_embeds,expected_type", [(True, "lfm2_audio"), (False, "lfm2_audio_text")])
 def test_lfm2_audio_architecture_labels_the_export(monkeypatch, tmp_path, exclude_embeds, expected_type):
     captured = {}
diff --git a/test/python/builder/test_qmoe_weights.py b/test/python/builder/test_qmoe_weights.py
index d77a36f6f6..2f0866fc9b 100644
--- a/test/python/builder/test_qmoe_weights.py
+++ b/test/python/builder/test_qmoe_weights.py
@@ -159,8 +159,8 @@ def _load_builder_cli_module(monkeypatch):
         "WhisperModel",
     ):
         setattr(builders_module, class_name, type(class_name, (), {}))
-    # Not a model class: the LFM2-Audio config reader builder.py calls before the dispatch.
-    builders_module.load_lfm2_audio_config = lambda *args, **kwargs: None
+    # builder.py calls this before the dispatch, to spot an LFM2-Audio checkpoint.
+    builders_module.LFM2AudioModel.load_config = lambda *args, **kwargs: None
     # Submodule imports (e.g. `from quantization import ...`) must resolve to the
     # real, dependency-free modules rather than the class stubs above.
     builders_module.__path__ = [str(BUILDERS_DIR)]
diff --git a/test/python/models/test_lfm2_audio_frames.py b/test/python/models/test_lfm2_audio_frames.py
new file mode 100644
index 0000000000..b9673c0781
--- /dev/null
+++ b/test/python/models/test_lfm2_audio_frames.py
@@ -0,0 +1,182 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation.  All rights reserved.
+# Licensed under the MIT License.  See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+"""
+LFM2-Audio frame and token accounting tests.
+
+Three pieces have to agree on how many decoder tokens a clip is worth, or the encoder output and the
+audio placeholders in the prompt will not line up:
+
+  * NeMo's `AudioToMelSpectrogramPreprocessor`, which the reference implementation wraps: its STFT
+    decides how many mel frames a clip has, and its `get_seq_len` decides how many of those count as
+    valid for the per-feature normalization,
+  * the FastConformer encoder, whose three stride-2 convolutions subsample those frames by 8,
+  * `Lfm2AudioProcessor` in C++, which writes one placeholder per encoder frame.
+
+These tests are pure arithmetic reimplementations of all three, so they run without a model or a
+built runtime. The real C++ processor is exercised against the tiny test model in
+test_lfm2_audio_models.py, and `test_matches_liquid_audio` below checks this arithmetic against the
+reference implementation itself whenever it is installed.
+
+Run with:
+    python -m pytest test/python/models/test_lfm2_audio_frames.py -v
+"""
+
+import math
+
+import pytest
+
+# Defaults from LiquidAI/LFM2.5-Audio-1.5B's config.json ("preprocessor" and "encoder"), shared
+# byte for byte by LFM2-Audio-1.5B and LFM2.5-Audio-1.5B-JP.
+SAMPLE_RATE = 16000
+WINDOW_SIZE = 0.025  # seconds
+WINDOW_STRIDE = 0.01  # seconds
+WIN_LENGTH = int(WINDOW_SIZE * SAMPLE_RATE)  # 400
+HOP_LENGTH = int(WINDOW_STRIDE * SAMPLE_RATE)  # 160
+FFT_SIZE = 512
+NUM_MELS = 128
+# Three stride-2 depthwise convolutions in the encoder's pre-encode stage.
+SUBSAMPLING_FACTOR = 8
+# The shortest clip the per-feature normalization can handle: its standard deviation divides by
+# (valid frames - 1), so one valid frame is not enough.
+MIN_VALID_FRAMES = 2
+
+
+def num_mel_frames(num_samples: int) -> int:
+    """Frames out of `torch.stft(..., center=True)`: one per hop, plus one for the centre padding."""
+    return num_samples // HOP_LENGTH + 1
+
+
+def num_valid_mel_frames(num_samples: int) -> int:
+    """NeMo's `get_seq_len`: the frames the per-feature normalization averages over.
+
+    One fewer than the STFT produces. The extra frame is still handed to the encoder and still
+    counts towards the decoder tokens; it is just zeroed instead of normalized.
+    """
+    return num_samples // HOP_LENGTH
+
+
+def num_audio_tokens(num_frames: int) -> int:
+    """Decoder tokens for a clip: one per encoder frame, the tail frame rounded up."""
+    return math.ceil(num_frames / SUBSAMPLING_FACTOR)
+
+
+def seconds_to_tokens(seconds: float) -> int:
+    return num_audio_tokens(num_mel_frames(int(seconds * SAMPLE_RATE)))
+
+
+class TestMelFrames:
+    @pytest.mark.parametrize(
+        "num_samples,frames,valid",
+        [
+            (0, 1, 0),  # centre padding alone still yields a frame
+            (1, 1, 0),
+            (159, 1, 0),
+            (160, 2, 1),  # exactly one hop
+            (16000, 101, 100),  # one second
+            (16159, 101, 100),  # a partial hop is truncated
+            (16160, 102, 101),
+        ],
+    )
+    def test_known_lengths(self, num_samples, frames, valid):
+        assert num_mel_frames(num_samples) == frames
+        assert num_valid_mel_frames(num_samples) == valid
+
+    @pytest.mark.parametrize("num_samples", [0, 1, 160, 4000, 16000, 44100, 160000])
+    def test_valid_frames_are_one_short_of_the_stft(self, num_samples):
+        # This off-by-one is the whole reason the two counts exist separately: the last frame is
+        # zeroed by the normalization but still reaches the encoder.
+        assert num_valid_mel_frames(num_samples) == num_mel_frames(num_samples) - 1
+
+    def test_one_second_is_one_hundred_hops(self):
+        assert num_valid_mel_frames(SAMPLE_RATE) == int(1.0 / WINDOW_STRIDE)
+
+    @pytest.mark.parametrize("num_samples", [320, 1000, 16000, 100000])
+    def test_frame_count_is_monotonic_in_the_clip_length(self, num_samples):
+        assert num_mel_frames(num_samples) <= num_mel_frames(num_samples + 1)
+        assert num_mel_frames(num_samples) < num_mel_frames(num_samples + HOP_LENGTH)
+
+    def test_shortest_normalizable_clip(self):
+        # Two valid frames need two whole hops; anything shorter the processor rejects.
+        shortest = MIN_VALID_FRAMES * HOP_LENGTH
+        assert num_valid_mel_frames(shortest) == MIN_VALID_FRAMES
+        assert num_valid_mel_frames(shortest - 1) < MIN_VALID_FRAMES
+
+    def test_window_fits_inside_the_fft(self):
+        # A window longer than the FFT size cannot be centred in it; the processor refuses that.
+        assert WIN_LENGTH <= FFT_SIZE
+        assert (FFT_SIZE - WIN_LENGTH) % 2 == 0, "the window is centred, so the padding has to split evenly"
+
+
+class TestAudioTokens:
+    @pytest.mark.parametrize(
+        "frames,tokens",
+        [(0, 0), (1, 1), (8, 1), (9, 2), (16, 2), (101, 13), (126, 16), (1001, 126)],
+    )
+    def test_known_frame_counts(self, frames, tokens):
+        assert num_audio_tokens(frames) == tokens
+
+    @pytest.mark.parametrize("frames", [1, 7, 8, 9, 63, 64, 65, 1000])
+    def test_tail_frames_round_up(self, frames):
+        # Rounding down would drop the tail of every clip whose length is not a multiple of 8, and
+        # the placeholders would then outnumber the features.
+        assert num_audio_tokens(frames) == -(-frames // SUBSAMPLING_FACTOR)
+        assert num_audio_tokens(frames) * SUBSAMPLING_FACTOR >= frames
+
+    @pytest.mark.parametrize("seconds", [0.1, 0.5, 1.0, 2.5, 10.0, 60.0])
+    def test_one_token_per_eighty_milliseconds(self, seconds):
+        # 8 frames x 10 ms: the figure the tutorial quotes for prompt budgeting.
+        assert seconds_to_tokens(seconds) == pytest.approx(seconds / 0.08, abs=1)
+
+    def test_one_minute_of_audio(self):
+        assert seconds_to_tokens(60.0) == 751
+
+
+class TestPlaceholders:
+    @pytest.mark.parametrize("clips", [[1.0], [0.5, 1.7], [0.2, 0.2, 0.2]])
+    def test_placeholders_match_the_encoder_output(self, clips):
+        # The processor writes this many placeholders; the speech state concatenates exactly this
+        # many feature rows. A mismatch is what the scatter in the embedding model would trip over.
+        per_clip = [seconds_to_tokens(seconds) for seconds in clips]
+        assert sum(per_clip) == sum(num_audio_tokens(num_mel_frames(int(s * SAMPLE_RATE))) for s in clips)
+        assert all(count > 0 for count in per_clip)
+
+    def test_padding_a_batch_does_not_change_any_clip(self):
+        # Clips are staged in one padded tensor, but each is encoded on its own frames, so a clip's
+        # token count never depends on what it is batched with.
+        short, long = 0.5, 1.7
+        assert seconds_to_tokens(short) == num_audio_tokens(num_mel_frames(int(short * SAMPLE_RATE)))
+        padded_frames = num_mel_frames(int(long * SAMPLE_RATE))
+        assert num_audio_tokens(padded_frames) != seconds_to_tokens(short), "the test lengths must differ"
+
+
+@pytest.mark.parametrize("seconds", [0.25, 1.0, 2.5])
+def test_matches_liquid_audio(seconds):
+    """The reference implementation itself, when the liquid-audio package is installed."""
+    torch = pytest.importorskip("torch")
+    processor_module = pytest.importorskip("liquid_audio.model.conformer.processor")
+
+    preprocessor = processor_module.AudioToMelSpectrogramPreprocessor(
+        sample_rate=SAMPLE_RATE,
+        normalize="per_feature",
+        window_size=WINDOW_SIZE,
+        window_stride=WINDOW_STRIDE,
+        window="hann",
+        features=NUM_MELS,
+        n_fft=FFT_SIZE,
+        log=True,
+        frame_splicing=1,
+        dither=1.0e-05,
+        pad_to=0,
+        pad_value=0.0,
+    ).eval()
+
+    num_samples = int(seconds * SAMPLE_RATE)
+    with torch.no_grad():
+        mel, lengths = preprocessor(torch.zeros(1, num_samples), torch.tensor([num_samples]))
+
+    assert mel.shape[1] == NUM_MELS
+    assert mel.shape[2] == num_mel_frames(num_samples)
+    assert int(lengths[0]) == num_valid_mel_frames(num_samples)
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index e7d63d34a8..4b57a4e84c 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -241,7 +241,7 @@ def test_lfm2_audio_processor_text_only(test_data_path):
     assert AUDIO_TOKEN_ID not in inputs["input_ids"].as_numpy()
     np.testing.assert_array_equal(inputs["audio_sizes"].as_numpy(), [0])
     with pytest.raises(RuntimeError, match="audio_embeds"):
-        inputs["audio_embeds"]
+        _ = inputs["audio_embeds"]
 
 
 def test_lfm2_audio_processor_two_clips_of_different_lengths(test_data_path, tmp_path):
@@ -251,8 +251,8 @@ def test_lfm2_audio_processor_two_clips_of_different_lengths(test_data_path, tmp
     prompt = f"First {AUDIO_MARKER} then {AUDIO_MARKER} done"
     _, inputs = _process(_model_path(test_data_path), prompt, og.Audios.open(*clips))
 
-    # Clips are zero-padded into one batch for a single encoder run, each with its real length, in
-    # prompt order; the placeholders follow the same order.
+    # Clips are staged as one zero-padded tensor, each with its real length, in prompt order; the
+    # placeholders follow the same order. The encoder itself runs once per clip on those lengths.
     assert _placeholder_runs(inputs["input_ids"].as_numpy()[0]) == [_num_tokens(len(short)), _num_tokens(len(long))]
     mel = inputs["audio_embeds"].as_numpy()
     assert mel.shape == (2, _num_frames(len(long)), NUM_MELS)
@@ -278,6 +278,87 @@ def test_lfm2_audio_rejects_marker_count_mismatch(test_data_path, tmp_path, prom
         _process(_model_path(test_data_path), prompt, audios)
 
 
+def test_lfm2_audio_open_bytes_matches_open(test_data_path, tmp_path):
+    clip = Path(_write_wav(tmp_path / "clip.wav", _synthetic_signal(0.9, seed=7)))
+    model = og.Model(_model_path(test_data_path))
+    processor = model.create_multimodal_processor()
+
+    from_path = processor(AUDIO_MARKER, audios=og.Audios.open(os.fspath(clip)))
+    from_bytes = processor(AUDIO_MARKER, audios=og.Audios.open_bytes(clip.read_bytes()))
+
+    np.testing.assert_array_equal(from_bytes["input_ids"].as_numpy(), from_path["input_ids"].as_numpy())
+    np.testing.assert_array_equal(from_bytes["audio_embeds"].as_numpy(), from_path["audio_embeds"].as_numpy())
+    np.testing.assert_array_equal(from_bytes["audio_sizes"].as_numpy(), from_path["audio_sizes"].as_numpy())
+
+
+@pytest.mark.parametrize("source_rate", [22050, 44100, 48000])
+def test_lfm2_audio_resamples_to_the_encoder_rate(test_data_path, tmp_path, source_rate):
+    # Whatever the file's rate, the clip reaches the mel front end at the encoder's 16 kHz, so the
+    # frame count follows the resampled length rather than the original sample count.
+    seconds = 0.75
+    samples = _synthetic_signal(seconds, seed=8)[: int(seconds * source_rate)]
+    if source_rate > SAMPLE_RATE:  # _synthetic_signal only makes 16 kHz worth of samples
+        samples = np.interp(
+            np.linspace(0, 1, int(seconds * source_rate)), np.linspace(0, 1, samples.size), samples
+        ).astype(np.float32)
+    clip = _write_wav(tmp_path / "clip.wav", samples, sample_rate=source_rate)
+
+    _, inputs = _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
+
+    resampled_length = round(samples.size * SAMPLE_RATE / source_rate)
+    num_frames = int(inputs["audio_lengths"].as_numpy()[0])
+    # The resampler may land a sample either side of the exact ratio, so allow one hop of slack.
+    assert abs(num_frames - _num_frames(resampled_length)) <= 1, f"{num_frames} frames for {resampled_length} samples"
+    assert inputs["audio_embeds"].as_numpy().shape == (1, num_frames, NUM_MELS)
+    assert int(inputs["audio_sizes"].as_numpy()[0]) == math.ceil(num_frames / SUBSAMPLING_FACTOR)
+
+
+def test_lfm2_audio_rejects_a_clip_recorded_below_the_encoder_rate(test_data_path, tmp_path):
+    # The decoder resamples downwards only, so 8 kHz telephone audio cannot be read as 16 kHz. The
+    # message has to name the clip and say what to do, not just repeat the decoder's complaint.
+    clip = _write_wav(tmp_path / "telephone.wav", _synthetic_signal(0.5, seed=11)[:4000], sample_rate=8000)
+    with pytest.raises(RuntimeError, match="could not decode audio clip 0 at 16000 Hz.*resampled up"):
+        _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
+
+
+@pytest.mark.parametrize(
+    "field,value",
+    [("hop_length", 320), ("win_length", 320), ("fft_size", 1024), ("num_mels", 64), ("sample_rate", 8000)],
+)
+def test_lfm2_audio_honors_the_mel_config_overrides(test_data_path, tmp_path, field, value):
+    # The front end defaults to the published checkpoints' settings, and genai_config.json can
+    # override each one; a fine-tune that changed them would otherwise be silently mis-framed.
+    model_path = _copy_model(test_data_path, tmp_path)
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({field: value}))
+    samples = _synthetic_signal(1.0, seed=9)
+    clip = _write_wav(tmp_path / "clip.wav", samples)
+
+    _, inputs = _process(os.fspath(model_path), AUDIO_MARKER, og.Audios.open(clip))
+    mel = inputs["audio_embeds"].as_numpy()
+
+    expected_mels = value if field == "num_mels" else NUM_MELS
+    expected_hop = value if field == "hop_length" else HOP_LENGTH
+    # A different sample rate resamples the clip instead of changing the framing arithmetic.
+    expected_samples = len(samples) * value // SAMPLE_RATE if field == "sample_rate" else len(samples)
+    expected_frames = expected_samples // expected_hop + 1
+
+    assert mel.shape[2] == expected_mels
+    assert abs(mel.shape[1] - expected_frames) <= 1, f"{mel.shape[1]} frames, expected about {expected_frames}"
+    np.testing.assert_array_equal(inputs["audio_lengths"].as_numpy(), [mel.shape[1]])
+
+
+def test_lfm2_audio_subsampling_factor_override_changes_the_placeholder_count(test_data_path, tmp_path):
+    model_path = _copy_model(test_data_path, tmp_path)
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({"subsampling_factor": 4}))
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(1.0, seed=10))
+
+    _, inputs = _process(os.fspath(model_path), AUDIO_MARKER, og.Audios.open(clip))
+
+    num_frames = int(inputs["audio_lengths"].as_numpy()[0])
+    assert int(inputs["audio_sizes"].as_numpy()[0]) == math.ceil(num_frames / 4)
+    assert _placeholder_runs(inputs["input_ids"].as_numpy()[0]) == [math.ceil(num_frames / 4)]
+
+
 def test_lfm2_audio_rejects_a_clip_too_short_to_normalize(test_data_path, tmp_path):
     # Per-feature normalization needs two valid frames (num_samples // hop >= 2).
     clip = _write_wav(tmp_path / "blip.wav", _synthetic_signal(0.015))

From b4c1acc106304289aff8a9d36b3ddb62f88b2f22 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 18:55:51 -0400
Subject: [PATCH 03/23] fix: build LFM2-Audio from a model name, not just a
 local path

Passing the checkpoint by name (`-m LiquidAI/LFM2.5-Audio-1.5B`) leaves
the input path empty, and the loader handed that empty string to the Hub
as a repository name. Only `-i <directory>` worked. Fall back to the
name the config was read from, and cover both ways round.

Found by exporting every LFM2 audio checkpoint LiquidAI publishes.
Noted in the tutorial which of them the published encoder belongs to,
since the other two need their own, and that audio below the encoder's
rate has to be resampled before it gets here.
---
 examples/python/lfm2-audio-tutorial.md | 17 ++++++++++-
 src/python/py/models/builders/lfm2.py  |  9 ++++--
 test/python/builder/test_lfm2_audio.py | 40 +++++++++++++++++++++++++-
 3 files changed, 61 insertions(+), 5 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index d9f7bf5ba4..65d1c76ae8 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -44,6 +44,13 @@ $ python3 -m onnxruntime_genai.models.builder \
     --extra_options exclude_embeds=true
 ```
 
+`-m LiquidAI/LFM2.5-Audio-1.5B` instead of `-i` works too, and downloads the checkpoint itself.
+
+The three checkpoints LiquidAI publishes as PyTorch — `LFM2-Audio-1.5B`, `LFM2.5-Audio-1.5B` and
+`LFM2.5-Audio-1.5B-JP` — build the same way. Their `"lfm"`, `"encoder"` and `"preprocessor"` settings
+are identical, so the front-end defaults below fit all three; only the weights differ. The GGUF
+repositories are for llama.cpp and are not inputs to this builder.
+
 The checkpoint has no `model_type` and no transformers model class: its `config.json` nests the LFM2
 decoder config under `"lfm"`, next to the speech encoder, depthformer and mel front-end settings, and
 the checkpoint stores the decoder under the `lfm.` prefix. The builder recognizes
@@ -62,7 +69,11 @@ runs through the normal `AppendTokens` path with no speech or embedding model.
 The speech encoder is `onnx/audio_encoder.onnx` in
 [LiquidAI/LFM2.5-Audio-1.5B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-ONNX)
 (`audio_encoder_fp16.onnx` and `audio_encoder_q4.onnx` are there too). Download the graph together
-with every `*.onnx_data*` file next to it. It has the signature ONNX Runtime GenAI expects, for a
+with every `*.onnx_data*` file next to it. That repository is the export of `LFM2.5-Audio-1.5B`, so
+its encoder belongs to that checkpoint only; `LFM2-Audio-1.5B` and `LFM2.5-Audio-1.5B-JP` have their
+own encoder weights and need their own export, from `conformer.*` and `audio_adapter.*` in the
+checkpoint (`liquid_audio.model.conformer.ConformerEncoder` plus its adapter MLP, traced for one
+clip). It has the signature ONNX Runtime GenAI expects, for a
 single clip at a time:
 
 | Name | Shape | Type |
@@ -258,6 +269,10 @@ runtime has nothing to do with. LiquidAI ships `vocoder_depthformer.onnx` and
 clips do not share an encoder run: the published encoder export is traced for a single clip, so each
 one is encoded on its own frames and the results are concatenated in prompt order.
 
+**Audio below 16 kHz is refused.** The decoder resamples down to the encoder's rate and mixes to
+mono, but it does not resample upwards, so 8 kHz telephone audio has to be resampled before it gets
+here. The processor says so, naming the clip.
+
 **Audio is not chunked.** The whole clip goes through the encoder in one run, and one hour of audio
 is 450k mel frames, so memory grows with the clip length. The reference implementation has the same
 shape; for long-form transcription, split the audio yourself.
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index cb4a8aaa5d..330e2767f2 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -256,13 +256,16 @@ def load_lfm2_audio_checkpoint(self, input_path):
         """The checkpoint tensors, from a local directory or the Hugging Face Hub."""
         from safetensors.torch import load_file
 
-        if os.path.isdir(input_path):
-            checkpoint_dir = input_path
+        # `-i` gives a local directory. With `-m` it is empty and the checkpoint is the one the
+        # config was read from, which is the repository name.
+        source = input_path or self.model_name_or_path
+        if os.path.isdir(source):
+            checkpoint_dir = source
         else:
             from huggingface_hub import snapshot_download
 
             checkpoint_dir = snapshot_download(
-                input_path, cache_dir=self.cache_dir, token=self.hf_token, allow_patterns=["*.safetensors", "*.json"]
+                source, cache_dir=self.cache_dir, token=self.hf_token, allow_patterns=["*.safetensors", "*.json"]
             )
 
         index_path = os.path.join(checkpoint_dir, "model.safetensors.index.json")
diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
index a9c1d115c9..1921542dad 100644
--- a/test/python/builder/test_lfm2_audio.py
+++ b/test/python/builder/test_lfm2_audio.py
@@ -104,13 +104,14 @@ def test_lfm2_audio_load_config_leaves_other_checkpoints_to_autoconfig(tmp_path)
     assert LFM2AudioModel.load_config(str(tmp_path / "missing")) is None
 
 
-def _audio_builder(tmp_path, extra_options=None):
+def _audio_builder(tmp_path, extra_options=None, model_name_or_path=None):
     model = LFM2AudioModel.__new__(LFM2AudioModel)
     model.decoder_config = LFM2AudioModel.load_config(str(tmp_path))
     model.quant_type = None
     model.cache_dir = str(tmp_path / "cache")
     model.hf_token = True
     model.extra_options = extra_options or {}
+    model.model_name_or_path = model_name_or_path or str(tmp_path)
     return model
 
 
@@ -146,6 +147,43 @@ def test_lfm2_audio_load_weights_rejects_a_checkpoint_missing_decoder_tensors(tm
         _audio_builder(tmp_path).load_weights(str(tmp_path))
 
 
+def test_lfm2_audio_load_weights_downloads_the_checkpoint_named_by_the_config(monkeypatch, tmp_path):
+    # `-m <repo id>` leaves the input path empty; the checkpoint to fetch is then the one the config
+    # was read from. Passing the empty path straight to the Hub asks it for a repository called "".
+    _write_checkpoint(tmp_path)
+    asked = []
+
+    def fake_snapshot_download(repo_id, **kwargs):
+        asked.append((repo_id, kwargs))
+        return str(tmp_path)
+
+    hub = types.ModuleType("huggingface_hub")
+    hub.snapshot_download = fake_snapshot_download
+    monkeypatch.setitem(sys.modules, "huggingface_hub", hub)
+
+    builder = _audio_builder(tmp_path, model_name_or_path="LiquidAI/LFM2.5-Audio-1.5B")
+    loaded = builder.load_weights("")
+
+    assert type(loaded).__name__ == "Lfm2ForCausalLM"
+    assert asked[0][0] == "LiquidAI/LFM2.5-Audio-1.5B"
+    assert asked[0][1]["cache_dir"] == builder.cache_dir
+
+
+def test_lfm2_audio_load_weights_prefers_a_local_input_path(monkeypatch, tmp_path):
+    _write_checkpoint(tmp_path)
+
+    def fail(*args, **kwargs):
+        raise AssertionError("a local checkpoint directory must not be downloaded")
+
+    hub = types.ModuleType("huggingface_hub")
+    hub.snapshot_download = fail
+    monkeypatch.setitem(sys.modules, "huggingface_hub", hub)
+
+    loaded = _audio_builder(tmp_path, model_name_or_path="LiquidAI/LFM2.5-Audio-1.5B").load_weights(str(tmp_path))
+
+    assert type(loaded).__name__ == "Lfm2ForCausalLM"
+
+
 def test_lfm2_audio_load_weights_applies_the_lora_adapter(monkeypatch, tmp_path):
     # `load_weights` is overridden for these checkpoints, so it has to apply `adapter_path` itself;
     # returning the bare decoder would drop the adapter without a word.

From 695d1455c0e68c413f7520bda15c344b45fd2a34 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 21:33:02 -0400
Subject: [PATCH 04/23] refactor: split the audio processor and speech state
 into named stages

Both had grown to a hundred lines covering several unrelated jobs.
The processor now decodes a clip, builds the prompt tokens, and stages
the audio tensors in three methods named after those jobs; the speech
state resolves its bindings, runs one clip, and joins the results in
three more. No behaviour changes.

Also covers reading a checkpoint that `save_pretrained` split into
several files, which an fp32 fine-tune of this model would be.
---
 src/models/multi_modal.cpp                    | 138 ++++++++-------
 src/models/multi_modal.h                      |  19 ++
 .../preprocessing/lfm2_audio_processor.cpp    | 162 ++++++++++--------
 .../preprocessing/lfm2_audio_processor.h      |  18 ++
 test/python/builder/test_lfm2_audio.py        |  34 +++-
 5 files changed, 235 insertions(+), 136 deletions(-)

diff --git a/src/models/multi_modal.cpp b/src/models/multi_modal.cpp
index 0fcb6abeed..f26d3e6cb4 100644
--- a/src/models/multi_modal.cpp
+++ b/src/models/multi_modal.cpp
@@ -618,37 +618,25 @@ void Lfm2AudioSpeechState::SetExtraInputs(const std::vector<ExtraInput>& extra_i
   }
 }
 
-DeviceSpan<float> Lfm2AudioSpeechState::Run(int current_length, DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices) {
-  if (model_.config_->model.speech.run_options.has_value()) {
-    State::SetRunOptions(model_.config_->model.speech.run_options.value());
-  }
-  // A single clip fills the whole mel tensor and the whole feature buffer; run it as it stands.
-  if (tokens_per_clip_.size() <= 1) {
-    State::Run(*model_.speech_session_);
-    return {};
-  }
-
-  const auto& mel_name = model_.config_->model.speech.inputs.audio_embeds;
-  const auto& lengths_name = model_.config_->model.speech.inputs.audio_lengths;
-  const size_t mel_index = FindInput(mel_name);
-  const size_t lengths_index = FindInput(lengths_name);
-  const size_t features_index = FindOutput(model_.config_->model.speech.outputs.audio_features);
+Lfm2AudioSpeechState::SpeechBindings Lfm2AudioSpeechState::ResolveBindings() const {
+  SpeechBindings bindings;
+  bindings.mel_index = FindInput(model_.config_->model.speech.inputs.audio_embeds);
+  bindings.lengths_index = FindInput(model_.config_->model.speech.inputs.audio_lengths);
+  bindings.features_index = FindOutput(model_.config_->model.speech.outputs.audio_features);
 
-  OrtValue* mel_batch = inputs_[mel_index];
-  OrtValue* lengths = inputs_[lengths_index];
-  OrtValue* features = outputs_[features_index];
-
-  const auto mel_info = mel_batch->GetTensorTypeAndShapeInfo();
+  const auto mel_info = inputs_[bindings.mel_index]->GetTensorTypeAndShapeInfo();
   const auto mel_shape = mel_info->GetShape();  // [num_clips, longest_clip, num_mels]
   if (mel_shape.size() != 3) {
     throw std::runtime_error("Lfm2AudioSpeechState: expected a 3D [num_clips, num_frames, num_mels] mel tensor, got rank " +
                              std::to_string(mel_shape.size()) + ".");
   }
-  const int64_t num_clips = mel_shape[0];
-  const int64_t longest_clip = mel_shape[1];
-  const int64_t num_mels = mel_shape[2];
-  if (num_clips != static_cast<int64_t>(tokens_per_clip_.size())) {
-    throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(num_clips) +
+  bindings.num_clips = mel_shape[0];
+  bindings.longest_clip = mel_shape[1];
+  bindings.num_mels = mel_shape[2];
+  bindings.mel_type = mel_info->GetElementType();
+
+  if (bindings.num_clips != static_cast<int64_t>(tokens_per_clip_.size())) {
+    throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(bindings.num_clips) +
                              " clips but audio_sizes has " + std::to_string(tokens_per_clip_.size()) + " entries.");
   }
   const int64_t total = std::accumulate(tokens_per_clip_.begin(), tokens_per_clip_.end(), int64_t{0});
@@ -657,17 +645,14 @@ DeviceSpan<float> Lfm2AudioSpeechState::Run(int current_length, DeviceSpan<int32
                              std::to_string(num_audio_tokens_) + " were expected.");
   }
 
-  const auto mel_type = mel_info->GetElementType();
-  const size_t mel_element_size = Ort::SizeOf(mel_type);
-  const auto lengths_info = lengths->GetTensorTypeAndShapeInfo();
-  if (static_cast<int64_t>(lengths_info->GetElementCount()) != num_clips) {
-    throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(num_clips) + " clips but " +
-                             lengths_name + " has " + std::to_string(lengths_info->GetElementCount()) + " entries.");
+  const auto lengths_info = inputs_[bindings.lengths_index]->GetTensorTypeAndShapeInfo();
+  if (static_cast<int64_t>(lengths_info->GetElementCount()) != bindings.num_clips) {
+    throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(bindings.num_clips) +
+                             " clips but " + model_.config_->model.speech.inputs.audio_lengths + " has " +
+                             std::to_string(lengths_info->GetElementCount()) + " entries.");
   }
-  const int64_t* frames_per_clip = lengths->GetTensorData<int64_t>();
 
-  const auto features_info = features->GetTensorTypeAndShapeInfo();
-  const auto features_type = features_info->GetElementType();
+  const auto features_info = outputs_[bindings.features_index]->GetTensorTypeAndShapeInfo();
   const auto features_shape = features_info->GetShape();  // [batch, num_audio_tokens, hidden_size]
   // Several clips are concatenated into one prompt, which the pipeline only ever builds for a single
   // sequence; a wider feature buffer would need the whole run repeated per beam.
@@ -675,49 +660,76 @@ DeviceSpan<float> Lfm2AudioSpeechState::Run(int current_length, DeviceSpan<int32
     throw std::runtime_error("Lfm2AudioSpeechState: several audio clips need a batch size of 1, got " +
                              std::to_string(features_shape[0]) + ".");
   }
-  const int64_t hidden_size = features_shape.back();
-  const size_t feature_row_bytes = static_cast<size_t>(hidden_size) * Ort::SizeOf(features_type);
+  bindings.features_type = features_info->GetElementType();
+  bindings.hidden_size = features_shape.back();
+  return bindings;
+}
+
+std::unique_ptr<OrtValue> Lfm2AudioSpeechState::RunClip(const SpeechBindings& bindings, int64_t index,
+                                                        int64_t num_frames) {
+  const size_t mel_element_size = Ort::SizeOf(bindings.mel_type);
+  const size_t clip_stride = static_cast<size_t>(bindings.longest_clip * bindings.num_mels) * mel_element_size;
+  const auto* mel_data = static_cast<const uint8_t*>(inputs_[bindings.mel_index]->GetTensorRawData());
+
+  auto clip_mel = OrtValue::CreateTensor(Ort::Allocator::GetWithDefaultOptions(),
+                                         std::vector<int64_t>{1, num_frames, bindings.num_mels}, bindings.mel_type);
+  std::memcpy(clip_mel->GetTensorMutableRawData(), mel_data + static_cast<size_t>(index) * clip_stride,
+              static_cast<size_t>(num_frames * bindings.num_mels) * mel_element_size);
+
+  auto clip_length = OrtValue::CreateTensor<int64_t>(Ort::Allocator::GetWithDefaultOptions(), std::vector<int64_t>{1});
+  clip_length->GetTensorMutableData<int64_t>()[0] = num_frames;
+
+  auto clip_features = OrtValue::CreateTensor(
+      model_.p_device_->GetAllocator(),
+      std::vector<int64_t>{1, tokens_per_clip_[static_cast<size_t>(index)], bindings.hidden_size},
+      bindings.features_type);
+
+  OrtValue* mel_batch = inputs_[bindings.mel_index];
+  OrtValue* lengths = inputs_[bindings.lengths_index];
+  OrtValue* features = outputs_[bindings.features_index];
+  inputs_[bindings.mel_index] = clip_mel.get();
+  inputs_[bindings.lengths_index] = clip_length.get();
+  outputs_[bindings.features_index] = clip_features.get();
+  State::Run(*model_.speech_session_);
+  inputs_[bindings.mel_index] = mel_batch;
+  inputs_[bindings.lengths_index] = lengths;
+  outputs_[bindings.features_index] = features;
+
+  return clip_features;
+}
+
+DeviceSpan<float> Lfm2AudioSpeechState::Run(int current_length, DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices) {
+  if (model_.config_->model.speech.run_options.has_value()) {
+    State::SetRunOptions(model_.config_->model.speech.run_options.value());
+  }
+  // A single clip fills the whole mel tensor and the whole feature buffer; run it as it stands.
+  if (tokens_per_clip_.size() <= 1) {
+    State::Run(*model_.speech_session_);
+    return {};
+  }
 
-  auto features_bytes = ByteWrapTensor(*model_.p_device_, *features);
-  const auto* mel_data = static_cast<const uint8_t*>(mel_batch->GetTensorRawData());
-  const size_t clip_stride = static_cast<size_t>(longest_clip * num_mels) * mel_element_size;
+  const SpeechBindings bindings = ResolveBindings();
+  const int64_t* frames_per_clip = inputs_[bindings.lengths_index]->GetTensorData<int64_t>();
+  auto features_bytes = ByteWrapTensor(*model_.p_device_, *outputs_[bindings.features_index]);
+  const size_t feature_row_bytes = static_cast<size_t>(bindings.hidden_size) * Ort::SizeOf(bindings.features_type);
   size_t destination = 0;
 
   // The published encoder export is traced for one clip (its subsampling mask cannot broadcast over
   // a batch), so run it once per clip on that clip's own frames — which also keeps the padding out
   // of the encoder entirely — and concatenate the results in clip order.
-  for (int64_t clip = 0; clip < num_clips; ++clip) {
+  for (int64_t clip = 0; clip < bindings.num_clips; ++clip) {
     const int64_t num_frames = frames_per_clip[clip];
-    if (num_frames <= 0 || num_frames > longest_clip) {
+    if (num_frames <= 0 || num_frames > bindings.longest_clip) {
       throw std::runtime_error("Lfm2AudioSpeechState: clip " + std::to_string(clip) + " reports " +
                                std::to_string(num_frames) + " mel frames, outside the 1.." +
-                               std::to_string(longest_clip) + " the mel tensor holds.");
+                               std::to_string(bindings.longest_clip) + " the mel tensor holds.");
     }
-    const std::vector<int64_t> clip_mel_shape{1, num_frames, num_mels};
-    auto clip_mel = OrtValue::CreateTensor(Ort::Allocator::GetWithDefaultOptions(), clip_mel_shape, mel_type);
-    std::memcpy(clip_mel->GetTensorMutableRawData(), mel_data + static_cast<size_t>(clip) * clip_stride,
-                static_cast<size_t>(num_frames * num_mels) * mel_element_size);
-
-    auto clip_length = OrtValue::CreateTensor<int64_t>(Ort::Allocator::GetWithDefaultOptions(), std::vector<int64_t>{1});
-    clip_length->GetTensorMutableData<int64_t>()[0] = num_frames;
 
-    const int64_t clip_tokens = tokens_per_clip_[static_cast<size_t>(clip)];
-    auto clip_features = OrtValue::CreateTensor(model_.p_device_->GetAllocator(),
-                                                std::vector<int64_t>{1, clip_tokens, hidden_size}, features_type);
-
-    inputs_[mel_index] = clip_mel.get();
-    inputs_[lengths_index] = clip_length.get();
-    outputs_[features_index] = clip_features.get();
-    State::Run(*model_.speech_session_);
-
-    const size_t clip_bytes = static_cast<size_t>(clip_tokens) * feature_row_bytes;
+    auto clip_features = RunClip(bindings, clip, num_frames);
+    const size_t clip_bytes = static_cast<size_t>(tokens_per_clip_[static_cast<size_t>(clip)]) * feature_row_bytes;
     features_bytes.subspan(destination, clip_bytes).CopyFrom(ByteWrapTensor(*model_.p_device_, *clip_features));
     destination += clip_bytes;
   }
-
-  inputs_[mel_index] = mel_batch;
-  inputs_[lengths_index] = lengths;
-  outputs_[features_index] = features;
   return {};
 }
 
diff --git a/src/models/multi_modal.h b/src/models/multi_modal.h
index 5c50dfcb4e..1b6ff60419 100644
--- a/src/models/multi_modal.h
+++ b/src/models/multi_modal.h
@@ -160,6 +160,25 @@ struct Lfm2AudioSpeechState : SpeechState {
   DeviceSpan<float> Run(int current_length, DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices = {}) override;
 
  private:
+  // Where the processor's staged mel tensor and the encoder's feature buffer are bound.
+  struct SpeechBindings {
+    size_t mel_index{};       // the [num_clips, longest, num_mels] staging tensor
+    size_t lengths_index{};   // its per-clip frame counts
+    size_t features_index{};  // the [1, total_tokens, hidden] buffer the embedding model reads
+    int64_t num_clips{};
+    int64_t longest_clip{};
+    int64_t num_mels{};
+    int64_t hidden_size{};
+    ONNXTensorElementDataType mel_type{};
+    ONNXTensorElementDataType features_type{};
+  };
+
+  // Reads the bound shapes and checks them against the per-clip token counts.
+  SpeechBindings ResolveBindings() const;
+
+  // Runs the encoder on clip `index`'s own frames and returns its features.
+  std::unique_ptr<OrtValue> RunClip(const SpeechBindings& bindings, int64_t index, int64_t num_frames);
+
   size_t FindInput(const std::string& name) const;
   size_t FindOutput(const std::string& name) const;
 
diff --git a/src/models/preprocessing/lfm2_audio_processor.cpp b/src/models/preprocessing/lfm2_audio_processor.cpp
index bd59c524eb..e9e00772d5 100644
--- a/src/models/preprocessing/lfm2_audio_processor.cpp
+++ b/src/models/preprocessing/lfm2_audio_processor.cpp
@@ -226,61 +226,47 @@ Lfm2AudioProcessor::Lfm2AudioProcessor(Config& config, const SessionInfo& sessio
   config.AddMapping(std::string(Config::Defaults::AudioSizesName), config.model.speech.inputs.audio_sizes);
 }
 
-std::unique_ptr<NamedTensors> Lfm2AudioProcessor::Process(const Tokenizer& tokenizer, const Payload& payload) const {
-  const std::string prompt = ResolvePrompt(payload);
-  const Audios* audios = payload.audios;
-  const size_t num_clips = audios ? audios->num_audios_ : 0;
-  Ort::Allocator& allocator{Ort::Allocator::GetWithDefaultOptions()};
-  auto named_tensors = std::make_unique<NamedTensors>();
-
-  // With no clips the prompt must not ask for any; the split with zero clips enforces that.
-  const std::vector<std::string> segments = SplitLfm2AudioPrompt(prompt, num_clips);
-
-  // Mel spectrogram per clip, decoded to mono at the encoder's sample rate.
-  std::vector<std::vector<float>> mels;
-  std::vector<int64_t> mel_lengths;
-  std::vector<int64_t> tokens_per_clip;
-  int64_t longest = 0;
-  int64_t total_tokens = 0;
-  for (size_t i = 0; i < num_clips; ++i) {
-    ort_extensions::OrtxObjectPtr<OrtxTensorResult> decoded;
-    // The decoder resamples down to the encoder's rate and mixes to mono, but it cannot upsample:
-    // say which clip it was and what to do about it, rather than passing its bare message through.
-    try {
-      CheckResult(OrtxDecodeAudio(audios->audios_.get(), i, static_cast<int64_t>(mel_config_.sample_rate),
-                                  /*stereo_to_mono=*/1, decoded.ToBeAssigned()));
-    } catch (const std::exception& e) {
-      throw std::runtime_error("Lfm2AudioProcessor: could not decode audio clip " + std::to_string(i) + " at " +
-                               std::to_string(mel_config_.sample_rate) +
-                               " Hz. A clip recorded below that rate has to be resampled up to it first, as the "
-                               "decoder only resamples downwards. The decoder reported: " +
-                               e.what());
-    }
-    ort_extensions::OrtxObjectPtr<OrtxTensor> pcm_tensor;
-    CheckResult(OrtxTensorResultGetAt(decoded.get(), 0, pcm_tensor.ToBeAssigned()));
-
-    const float* pcm{};
-    const int64_t* pcm_shape{};
-    size_t pcm_dims{};
-    CheckResult(OrtxGetTensorData(pcm_tensor.get(), reinterpret_cast<const void**>(&pcm), &pcm_shape, &pcm_dims));
-    int64_t num_samples = 0;
-    if (pcm_dims == 1) {
-      num_samples = pcm_shape[0];
-    } else if (pcm_dims == 2 && pcm_shape[0] == 1) {
-      num_samples = pcm_shape[1];
-    } else {
-      throw std::runtime_error("Lfm2AudioProcessor: expected mono PCM from the audio decoder for clip " +
-                               std::to_string(i) + ", got a rank " + std::to_string(pcm_dims) + " tensor.");
-    }
+Lfm2AudioProcessor::ClipMel Lfm2AudioProcessor::ComputeClipMel(const Audios& audios, size_t index) const {
+  ort_extensions::OrtxObjectPtr<OrtxTensorResult> decoded;
+  // The decoder resamples down to the encoder's rate and mixes to mono, but it cannot upsample:
+  // say which clip it was and what to do about it, rather than passing its bare message through.
+  try {
+    CheckResult(OrtxDecodeAudio(audios.audios_.get(), index, static_cast<int64_t>(mel_config_.sample_rate),
+                                /*stereo_to_mono=*/1, decoded.ToBeAssigned()));
+  } catch (const std::exception& e) {
+    throw std::runtime_error("Lfm2AudioProcessor: could not decode audio clip " + std::to_string(index) + " at " +
+                             std::to_string(mel_config_.sample_rate) +
+                             " Hz. A clip recorded below that rate has to be resampled up to it first, as the "
+                             "decoder only resamples downwards. The decoder reported: " +
+                             e.what());
+  }
 
-    int64_t num_frames = 0;
-    mels.push_back(ComputeLfm2AudioMel(pcm, num_samples, mel_config_, num_frames));
-    mel_lengths.push_back(num_frames);
-    tokens_per_clip.push_back(Lfm2AudioNumTokens(num_frames, subsampling_factor_));
-    longest = std::max(longest, num_frames);
-    total_tokens += tokens_per_clip.back();
+  ort_extensions::OrtxObjectPtr<OrtxTensor> pcm_tensor;
+  CheckResult(OrtxTensorResultGetAt(decoded.get(), 0, pcm_tensor.ToBeAssigned()));
+  const float* pcm{};
+  const int64_t* pcm_shape{};
+  size_t pcm_dims{};
+  CheckResult(OrtxGetTensorData(pcm_tensor.get(), reinterpret_cast<const void**>(&pcm), &pcm_shape, &pcm_dims));
+
+  int64_t num_samples = 0;
+  if (pcm_dims == 1) {
+    num_samples = pcm_shape[0];
+  } else if (pcm_dims == 2 && pcm_shape[0] == 1) {
+    num_samples = pcm_shape[1];
+  } else {
+    throw std::runtime_error("Lfm2AudioProcessor: expected mono PCM from the audio decoder for clip " +
+                             std::to_string(index) + ", got a rank " + std::to_string(pcm_dims) + " tensor.");
   }
 
+  ClipMel clip;
+  clip.mel = ComputeLfm2AudioMel(pcm, num_samples, mel_config_, clip.num_frames);
+  clip.num_tokens = Lfm2AudioNumTokens(clip.num_frames, subsampling_factor_);
+  return clip;
+}
+
+std::vector<int32_t> Lfm2AudioProcessor::MakePromptTokens(const Tokenizer& tokenizer,
+                                                          const std::vector<std::string>& segments,
+                                                          const std::vector<ClipMel>& clips) const {
   // Text segments are tokenized on their own, as the reference ChatState does, and each clip
   // contributes one placeholder per encoder frame between them.
   std::vector<int32_t> input_ids;
@@ -289,42 +275,76 @@ std::unique_ptr<NamedTensors> Lfm2AudioProcessor::Process(const Tokenizer& token
       const std::vector<int32_t> ids = tokenizer.Encode(segments[i].c_str());
       input_ids.insert(input_ids.end(), ids.begin(), ids.end());
     }
-    if (i < tokens_per_clip.size()) {
-      input_ids.insert(input_ids.end(), static_cast<size_t>(tokens_per_clip[i]), audio_token_id_);
+    if (i < clips.size()) {
+      input_ids.insert(input_ids.end(), static_cast<size_t>(clips[i].num_tokens), audio_token_id_);
     }
   }
-  named_tensors->emplace(std::string(Config::Defaults::InputIdsName),
-                         std::make_shared<Tensor>(MakeInputIds(input_ids, allocator)));
+  return input_ids;
+}
 
-  if (num_clips == 0) {
-    // The pipeline reads audio_sizes to skip the speech run.
-    named_tensors->emplace(std::string(Config::Defaults::AudioSizesName),
-                           std::make_shared<Tensor>(MakeInt64Tensor({0}, {1}, allocator)));
-    return named_tensors;
+void Lfm2AudioProcessor::AddAudioTensors(const std::vector<ClipMel>& clips, Ort::Allocator& allocator,
+                                         NamedTensors& named_tensors) const {
+  const int64_t num_clips = static_cast<int64_t>(clips.size());
+  const int64_t num_mels = mel_config_.num_mels;
+  int64_t longest = 0;
+  std::vector<int64_t> mel_lengths;
+  std::vector<int64_t> tokens_per_clip;
+  for (const auto& clip : clips) {
+    longest = std::max(longest, clip.num_frames);
+    mel_lengths.push_back(clip.num_frames);
+    tokens_per_clip.push_back(clip.num_tokens);
   }
 
   // The clips are staged in one zero-padded [num_clips, longest, num_mels] tensor with their real
   // lengths alongside. It is a container, not a batched encoder run: Lfm2AudioSpeechState slices
   // each clip's own frames back out and runs the encoder on them one clip at a time.
-  const int64_t num_mels = mel_config_.num_mels;
-  auto batch = OrtValue::CreateTensor<float>(allocator, std::vector<int64_t>{static_cast<int64_t>(num_clips), longest, num_mels});
+  auto batch = OrtValue::CreateTensor<float>(allocator, std::vector<int64_t>{num_clips, longest, num_mels});
   float* batch_data = batch->GetTensorMutableData<float>();
   std::fill_n(batch_data, static_cast<size_t>(num_clips) * static_cast<size_t>(longest * num_mels), 0.0f);
-  for (size_t i = 0; i < num_clips; ++i) {
-    std::copy(mels[i].begin(), mels[i].end(), batch_data + i * static_cast<size_t>(longest * num_mels));
+  for (size_t i = 0; i < clips.size(); ++i) {
+    std::copy(clips[i].mel.begin(), clips[i].mel.end(), batch_data + i * static_cast<size_t>(longest * num_mels));
   }
 
   if (mel_type_ == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
-    named_tensors->emplace(std::string(Config::Defaults::AudioEmbedsName), std::make_shared<Tensor>(std::move(batch)));
+    named_tensors.emplace(std::string(Config::Defaults::AudioEmbedsName), std::make_shared<Tensor>(std::move(batch)));
   } else {
     std::unique_ptr<OrtValue> converted;
     Cast(*batch, converted, *GetDeviceInterface(DeviceType::CPU), mel_type_);
-    named_tensors->emplace(std::string(Config::Defaults::AudioEmbedsName), std::make_shared<Tensor>(std::move(converted)));
+    named_tensors.emplace(std::string(Config::Defaults::AudioEmbedsName), std::make_shared<Tensor>(std::move(converted)));
   }
-  named_tensors->emplace(std::string(Config::Defaults::AudioLengthsName),
-                         std::make_shared<Tensor>(MakeInt64Tensor(mel_lengths, {static_cast<int64_t>(num_clips)}, allocator)));
-  named_tensors->emplace(std::string(Config::Defaults::AudioSizesName),
-                         std::make_shared<Tensor>(MakeInt64Tensor(tokens_per_clip, {static_cast<int64_t>(num_clips)}, allocator)));
+  named_tensors.emplace(std::string(Config::Defaults::AudioLengthsName),
+                        std::make_shared<Tensor>(MakeInt64Tensor(mel_lengths, {num_clips}, allocator)));
+  named_tensors.emplace(std::string(Config::Defaults::AudioSizesName),
+                        std::make_shared<Tensor>(MakeInt64Tensor(tokens_per_clip, {num_clips}, allocator)));
+}
+
+std::unique_ptr<NamedTensors> Lfm2AudioProcessor::Process(const Tokenizer& tokenizer, const Payload& payload) const {
+  const std::string prompt = ResolvePrompt(payload);
+  const Audios* audios = payload.audios;
+  const size_t num_clips = audios ? audios->num_audios_ : 0;
+  Ort::Allocator& allocator{Ort::Allocator::GetWithDefaultOptions()};
+  auto named_tensors = std::make_unique<NamedTensors>();
+
+  // With no clips the prompt must not ask for any; the split with zero clips enforces that.
+  const std::vector<std::string> segments = SplitLfm2AudioPrompt(prompt, num_clips);
+
+  std::vector<ClipMel> clips;
+  clips.reserve(num_clips);
+  for (size_t i = 0; i < num_clips; ++i) {
+    clips.push_back(ComputeClipMel(*audios, i));
+  }
+
+  named_tensors->emplace(std::string(Config::Defaults::InputIdsName),
+                         std::make_shared<Tensor>(MakeInputIds(MakePromptTokens(tokenizer, segments, clips), allocator)));
+
+  if (clips.empty()) {
+    // The pipeline reads audio_sizes to skip the speech run.
+    named_tensors->emplace(std::string(Config::Defaults::AudioSizesName),
+                           std::make_shared<Tensor>(MakeInt64Tensor({0}, {1}, allocator)));
+    return named_tensors;
+  }
+
+  AddAudioTensors(clips, allocator, *named_tensors);
   return named_tensors;
 }
 
diff --git a/src/models/preprocessing/lfm2_audio_processor.h b/src/models/preprocessing/lfm2_audio_processor.h
index dbcb051007..da9b2a8651 100644
--- a/src/models/preprocessing/lfm2_audio_processor.h
+++ b/src/models/preprocessing/lfm2_audio_processor.h
@@ -57,6 +57,24 @@ struct Lfm2AudioProcessor : Processor {
   std::unique_ptr<NamedTensors> Process(const Tokenizer& tokenizer, const Payload& payload) const override;
 
  private:
+  // One clip's log-mel frames, with what the rest of the pipeline has to know about them.
+  struct ClipMel {
+    std::vector<float> mel;  // num_frames * num_mels, frame-major
+    int64_t num_frames{};    // what the encoder is given for this clip
+    int64_t num_tokens{};    // placeholders it is worth, one per encoder frame
+  };
+
+  // Decodes clip `index` to mono PCM at the encoder's sample rate and turns it into log-mel frames.
+  ClipMel ComputeClipMel(const Audios& audios, size_t index) const;
+
+  // The prompt's text tokenized segment by segment, with each clip's placeholders spliced between.
+  std::vector<int32_t> MakePromptTokens(const Tokenizer& tokenizer, const std::vector<std::string>& segments,
+                                        const std::vector<ClipMel>& clips) const;
+
+  // Stages the clips in one zero-padded [num_clips, longest, num_mels] tensor, cast to the speech
+  // model's input type, and adds it with the per-clip frame and placeholder counts.
+  void AddAudioTensors(const std::vector<ClipMel>& clips, Ort::Allocator& allocator, NamedTensors& named_tensors) const;
+
   ONNXTensorElementDataType mel_type_;
   Lfm2AudioMelConfig mel_config_;
   int64_t subsampling_factor_{};
diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
index 1921542dad..a47987918a 100644
--- a/test/python/builder/test_lfm2_audio.py
+++ b/test/python/builder/test_lfm2_audio.py
@@ -54,7 +54,7 @@
 }
 
 
-def _write_checkpoint(path, decoder_weights=None):
+def _write_checkpoint(path, decoder_weights=None, shards=1):
     """An LFM2-Audio checkpoint directory: nested config.json plus lfm.* and audio tensors."""
     path.mkdir(parents=True, exist_ok=True)
     config = {
@@ -81,7 +81,20 @@ def _write_checkpoint(path, decoder_weights=None):
     tensors["audio_embedding.embedding.weight"] = torch.zeros(16, 32)
     tensors["depth_linear.weight"] = torch.zeros(8, 32)
     tensors["codebook_offsets"] = torch.arange(8) * 2049
-    save_file({name: tensor.contiguous() for name, tensor in tensors.items()}, str(path / "model.safetensors"))
+    tensors = {name: tensor.contiguous() for name, tensor in tensors.items()}
+    if shards == 1:
+        save_file(tensors, str(path / "model.safetensors"))
+    else:
+        # What `save_pretrained` writes once a checkpoint is over its shard size: several files and
+        # an index naming which tensor lives in which. An fp32 fine-tune of this model reaches it.
+        names = sorted(tensors)
+        weight_map = {}
+        for shard in range(shards):
+            filename = f"model-{shard + 1:05d}-of-{shards:05d}.safetensors"
+            part = {name: tensors[name] for name in names[shard::shards]}
+            save_file(part, str(path / filename))
+            weight_map.update(dict.fromkeys(part, filename))
+        (path / "model.safetensors.index.json").write_text(json.dumps({"metadata": {}, "weight_map": weight_map}))
     return decoder_weights
 
 
@@ -130,6 +143,23 @@ def test_lfm2_audio_load_weights_loads_the_decoder_with_tied_logits(tmp_path):
     assert not any(parameter.is_meta for parameter in loaded.parameters())
 
 
+@pytest.mark.parametrize("shards", [2, 3])
+def test_lfm2_audio_load_weights_reads_a_sharded_checkpoint(tmp_path, shards):
+    # These checkpoints are one file today, but `save_pretrained` shards anything over its size
+    # limit, which an fp32 fine-tune of this model exceeds. The tensors have to be gathered from
+    # every shard the index names, not just the first.
+    decoder_weights = _write_checkpoint(tmp_path, shards=shards)
+    assert not (tmp_path / "model.safetensors").exists(), "the sharded layout has no single file"
+
+    loaded = _audio_builder(tmp_path).load_weights(str(tmp_path))
+
+    torch.testing.assert_close(
+        loaded.model.layers[0].conv.in_proj.weight, decoder_weights["lfm.layers.0.conv.in_proj.weight"]
+    )
+    torch.testing.assert_close(loaded.model.embedding_norm.weight, decoder_weights["lfm.embedding_norm.weight"])
+    assert not any(parameter.is_meta for parameter in loaded.parameters())
+
+
 def test_lfm2_audio_load_weights_rejects_a_checkpoint_missing_decoder_tensors(tmp_path):
     torch.manual_seed(0)
     model = transformers.Lfm2ForCausalLM(transformers.Lfm2Config(**DECODER_CONFIG))

From 470dadf1f8c60c73d7d301267d1dc1ac5d40ee89 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 22:17:07 -0400
Subject: [PATCH 05/23] docs: say what to do about precision, with measured
 sizes

`-p int4` quantizes the decoder and nothing else. The embedding table
and the speech encoder are put together by hand, so they stay at
whatever they were built or downloaded at, and together they outweigh
the quantized decoder: 1787 MB for a build where only 730 MB of it was
quantized.

Measured all five combinations on three clips. Every int4 build wrote
the same words as every other, so a half-width embedding table and the
published 4-bit encoder cost nothing and take the build to 1178 MB. The
only difference anywhere is the int4 decoder against fp32, which dropped
one comma.

Also: the encoder variants refer to their own data file by name, so
renaming the pair breaks the build with an external-data error. Say so,
and stop the example renaming it.
---
 examples/python/lfm2-audio-tutorial.md | 49 ++++++++++++++++++++++----
 1 file changed, 42 insertions(+), 7 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 65d1c76ae8..a8eef44284 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -8,7 +8,7 @@ runs the speech-to-text half of it — ASR and spoken-prompt chat — as three O
 
 | Model | Inputs | Outputs |
 | --- | --- | --- |
-| `speech.onnx` | `mel_spectrogram`, `mel_lengths` | `audio_embeddings`, `audio_lengths` |
+| speech encoder (`audio_encoder.onnx`) | `mel_spectrogram`, `mel_lengths` | `audio_embeddings`, `audio_lengths` |
 | `embeddings.onnx` | `input_ids`, `audio_features` | `inputs_embeds` |
 | `model.onnx` (decoder) | `inputs_embeds`, `attention_mask`, `position_ids`, KV + conv cache | `logits` |
 
@@ -19,15 +19,16 @@ published as ONNX by LiquidAI, and the embedding model is a small graph you buil
 RQ-transformer ("depthformer") that ONNX Runtime GenAI has no generation loop for, and those codes
 then need the audio detokenizer and an inverse STFT to become a waveform. The export below drops the
 depthformer and the audio embeddings with it, so the model answers in text only. See
-[Known limitations](#5-known-limitations).
+[Known limitations](#6-known-limitations).
 
 ## Steps
 
 1. [Build the decoder](#1-build-the-decoder)
 2. [Get the speech encoder and build the embedding model](#2-get-the-speech-encoder-and-build-the-embedding-model)
 3. [Write `genai_config.json`](#3-write-genai_configjson)
-4. [Run the model](#4-run-the-model)
-5. [Known limitations](#5-known-limitations)
+4. [Choose the precisions](#4-choose-the-precisions)
+5. [Run the model](#5-run-the-model)
+6. [Known limitations](#6-known-limitations)
 
 ## 1. Build the decoder
 
@@ -176,7 +177,7 @@ produced. Leave the `decoder` and `search` sections it wrote alone:
         "eos_token_id": 7,
         "pad_token_id": 0,
         "speech": {
-            "filename": "speech.onnx",
+            "filename": "audio_encoder.onnx",
             "inputs": {
                 "audio_embeds": "mel_spectrogram",
                 "audio_lengths": "mel_lengths",
@@ -197,6 +198,9 @@ produced. Leave the `decoder` and `search` sections it wrote alone:
 }
 ```
 
+`filename` is whatever the encoder file is called on disk — keep the name it was published under,
+for the reason in [Choose the precisions](#4-choose-the-precisions).
+
 `audio_sizes` is not an encoder input; the name only tells the runtime what to call the per-clip
 token counts it computes, so leave it as is unless it collides with a real input of your graph.
 `audio_token_id` must be the id the embedding model scatters over, and must not be an id the
@@ -222,7 +226,38 @@ LFM2-Audio checkpoint shares (`"preprocessor"` in `config.json`), which are NeMo
 Set the overrides only for a fine-tune that changed them; the defaults already match all the
 published models.
 
-## 4. Run the model
+## 4. Choose the precisions
+
+`-p int4` quantizes the decoder, and the decoder alone. The embedding model and the speech encoder
+are assembled by hand, so they stay at whatever precision you built or downloaded them at — and
+together they are bigger than the quantized decoder. Measured on LFM2.5-Audio-1.5B, transcribing
+three clips (16 kHz, 44.1 kHz stereo, and an mp3):
+
+| Decoder | Embedding table | Encoder | Size | Transcripts |
+| --- | --- | --- | --- | --- |
+| fp32 | fp32 | fp32 | 5736 MB | baseline |
+| int4 | fp32 | fp32 | 1787 MB | correct; one comma differs from fp32 |
+| int4 | fp16 | fp32 | 1518 MB | identical to the row above |
+| int4 | fp16 | fp16 | 1278 MB | identical to the row above |
+| **int4** | **fp16** | **q4** | **1178 MB** | **identical to the row above** |
+
+Quantizing the other two components is free here: every int4 row produced the same tokens as every
+other, so the last row is the one to build. The only difference anywhere is the int4 decoder against
+fp32 — `And so, my fellow Americans` became `And so my fellow Americans` on one clip, with the words
+otherwise unchanged.
+
+For the embedding table, `astype(np.float16)` on the weight in the snippet above is the whole change;
+keep the graph's output fp32 by casting after the `Gather`, so the decoder still receives what it
+declares. A 4-bit table would be smaller still — the builder's own text-only export gets one, through
+`GatherBlockQuantized` over the quantized `lm_head` weights — but `exclude_embeds` is what puts the
+table outside the decoder in the first place, so the pipeline cannot share that copy.
+
+For the encoder, take `audio_encoder_q4.onnx` instead of `audio_encoder.onnx`. **Keep the published
+file names.** Each variant's graph refers to its own `*.onnx_data` by name, so renaming the pair to
+`speech.onnx` breaks the lookup with `External data path validation failed`. Leave both files as they
+are and point `model.speech.filename` at the name you downloaded.
+
+## 5. Run the model
 
 [`model-mm.py`](model-mm.py) drives any multi-modal model in this repository:
 
@@ -254,7 +289,7 @@ Perform ASR.<|im_end|>
 <|im_start|>assistant
 ```
 
-## 5. Known limitations
+## 6. Known limitations
 
 **Audio output is not supported.** Interleaved and TTS generation need the depthformer, which
 predicts 8 codebook entries per 80 ms audio frame in an inner autoregressive loop, plus the audio

From 58a9a423beca0f5ab901c3700606d1e04c63e478 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 22:29:19 -0400
Subject: [PATCH 06/23] fix: stop generating when the model starts speaking

These checkpoints answer in speech as well as text, chosen by the system
prompt. Asked for speech, the model emits <|audio_start|> and everything
after it is audio codes meant for the depthformer, which this runtime
does not have. Those positions were being read off the text head, which
gave fluent, plausible nonsense with nothing to say it was wrong: a TTS
prompt returned eleven tokens of invented sentence.

<|audio_start|> now goes into the stop tokens when the decoder is built,
so an answer that turns to speech ends there instead, keeping whatever
text came before it.

Of the model's three modes, only transcription was ever exercised. Now
measured on the real model: transcription is unchanged and still matches
the reference token for token; a speech prompt returns nothing at all
rather than nonsense; and the interleaved prompt returns text, which on
a ten second clip was the reference's interleaved text exactly, all 29
tokens of it, before the reference carried on into speech. The tutorial
says which prompt gives what, and that the temperatures quoted for the
model are for the speech stream this runtime does not produce.
---
 examples/python/lfm2-audio-tutorial.md       | 55 +++++++++++++-----
 src/python/py/models/builders/lfm2.py        | 14 +++++
 test/python/builder/test_lfm2_audio.py       | 22 +++++++
 test/python/models/test_lfm2_audio_models.py | 61 ++++++++++++++++++++
 4 files changed, 138 insertions(+), 14 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index a8eef44284..0d81a1747f 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -273,13 +273,7 @@ produced. Text on either side of a marker is tokenized on its own, which is what
 `liquid_audio.ChatState` does.
 
 LFM2-Audio has no audio token of its own and no chat-template entry for audio: `<|audio|>` is this
-runtime's marker, the same role `<image>` plays for LFM2-VL. The system prompt selects the task, as
-in the reference implementation:
-
-| Task | System prompt |
-| --- | --- |
-| Transcription | `Perform ASR.` |
-| Spoken question, text answer | `Respond with interleaved text and audio.` (the text half of the answer) |
+runtime's marker, the same role `<image>` plays for LFM2-VL.
 
 ```
 <|startoftext|><|im_start|>system
@@ -289,15 +283,48 @@ Perform ASR.<|im_end|>
 <|im_start|>assistant
 ```
 
+### The model's three modes
+
+The system prompt picks the task, and the reference implementation pairs each with a generation
+routine of its own. Only the first produces text alone, so only the first runs here in full:
+
+| Mode | System prompt | Reference routine | Output | Here |
+| --- | --- | --- | --- | --- |
+| ASR | `Perform ASR.` | `generate_sequential` | text | works |
+| TTS | `Perform TTS. Use the UK male voice.` (also US male, US female, UK female) | `generate_sequential` | speech | stops immediately |
+| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | the text, up to the first switch to speech |
+
+**ASR** is the supported path, and matches the reference token for token.
+
+**TTS** produces nothing here. The model answers a TTS prompt by emitting `<|audio_start|>` as its
+very first token and then audio codes, so generation stops there and the answer is empty. That is
+deliberate: the builder puts `<|audio_start|>` in `eos_token_id` for these models, because the
+positions after it are meant for the depthformer this runtime does not have — read off the text head
+they decode to fluent, plausible nonsense. A sequential answer that begins in text and turns to
+speech part way keeps the text it had before the switch.
+
+**Interleaved** returns text only. The reference alternates by count — `interleaved_n_text` text
+tokens, then `interleaved_n_audio` audio frames, from `config.json` — rather than by any token in the
+stream, so there is nothing here to switch on, and generation simply runs to `<|im_end|>`. On a ten
+second clip the text produced this way was the reference's interleaved text stream exactly, for all
+29 tokens before the end of the turn; the reference then carried on past that point, alternating
+text with speech, where this ends the turn. Treat it as the opening of the answer, not the whole of
+it.
+
+Sampling: the reference generates text greedily in every mode, so `do_sample=False` is right here.
+The temperatures and `top_k` values quoted for the model (`audio_temperature=0.8, audio_top_k=64`
+for TTS, `1.0` and `4` for interleaved) apply only to the audio stream, which this runtime does not
+produce; they have no text-side equivalent to set.
+
 ## 6. Known limitations
 
-**Audio output is not supported.** Interleaved and TTS generation need the depthformer, which
-predicts 8 codebook entries per 80 ms audio frame in an inner autoregressive loop, plus the audio
-detokenizer and an inverse STFT to turn those codes into a waveform. ONNX Runtime GenAI's generation
-loop samples one token stream, so none of that has a home here yet. The model still answers in text
-when asked to; a prompt that asks it to speak makes it emit `<|audio_start|>` and then codes the
-runtime has nothing to do with. LiquidAI ships `vocoder_depthformer.onnx` and
-`audio_detokenizer.onnx` in the ONNX repository, and their
+**Audio output is not supported.** TTS and the speech half of interleaved generation need the
+depthformer, which predicts 8 codebook entries per 80 ms audio frame in an inner autoregressive loop,
+plus the audio detokenizer and an inverse STFT to turn those codes into a waveform. ONNX Runtime
+GenAI's generation loop samples one token stream, so none of that has a home here yet. Generation
+stops at `<|audio_start|>` rather than reading the depthformer's positions off the text head; see
+[the three modes](#the-models-three-modes) for what each prompt gives you. LiquidAI ships
+`vocoder_depthformer.onnx` and `audio_detokenizer.onnx` in the ONNX repository, and their
 [onnx-export](https://github.com/Liquid4All/onnx-export) repository drives them from Python.
 
 **One prompt at a time.** Several clips in one prompt work, but batched prompts are refused. The
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index 330e2767f2..a058c7b479 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -232,6 +232,20 @@ def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
         super().__init__(config, io_dtype, onnx_dtype, ep, cache_dir, extra_options)
         self.decoder_config = config
 
+    def update_genai_config(self, genai_config):
+        super().update_genai_config(genai_config)
+
+        # These checkpoints answer in speech as well as text: asked to, the model emits
+        # <|audio_start|> and everything after it is audio codes for the depthformer, which this
+        # runtime does not have. Sampling those positions from the text head gives fluent nonsense,
+        # so stop there instead. An answer that turns to speech part way keeps the text it had.
+        audio_start_token_id = 128
+        eos_token_id = genai_config["model"].get("eos_token_id")
+        eos_token_ids = list(eos_token_id) if isinstance(eos_token_id, list) else [eos_token_id]
+        if audio_start_token_id not in eos_token_ids:
+            eos_token_ids.append(audio_start_token_id)
+        genai_config["model"]["eos_token_id"] = eos_token_ids
+
     def load_weights(self, input_path):
         if input_path.endswith(".gguf") or self.quant_type is not None:
             return super().load_weights(input_path)
diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
index a47987918a..15c31a858b 100644
--- a/test/python/builder/test_lfm2_audio.py
+++ b/test/python/builder/test_lfm2_audio.py
@@ -246,6 +246,28 @@ def test_lfm2_audio_load_weights_without_an_adapter_returns_the_decoder(tmp_path
     assert type(loaded).__name__ == "Lfm2ForCausalLM"
 
 
+@pytest.mark.parametrize(
+    "existing,expected",
+    [(7, [7, 128]), ([7], [7, 128]), ([7, 2], [7, 2, 128]), ([7, 128], [7, 128])],
+    ids=["scalar", "list", "several", "already-there"],
+)
+def test_lfm2_audio_stops_when_the_model_starts_speaking(existing, expected):
+    # The model answers in speech too. Everything after <|audio_start|> is audio codes meant for the
+    # depthformer, which this runtime does not have, so generation has to end there rather than read
+    # those positions off the text head.
+    builder = LFM2AudioModel.__new__(LFM2AudioModel)
+    builder.layer_types = ["conv", "full_attention"]
+    builder.conv_L_cache = 3
+    genai_config = {"model": {"decoder": {}, "eos_token_id": existing}}
+
+    builder.update_genai_config(genai_config)
+
+    assert genai_config["model"]["eos_token_id"] == expected
+    # The LFM2 decoder settings the base class writes are still there.
+    assert genai_config["model"]["decoder"]["layer_types"] == ["conv", "full_attention"]
+    assert genai_config["model"]["decoder"]["conv_cache_size"] == 2
+
+
 @pytest.mark.parametrize("exclude_embeds,expected_type", [(True, "lfm2_audio"), (False, "lfm2_audio_text")])
 def test_lfm2_audio_architecture_labels_the_export(monkeypatch, tmp_path, exclude_embeds, expected_type):
     captured = {}
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index 4b57a4e84c..feda9cb7fe 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -32,6 +32,7 @@
 import pytest
 
 AUDIO_MARKER = "<|audio|>"
+AUDIO_START_TOKEN_ID = 128  # <|audio_start|>: the model switching from text to speech
 AUDIO_TOKEN_ID = 133  # <|reserved_123|>, model.audio_token_id in the fixture's genai_config.json
 NUM_MELS = 128
 SAMPLE_RATE = 16000
@@ -612,6 +613,66 @@ def test_lfm2_audio_features_follow_the_clip_order(test_data_path, tmp_path):
     assert outputs[0] != outputs[1], "swapping the clips must change what the decoder sees"
 
 
+def _generate_with_eos(model_path, clip, eos, num_tokens):
+    model = og.Model(os.fspath(model_path))
+    processor = model.create_multimodal_processor()
+    inputs = processor(AUDIO_MARKER, audios=og.Audios.open(clip))
+    prompt_length = inputs["input_ids"].as_numpy().shape[1]
+    params = og.GeneratorParams(model)
+    params.set_search_options(do_sample=False, max_length=prompt_length + num_tokens)
+    generator = og.Generator(model, params)
+    generator.set_inputs(inputs)
+    generated = []
+    while not generator.is_done():
+        generator.generate_next_token()
+        generated.append(int(generator.get_next_tokens()[0]))
+    return generated
+
+
+def test_lfm2_audio_generation_stops_when_the_model_starts_speaking(test_data_path, tmp_path):
+    # Everything the model emits after <|audio_start|> is audio codes for a depthformer this runtime
+    # does not have, so the builder puts that token in eos_token_id and generation has to end on it.
+    # The fixture decoder never emits 128 on its own, so this pins the mechanism on a token it does
+    # emit: whichever id is named as the audio stop, the run ends at its first occurrence.
+    model_path = _copy_model(test_data_path, tmp_path)
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5, seed=40))
+
+    unrestricted = _generate_with_eos(model_path, clip, [7], num_tokens=40)
+    assert len(unrestricted) == 40, "the fixture should run to max_length without a stop token"
+
+    stop_token = unrestricted[5]
+    first = unrestricted.index(stop_token)
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({"eos_token_id": [stop_token]}))
+
+    stopped = _generate_with_eos(model_path, clip, [stop_token], num_tokens=40)
+
+    assert stopped == unrestricted[: first + 1], "generation must end at the stop token, with nothing after it"
+
+
+def test_lfm2_audio_audio_start_in_eos_leaves_normal_stopping_alone(test_data_path, tmp_path):
+    # Adding <|audio_start|> alongside <|im_end|> must not change a run that never starts speaking.
+    model_path = _copy_model(test_data_path, tmp_path)
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5, seed=41))
+
+    without = _generate_with_eos(model_path, clip, [7], num_tokens=25)
+    _edit_json(
+        model_path / "genai_config.json",
+        lambda config: config["model"].update({"eos_token_id": [7, AUDIO_START_TOKEN_ID]}),
+    )
+    with_audio_stop = _generate_with_eos(model_path, clip, [7, AUDIO_START_TOKEN_ID], num_tokens=25)
+
+    assert AUDIO_START_TOKEN_ID not in without, "the fixture is not expected to emit <|audio_start|>"
+    assert with_audio_stop == without
+
+
+def test_lfm2_audio_fixture_config_is_what_the_builder_writes(test_data_path):
+    # The tiny model stands in for a built one; its stop tokens have to match what the builder emits
+    # for these checkpoints, or the tests above would be exercising a different contract.
+    config = json.loads((Path(_model_path(test_data_path)) / "genai_config.json").read_text())
+    assert config["model"]["type"] == "lfm2_audio"
+    assert config["model"]["audio_token_id"] == AUDIO_TOKEN_ID
+
+
 def test_lfm2_audio_rejects_rewind(test_data_path, tmp_path):
     clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5))
     generator, _ = _generate(_model_path(test_data_path), f"{AUDIO_MARKER}Hi", og.Audios.open(clip), num_tokens=1)

From e69976c38717f563654e48278bc5204fe1ce5a5d Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 22:43:17 -0400
Subject: [PATCH 07/23] docs: correct what interleaved mode gives, having
 listened to it

The earlier note was measured with the audio taken greedily, which is
not how the model card runs interleaved generation, and it overstated
the case twice over: it said this runtime returns the text up to the
first switch to speech, when the switch comes after six tokens and the
whole text still came through; and it said the reference carries on past
where this stops, which was an artefact of that sampling.

Generated the speech properly this time, with the sampling the model
card gives, and decoded it. The reference answered with 29 text tokens
and 48 audio frames; those frames are 3.8 seconds of real speech, which
our own transcription reads back as the opening of the same answer. This
runtime gives the same 29 tokens. With the audio taken greedily the
reference instead runs to 54 tokens, so the text does depend on the
speech, and the agreement is not a guarantee. Says so now.
---
 examples/python/lfm2-audio-tutorial.md | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 0d81a1747f..581fd67cf4 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -292,7 +292,7 @@ routine of its own. Only the first produces text alone, so only the first runs h
 | --- | --- | --- | --- | --- |
 | ASR | `Perform ASR.` | `generate_sequential` | text | works |
 | TTS | `Perform TTS. Use the UK male voice.` (also US male, US female, UK female) | `generate_sequential` | speech | stops immediately |
-| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | the text, up to the first switch to speech |
+| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | the text; none of the speech |
 
 **ASR** is the supported path, and matches the reference token for token.
 
@@ -303,13 +303,16 @@ positions after it are meant for the depthformer this runtime does not have —
 they decode to fluent, plausible nonsense. A sequential answer that begins in text and turns to
 speech part way keeps the text it had before the switch.
 
-**Interleaved** returns text only. The reference alternates by count — `interleaved_n_text` text
-tokens, then `interleaved_n_audio` audio frames, from `config.json` — rather than by any token in the
-stream, so there is nothing here to switch on, and generation simply runs to `<|im_end|>`. On a ten
-second clip the text produced this way was the reference's interleaved text stream exactly, for all
-29 tokens before the end of the turn; the reference then carried on past that point, alternating
-text with speech, where this ends the turn. Treat it as the opening of the answer, not the whole of
-it.
+**Interleaved** returns text only. The reference alternates by count — six text tokens, then
+`interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint) — rather than on any token in the
+stream, so there is nothing here to switch on and generation simply runs to `<|im_end|>`.
+
+The speech it generates is real, and it feeds back into the context, so the text depends on it. On a
+ten second clip the reference answered with 29 text tokens and 48 audio frames using the sampling the
+model card gives (`audio_temperature=1.0, audio_top_k=4`), and with 54 text tokens when the audio was
+taken greedily instead. This runtime produced those same first 29 tokens and stopped. The first 29
+agree across all three, and they diverge after that. So take what you get as the answer the model
+would speak, not as a reproduction of any one interleaved run.
 
 Sampling: the reference generates text greedily in every mode, so `do_sample=False` is right here.
 The temperatures and `top_k` values quoted for the model (`audio_temperature=0.8, audio_top_k=64`
@@ -320,7 +323,9 @@ produce; they have no text-side equivalent to set.
 
 **Audio output is not supported.** TTS and the speech half of interleaved generation need the
 depthformer, which predicts 8 codebook entries per 80 ms audio frame in an inner autoregressive loop,
-plus the audio detokenizer and an inverse STFT to turn those codes into a waveform. ONNX Runtime
+plus the audio detokenizer to turn those codes into a waveform. That speech is worth having: the 48
+frames the reference generated for the clip above decode to 3.8 seconds of 24 kHz audio, which this
+runtime's own ASR transcribes back as the opening of the same answer. It simply has no path here. ONNX Runtime
 GenAI's generation loop samples one token stream, so none of that has a home here yet. Generation
 stops at `<|audio_start|>` rather than reading the depthformer's positions off the text head; see
 [the three modes](#the-models-three-modes) for what each prompt gives you. LiquidAI ships

From 9c8eacc2d7fd5110983d533e4ff3f4d905d02bc4 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 22:47:18 -0400
Subject: [PATCH 08/23] docs: verify the speech the model produces, and say
 what it is

Asked the reference to speak a known sentence in two of its voices, and
read the result back with our own transcription: both came back as the
sentence exactly, punctuation and all. The two voices are different
waveforms, and the male one carries more of its energy low down than the
female one. So the speech this runtime leaves on the table is good, not
a curiosity, and the earlier partial read-back was a truncated clip
rather than poor audio.

Also: a speech prompt yields no text at all, not merely a short one.
The entire text stream is the token that starts the audio followed by
the end of the turn.
---
 examples/python/lfm2-audio-tutorial.md | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 581fd67cf4..2a1a4242fd 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -296,12 +296,13 @@ routine of its own. Only the first produces text alone, so only the first runs h
 
 **ASR** is the supported path, and matches the reference token for token.
 
-**TTS** produces nothing here. The model answers a TTS prompt by emitting `<|audio_start|>` as its
-very first token and then audio codes, so generation stops there and the answer is empty. That is
-deliberate: the builder puts `<|audio_start|>` in `eos_token_id` for these models, because the
-positions after it are meant for the depthformer this runtime does not have — read off the text head
-they decode to fluent, plausible nonsense. A sequential answer that begins in text and turns to
-speech part way keeps the text it had before the switch.
+**TTS** produces nothing here, and there is no text to be had: asked to speak a sentence, the whole
+text stream the model emits is `<|audio_start|>` followed by `<|im_end|>`, with everything in between
+being audio. Generation stops at that first token, which is deliberate — the builder puts
+`<|audio_start|>` in `eos_token_id` for these models, because the positions after it are meant for
+the depthformer this runtime does not have, and read off the text head they decode to fluent,
+plausible nonsense. A sequential answer that begins in text and turns to speech part way keeps the
+text it had before the switch.
 
 **Interleaved** returns text only. The reference alternates by count — six text tokens, then
 `interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint) — rather than on any token in the
@@ -323,9 +324,11 @@ produce; they have no text-side equivalent to set.
 
 **Audio output is not supported.** TTS and the speech half of interleaved generation need the
 depthformer, which predicts 8 codebook entries per 80 ms audio frame in an inner autoregressive loop,
-plus the audio detokenizer to turn those codes into a waveform. That speech is worth having: the 48
-frames the reference generated for the clip above decode to 3.8 seconds of 24 kHz audio, which this
-runtime's own ASR transcribes back as the opening of the same answer. It simply has no path here. ONNX Runtime
+plus the audio detokenizer to turn those codes into a waveform. That speech is worth having, and it
+is good: asked through the reference to say *The quick brown fox jumps over the lazy dog.*, the model
+produced about three seconds of 24 kHz audio per voice, and this runtime's own ASR read both back as
+that sentence exactly, punctuation included. The two voices are different waveforms, and the male one
+carries more of its energy below 1 kHz than the female one, as it should. None of it has a path here. ONNX Runtime
 GenAI's generation loop samples one token stream, so none of that has a home here yet. Generation
 stops at `<|audio_start|>` rather than reading the depthformer's positions off the text head; see
 [the three modes](#the-models-three-modes) for what each prompt gives you. LiquidAI ships

From 907201217a2b805b2528be796879168c1ccbbfe2 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 23:10:25 -0400
Subject: [PATCH 09/23] fix: stop at the end of the text half of an interleaved
 answer too

Stopping when the model starts speaking covered the case where the whole
answer is spoken, but not the interleaved one, where the model writes an
answer, marks the text finished, and says the rest aloud. Asked a spoken
question that way, the answer came back followed by thirty-odd tokens of
invented whitespace read off the text head at positions meant for the
depthformer.

Both tokens that hand the turn over to speech now end generation. Asked
"What is the capital of France?", the interleaved answer is now exactly
"The capital of France is Paris" -- the text the model wrote before it
began speaking, and what it then spoke. Transcription and a turn that
stays in text are unchanged.

Found by listening to the speech instead of only counting its frames:
synthesising a question, asking it back, and reading the answer aloud.
---
 examples/python/lfm2-audio-tutorial.md       | 46 ++++++++++++--------
 src/python/py/models/builders/lfm2.py        | 14 +++---
 test/python/builder/test_lfm2_audio.py       | 17 +++++---
 test/python/models/test_lfm2_audio_models.py | 17 +++++---
 4 files changed, 56 insertions(+), 38 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 2a1a4242fd..d4b61de8c7 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -292,28 +292,34 @@ routine of its own. Only the first produces text alone, so only the first runs h
 | --- | --- | --- | --- | --- |
 | ASR | `Perform ASR.` | `generate_sequential` | text | works |
 | TTS | `Perform TTS. Use the UK male voice.` (also US male, US female, UK female) | `generate_sequential` | speech | stops immediately |
-| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | the text; none of the speech |
+| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | the text answer; none of the speech |
 
 **ASR** is the supported path, and matches the reference token for token.
 
+Two tokens mark the turn passing from text to speech, and the builder puts both in `eos_token_id`:
+`<|audio_start|>` when the rest of the answer is spoken, and `<|text_end|>` when the text half of an
+interleaved answer is done. The positions after either are audio codes meant for the depthformer this
+runtime does not have; read off the text head they decode to fluent, plausible nonsense, so
+generation ends there with whatever text came first.
+
 **TTS** produces nothing here, and there is no text to be had: asked to speak a sentence, the whole
-text stream the model emits is `<|audio_start|>` followed by `<|im_end|>`, with everything in between
-being audio. Generation stops at that first token, which is deliberate — the builder puts
-`<|audio_start|>` in `eos_token_id` for these models, because the positions after it are meant for
-the depthformer this runtime does not have, and read off the text head they decode to fluent,
-plausible nonsense. A sequential answer that begins in text and turns to speech part way keeps the
-text it had before the switch.
-
-**Interleaved** returns text only. The reference alternates by count — six text tokens, then
-`interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint) — rather than on any token in the
-stream, so there is nothing here to switch on and generation simply runs to `<|im_end|>`.
-
-The speech it generates is real, and it feeds back into the context, so the text depends on it. On a
-ten second clip the reference answered with 29 text tokens and 48 audio frames using the sampling the
-model card gives (`audio_temperature=1.0, audio_top_k=4`), and with 54 text tokens when the audio was
-taken greedily instead. This runtime produced those same first 29 tokens and stopped. The first 29
-agree across all three, and they diverge after that. So take what you get as the answer the model
-would speak, not as a reproduction of any one interleaved run.
+text stream the model emits is `<|audio_start|>` followed by `<|im_end|>`, everything in between
+being audio. Generation stops on that first token. A sequential answer that begins in text and turns
+to speech part way keeps the text it had before the switch.
+
+**Interleaved** gives the text answer and none of the speech. Asked a spoken question, the model
+writes its answer, emits `<|text_end|>`, and says the rest aloud; generation stops at that token, so
+what comes back is the answer itself. Asked *What is the capital of France?*, this runtime returns
+`The capital of France is Paris` — the same text the reference produced before it began speaking,
+which it then spoke.
+
+Between those text tokens the reference is also emitting audio, six text tokens at a time against
+`interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint), and that speech feeds back into
+the context. So the text can depend on audio this runtime never generates: on a ten second clip with
+no question in it, the reference wrote 29 text tokens with the sampling the model card gives and 54
+with the audio taken greedily, and this runtime matched the first 29 and stopped. Short answers to
+spoken questions come through whole; a long turn is the model's answer as far as it gets, not a
+reproduction of any one interleaved run.
 
 Sampling: the reference generates text greedily in every mode, so `do_sample=False` is right here.
 The temperatures and `top_k` values quoted for the model (`audio_temperature=0.8, audio_top_k=64`
@@ -328,7 +334,9 @@ plus the audio detokenizer to turn those codes into a waveform. That speech is w
 is good: asked through the reference to say *The quick brown fox jumps over the lazy dog.*, the model
 produced about three seconds of 24 kHz audio per voice, and this runtime's own ASR read both back as
 that sentence exactly, punctuation included. The two voices are different waveforms, and the male one
-carries more of its energy below 1 kHz than the female one, as it should. None of it has a path here. ONNX Runtime
+carries more of its energy below 1 kHz than the female one, as it should. Asked the spoken question
+above in interleaved mode, it answered in 41 audio frames ending in the end-of-audio code, 3.2
+seconds that read back as *The capital of France is Paris.* None of it has a path here. ONNX Runtime
 GenAI's generation loop samples one token stream, so none of that has a home here yet. Generation
 stops at `<|audio_start|>` rather than reading the depthformer's positions off the text head; see
 [the three modes](#the-models-three-modes) for what each prompt gives you. LiquidAI ships
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index a058c7b479..ec793a5fa5 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -235,15 +235,15 @@ def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
     def update_genai_config(self, genai_config):
         super().update_genai_config(genai_config)
 
-        # These checkpoints answer in speech as well as text: asked to, the model emits
-        # <|audio_start|> and everything after it is audio codes for the depthformer, which this
-        # runtime does not have. Sampling those positions from the text head gives fluent nonsense,
-        # so stop there instead. An answer that turns to speech part way keeps the text it had.
-        audio_start_token_id = 128
+        # These checkpoints answer in speech as well as text, and two tokens mark the turn passing
+        # from one to the other: <|audio_start|> when the whole answer is spoken, and <|text_end|>
+        # when text and speech are interleaved. Everything after either is audio codes for the
+        # depthformer, which this runtime does not have, and sampling those positions from the text
+        # head gives fluent nonsense. Stop on them instead, keeping whatever text came first.
+        modality_switch_token_ids = [128, 130]  # <|audio_start|>, <|text_end|>
         eos_token_id = genai_config["model"].get("eos_token_id")
         eos_token_ids = list(eos_token_id) if isinstance(eos_token_id, list) else [eos_token_id]
-        if audio_start_token_id not in eos_token_ids:
-            eos_token_ids.append(audio_start_token_id)
+        eos_token_ids += [token for token in modality_switch_token_ids if token not in eos_token_ids]
         genai_config["model"]["eos_token_id"] = eos_token_ids
 
     def load_weights(self, input_path):
diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
index 15c31a858b..4aabd690aa 100644
--- a/test/python/builder/test_lfm2_audio.py
+++ b/test/python/builder/test_lfm2_audio.py
@@ -248,13 +248,20 @@ def test_lfm2_audio_load_weights_without_an_adapter_returns_the_decoder(tmp_path
 
 @pytest.mark.parametrize(
     "existing,expected",
-    [(7, [7, 128]), ([7], [7, 128]), ([7, 2], [7, 2, 128]), ([7, 128], [7, 128])],
-    ids=["scalar", "list", "several", "already-there"],
+    [
+        (7, [7, 128, 130]),
+        ([7], [7, 128, 130]),
+        ([7, 2], [7, 2, 128, 130]),
+        ([7, 128, 130], [7, 128, 130]),
+        ([128], [128, 130]),
+    ],
+    ids=["scalar", "list", "several", "already-there", "partly-there"],
 )
 def test_lfm2_audio_stops_when_the_model_starts_speaking(existing, expected):
-    # The model answers in speech too. Everything after <|audio_start|> is audio codes meant for the
-    # depthformer, which this runtime does not have, so generation has to end there rather than read
-    # those positions off the text head.
+    # The model answers in speech too. <|audio_start|> marks a wholly spoken answer and <|text_end|>
+    # the text half of an interleaved one ending; after either, the positions are audio codes meant
+    # for the depthformer, which this runtime does not have, so generation has to end there rather
+    # than read them off the text head.
     builder = LFM2AudioModel.__new__(LFM2AudioModel)
     builder.layer_types = ["conv", "full_attention"]
     builder.conv_L_cache = 3
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index feda9cb7fe..344b7c1c13 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -32,7 +32,8 @@
 import pytest
 
 AUDIO_MARKER = "<|audio|>"
-AUDIO_START_TOKEN_ID = 128  # <|audio_start|>: the model switching from text to speech
+AUDIO_START_TOKEN_ID = 128  # <|audio_start|>: the answer is spoken from here on
+TEXT_END_TOKEN_ID = 130  # <|text_end|>: the text half of an interleaved answer is over
 AUDIO_TOKEN_ID = 133  # <|reserved_123|>, model.audio_token_id in the fixture's genai_config.json
 NUM_MELS = 128
 SAMPLE_RATE = 16000
@@ -649,20 +650,22 @@ def test_lfm2_audio_generation_stops_when_the_model_starts_speaking(test_data_pa
     assert stopped == unrestricted[: first + 1], "generation must end at the stop token, with nothing after it"
 
 
-def test_lfm2_audio_audio_start_in_eos_leaves_normal_stopping_alone(test_data_path, tmp_path):
-    # Adding <|audio_start|> alongside <|im_end|> must not change a run that never starts speaking.
+def test_lfm2_audio_modality_stops_leave_normal_stopping_alone(test_data_path, tmp_path):
+    # Adding the two modality-switch tokens alongside <|im_end|> must not change a run that stays in
+    # text the whole way.
     model_path = _copy_model(test_data_path, tmp_path)
     clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5, seed=41))
+    switches = [AUDIO_START_TOKEN_ID, TEXT_END_TOKEN_ID]
 
     without = _generate_with_eos(model_path, clip, [7], num_tokens=25)
     _edit_json(
         model_path / "genai_config.json",
-        lambda config: config["model"].update({"eos_token_id": [7, AUDIO_START_TOKEN_ID]}),
+        lambda config: config["model"].update({"eos_token_id": [7, *switches]}),
     )
-    with_audio_stop = _generate_with_eos(model_path, clip, [7, AUDIO_START_TOKEN_ID], num_tokens=25)
+    with_switch_stops = _generate_with_eos(model_path, clip, [7, *switches], num_tokens=25)
 
-    assert AUDIO_START_TOKEN_ID not in without, "the fixture is not expected to emit <|audio_start|>"
-    assert with_audio_stop == without
+    assert not set(switches) & set(without), "the fixture is not expected to emit the switch tokens"
+    assert with_switch_stops == without
 
 
 def test_lfm2_audio_fixture_config_is_what_the_builder_writes(test_data_path):

From 8f592728d22ed90a7c52360d50035d713e11e7da Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 23:17:52 -0400
Subject: [PATCH 10/23] test: cover the checks on the speech inputs, and drop
 one that cannot fire

Went through every way the audio code can refuse something and asked
which were actually reachable. The tensors the processor returns can be
replaced before they reach the generator, so the checks that they still
agree with each other are reachable, and five of them now have tests:
a mel tensor of the wrong rank, a clip count that disagrees with the
placeholders, a frame count per clip that disagrees with the tensor, and
a clip claiming frames past the end of it, which would otherwise be
copied out of bounds. Beam search with several clips is refused too,
since they share one buffer.

One check could never fire: it compared the sum of the per-clip token
counts against a total derived from those same counts upstream. Removed.

Also covers the three front-end settings that change the numbers rather
than the shapes, where a default quietly ignoring the override would
have looked perfectly healthy.
---
 src/models/multi_modal.cpp                   |  6 --
 test/python/models/test_lfm2_audio_models.py | 88 +++++++++++++++++++-
 2 files changed, 84 insertions(+), 10 deletions(-)

diff --git a/src/models/multi_modal.cpp b/src/models/multi_modal.cpp
index f26d3e6cb4..cc6dbf39a1 100644
--- a/src/models/multi_modal.cpp
+++ b/src/models/multi_modal.cpp
@@ -639,12 +639,6 @@ Lfm2AudioSpeechState::SpeechBindings Lfm2AudioSpeechState::ResolveBindings() con
     throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(bindings.num_clips) +
                              " clips but audio_sizes has " + std::to_string(tokens_per_clip_.size()) + " entries.");
   }
-  const int64_t total = std::accumulate(tokens_per_clip_.begin(), tokens_per_clip_.end(), int64_t{0});
-  if (total != num_audio_tokens_) {
-    throw std::runtime_error("Lfm2AudioSpeechState: audio_sizes sums to " + std::to_string(total) + " tokens but " +
-                             std::to_string(num_audio_tokens_) + " were expected.");
-  }
-
   const auto lengths_info = inputs_[bindings.lengths_index]->GetTensorTypeAndShapeInfo();
   if (static_cast<int64_t>(lengths_info->GetElementCount()) != bindings.num_clips) {
     throw std::runtime_error("Lfm2AudioSpeechState: the mel tensor holds " + std::to_string(bindings.num_clips) +
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index 344b7c1c13..0d3c2301b4 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -165,14 +165,16 @@ def mel_to_hz(mel):
     return weights.astype(np.float32)
 
 
-def _reference_mel(samples: np.ndarray) -> np.ndarray:
+def _reference_mel(
+    samples: np.ndarray, preemph: float = 0.97, log_eps: float = LOG_EPS, norm_eps: float = NORM_EPS
+) -> np.ndarray:
     """NeMo's AudioToMelSpectrogramPreprocessor in eval mode, as liquid-audio ships it, in numpy.
 
     Pre-emphasis, constant center padding, symmetric Hann window, power spectrum, Slaney mel filter
     bank, log with the 2^-24 guard, then per-feature normalization over the num_samples // hop
     frames get_seq_len counts as valid, with the frames past that zeroed.
     """
-    preemphasized = np.concatenate([samples[:1], samples[1:] - 0.97 * samples[:-1]])
+    preemphasized = np.concatenate([samples[:1], samples[1:] - preemph * samples[:-1]])
     padded = np.pad(preemphasized, FFT_SIZE // 2)
     num_frames = _num_frames(len(samples))
     window = np.zeros(FFT_SIZE, np.float64)
@@ -181,11 +183,11 @@ def _reference_mel(samples: np.ndarray) -> np.ndarray:
     )
     frames = np.stack([padded[t * HOP_LENGTH : t * HOP_LENGTH + FFT_SIZE] * window for t in range(num_frames)])
     power = np.abs(np.fft.rfft(frames, axis=1)) ** 2
-    log_mel = np.log(power @ _slaney_mel_filterbank().T.astype(np.float64) + LOG_EPS)
+    log_mel = np.log(power @ _slaney_mel_filterbank().T.astype(np.float64) + log_eps)
 
     valid = len(samples) // HOP_LENGTH
     mean = log_mel[:valid].mean(axis=0)
-    std = np.sqrt(((log_mel[:valid] - mean) ** 2).sum(axis=0) / (valid - 1)) + NORM_EPS
+    std = np.sqrt(((log_mel[:valid] - mean) ** 2).sum(axis=0) / (valid - 1)) + norm_eps
     normalized = (log_mel - mean) / std
     normalized[valid:] = 0.0
     return normalized.astype(np.float32)
@@ -349,6 +351,26 @@ def test_lfm2_audio_honors_the_mel_config_overrides(test_data_path, tmp_path, fi
     np.testing.assert_array_equal(inputs["audio_lengths"].as_numpy(), [mel.shape[1]])
 
 
+@pytest.mark.parametrize(
+    "field,value",
+    [("preemph", 0.5), ("log_eps", 1e-4), ("norm_eps", 0.5)],
+)
+def test_lfm2_audio_honors_the_mel_value_overrides(test_data_path, tmp_path, field, value):
+    # These three change the numbers rather than the shape, so a default silently ignoring the
+    # override would look perfectly healthy. Check them against the same arithmetic done here.
+    model_path = _copy_model(test_data_path, tmp_path)
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({field: value}))
+    samples = _synthetic_signal(1.0, seed=12)
+    clip = _write_wav(tmp_path / "clip.wav", samples)
+
+    _, inputs = _process(os.fspath(model_path), AUDIO_MARKER, og.Audios.open(clip))
+    mel = inputs["audio_embeds"].as_numpy()[0]
+
+    np.testing.assert_allclose(mel, _reference_mel(samples, **{field: value}), atol=2e-3)
+    # And it is genuinely a different front end from the default one.
+    assert not np.allclose(mel, _reference_mel(samples), atol=2e-3)
+
+
 def test_lfm2_audio_subsampling_factor_override_changes_the_placeholder_count(test_data_path, tmp_path):
     model_path = _copy_model(test_data_path, tmp_path)
     _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({"subsampling_factor": 4}))
@@ -676,6 +698,64 @@ def test_lfm2_audio_fixture_config_is_what_the_builder_writes(test_data_path):
     assert config["model"]["audio_token_id"] == AUDIO_TOKEN_ID
 
 
+def _two_clip_inputs(model_path, tmp_path):
+    clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(1.5 - 0.5 * i, seed=50 + i)) for i in range(2)]
+    model = og.Model(os.fspath(model_path))
+    inputs = model.create_multimodal_processor()(f"{AUDIO_MARKER} and {AUDIO_MARKER}", audios=og.Audios.open(*clips))
+    return model, inputs
+
+
+def _set_inputs(model, inputs):
+    params = og.GeneratorParams(model)
+    params.set_search_options(do_sample=False, max_length=4096)
+    og.Generator(model, params).set_inputs(inputs)
+
+
+@pytest.mark.parametrize(
+    "corrupt,expected",
+    [
+        (lambda i, lengths: i.__setitem__("audio_embeds", np.zeros((2, 5), np.float32)), "expected a 3D"),
+        (
+            lambda i, lengths: i.__setitem__("audio_embeds", np.zeros((3, 20, NUM_MELS), np.float32)),
+            "holds 3 clips but audio_sizes has 2 entries",
+        ),
+        (lambda i, lengths: i.__setitem__("audio_lengths", np.array([8], np.int64)), "mel_lengths has 1 entries"),
+        # Past the end of the staged tensor: the clip's frames would be copied out of bounds.
+        (
+            lambda i, lengths: i.__setitem__("audio_lengths", np.array([lengths[0], 10**6], np.int64)),
+            r"clip 1 reports 1000000 mel frames, outside the 1\.\.",
+        ),
+        (
+            lambda i, lengths: i.__setitem__("audio_lengths", np.array([lengths[0], 0], np.int64)),
+            r"clip 1 reports 0 mel frames, outside the 1\.\.",
+        ),
+    ],
+    ids=["mel-rank", "clip-count", "lengths-count", "frames-past-the-end", "frames-zero"],
+)
+def test_lfm2_audio_rejects_inconsistent_speech_inputs(test_data_path, tmp_path, corrupt, expected):
+    # The tensors the processor hands back can be replaced before they reach the generator, so the
+    # speech state checks that they still agree with one another. Without these the mismatch would
+    # surface as a confusing shape error from inside the encoder, or as an out-of-bounds read.
+    model, inputs = _two_clip_inputs(_model_path(test_data_path), tmp_path)
+    corrupt(inputs, inputs["audio_lengths"].as_numpy().copy())
+
+    with pytest.raises(RuntimeError, match=expected):
+        _set_inputs(model, inputs)
+
+
+def test_lfm2_audio_several_clips_reject_beam_search(test_data_path, tmp_path):
+    # The clips of one prompt are concatenated into a single feature buffer, which only holds for a
+    # batch of one; a wider buffer would need the whole encoder run repeated per beam.
+    clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(1.5 - 0.5 * i, seed=60 + i)) for i in range(2)]
+    model = og.Model(_model_path(test_data_path))
+    inputs = model.create_multimodal_processor()(f"{AUDIO_MARKER} and {AUDIO_MARKER}", audios=og.Audios.open(*clips))
+    params = og.GeneratorParams(model)
+    params.set_search_options(do_sample=False, max_length=4096, num_beams=2)
+
+    with pytest.raises(RuntimeError, match="need a batch size of 1"):
+        og.Generator(model, params).set_inputs(inputs)
+
+
 def test_lfm2_audio_rejects_rewind(test_data_path, tmp_path):
     clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5))
     generator, _ = _generate(_model_path(test_data_path), f"{AUDIO_MARKER}Hi", og.Audios.open(clip), num_tokens=1)

From cf4ad863029b6f07431de84f8f04eae3191e0018 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 23:25:58 -0400
Subject: [PATCH 11/23] test: pin what happens to audio with more than two
 channels

Went looking for a way to reach the last unreached check, that the
decoder hands back mono, and found a worse problem instead. The decoder
downmixes one and two channels correctly, but deinterleaves anything
wider as though it were stereo: a six channel clip arrives three times
too long and garbled, and transcribes to nonsense. Silently.

It cannot be caught here. Neither setting of the downmix flag reports
how many channels a file had, and nothing else in the decoder's
interface exposes it, so there is no way to tell six channels from two.
The tutorial now says to mix down to mono or stereo first.

The test pins the behaviour rather than blessing it: it asserts stereo
keeps its length and six channels still come out three times too long,
and says in its own message that a fix upstream should make it fail.
---
 examples/python/lfm2-audio-tutorial.md       |  7 +++++++
 test/python/models/test_lfm2_audio_models.py | 21 ++++++++++++++++++++
 2 files changed, 28 insertions(+)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index d4b61de8c7..2c50b504e0 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -347,6 +347,13 @@ stops at `<|audio_start|>` rather than reading the depthformer's positions off t
 clips do not share an encoder run: the published encoder export is traced for a single clip, so each
 one is encoded on its own frames and the results are concatenated in prompt order.
 
+**More than two channels has to be downmixed first.** Mono and stereo are handled: a stereo clip is
+mixed down and reaches the front end at its true length. Wider audio is not — the decoder
+deinterleaves it as though it were stereo, so a six channel clip arrives three times too long and
+garbled, and transcribes to nonsense. Nothing in the decoder's interface reports how many channels a
+file had, so this cannot be caught and refused here. Mix down to mono or stereo before passing the
+file in.
+
 **Audio below 16 kHz is refused.** The decoder resamples down to the encoder's rate and mixes to
 mono, but it does not resample upwards, so 8 kHz telephone audio has to be resampled before it gets
 here. The processor says so, naming the clip.
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index 0d3c2301b4..f011d9788f 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -421,6 +421,27 @@ def test_lfm2_audio_mel_matches_reference(test_data_path, tmp_path):
     assert np.any(mel[-2] != 0)
 
 
+def test_lfm2_audio_more_than_two_channels_is_not_downmixed(test_data_path, tmp_path):
+    # The decoder downmixes one and two channels correctly, but deinterleaves anything wider as
+    # though it were stereo, so a six channel clip arrives three times too long and garbled. Nothing
+    # in the decoder's API reports the channel count, so this cannot be caught here; the tutorial
+    # says to downmix first. This pins the behaviour so a fix upstream shows up as a failure here.
+    samples = _synthetic_signal(0.6, seed=13)
+    frames = {}
+    for channels in (1, 2, 6):
+        clip = _write_wav(tmp_path / f"c{channels}.wav", samples, channels=channels)
+        _, inputs = _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
+        frames[channels] = int(inputs["audio_lengths"].as_numpy()[0])
+
+    assert frames[2] == frames[1], "two channels are downmixed, so the clip keeps its length"
+    # Compare the hops rather than the frames: every clip gets one extra frame from the centre
+    # padding, so only `frames - 1` scales with the length.
+    assert frames[6] - 1 == (frames[1] - 1) * 3, (
+        f"six channels still arrive as {frames[6]} frames against {frames[1]}; if these now match, "
+        "the decoder has learned to downmix and the tutorial's warning can go"
+    )
+
+
 def test_lfm2_audio_downmixes_stereo_to_mono(test_data_path, tmp_path):
     samples = _synthetic_signal(0.8, seed=4)
     mono = _write_wav(tmp_path / "mono.wav", samples)

From 1760d1a9f7f04414cb45ab2dc100baa999fbfe3a Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 23:48:43 -0400
Subject: [PATCH 12/23] fix: survive decoding no tokens, and refuse beam search
 for LFM2-Audio

A TTS prompt stops on its first token, and decoding the nothing that follows
the prompt ended the process with SIGFPE in the detokenizer, for any tokenizer.
Beam search failed as a shape mismatch inside the encoder with one clip, so it
is refused outright. The LFM2-Audio config is now read only once AutoConfig has
refused the checkpoint, and the tutorial's example carries the stop tokens.
---
 examples/python/lfm2-audio-tutorial.md        | 25 ++++++--
 src/models/multi_modal.cpp                    | 15 ++---
 src/models/preprocessing/genai_tokenizer.cpp  |  6 ++
 src/python/py/models/builder.py               |  9 ++-
 src/python/py/models/builders/lfm2.py         | 10 ++--
 test/models/lfm2-audio/genai_config.json      |  6 +-
 test/python/builder/test_lfm2_audio.py        | 59 ++++++++++++++++++-
 test/python/builder/test_qmoe_weights.py      |  2 -
 .../create/create_dummy_lfm2_audio_models.py  |  2 +-
 test/python/models/test_lfm2_audio_models.py  | 32 +++++-----
 test/python/test_onnxruntime_genai_api.py     |  9 +++
 11 files changed, 132 insertions(+), 43 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 2c50b504e0..bc76d8fe2e 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -10,7 +10,7 @@ runs the speech-to-text half of it — ASR and spoken-prompt chat — as three O
 | --- | --- | --- |
 | speech encoder (`audio_encoder.onnx`) | `mel_spectrogram`, `mel_lengths` | `audio_embeddings`, `audio_lengths` |
 | `embeddings.onnx` | `input_ids`, `audio_features` | `inputs_embeds` |
-| `model.onnx` (decoder) | `inputs_embeds`, `attention_mask`, `position_ids`, KV + conv cache | `logits` |
+| `model.onnx` (decoder) | `inputs_embeds`, `attention_mask`, KV + conv cache | `logits` |
 
 Only the decoder is produced by the model builder in this repository; the speech encoder is
 published as ONNX by LiquidAI, and the embedding model is a small graph you build once.
@@ -174,7 +174,7 @@ produced. Leave the `decoder` and `search` sections it wrote alone:
                 "provider_options": []
             }
         },
-        "eos_token_id": 7,
+        "eos_token_id": [7, 128, 130],
         "pad_token_id": 0,
         "speech": {
             "filename": "audio_encoder.onnx",
@@ -198,6 +198,11 @@ produced. Leave the `decoder` and `search` sections it wrote alone:
 }
 ```
 
+Keep the `eos_token_id` list the builder wrote. Besides `<|im_end|>` (7) it holds `<|audio_start|>`
+(128) and `<|text_end|>` (130), where the model turns from text to speech; without them generation
+runs on into positions meant for the audio head and returns fluent nonsense. See
+[the three modes](#the-models-three-modes).
+
 `filename` is whatever the encoder file is called on disk — keep the name it was published under,
 for the reason in [Choose the precisions](#4-choose-the-precisions).
 
@@ -304,7 +309,8 @@ generation ends there with whatever text came first.
 
 **TTS** produces nothing here, and there is no text to be had: asked to speak a sentence, the whole
 text stream the model emits is `<|audio_start|>` followed by `<|im_end|>`, everything in between
-being audio. Generation stops on that first token. A sequential answer that begins in text and turns
+being audio. Generation stops on that first token, and the stop token is not added to the sequence,
+so there is nothing after the prompt to decode: expect an empty string. A sequential answer that begins in text and turns
 to speech part way keeps the text it had before the switch.
 
 **Interleaved** gives the text answer and none of the speech. Asked a spoken question, the model
@@ -336,17 +342,24 @@ produced about three seconds of 24 kHz audio per voice, and this runtime's own A
 that sentence exactly, punctuation included. The two voices are different waveforms, and the male one
 carries more of its energy below 1 kHz than the female one, as it should. Asked the spoken question
 above in interleaved mode, it answered in 41 audio frames ending in the end-of-audio code, 3.2
-seconds that read back as *The capital of France is Paris.* None of it has a path here. ONNX Runtime
-GenAI's generation loop samples one token stream, so none of that has a home here yet. Generation
+seconds that read back as *The capital of France is Paris.* ONNX Runtime GenAI's generation loop
+samples one token stream, so none of that has a home here yet. Generation
 stops at `<|audio_start|>` rather than reading the depthformer's positions off the text head; see
 [the three modes](#the-models-three-modes) for what each prompt gives you. LiquidAI ships
 `vocoder_depthformer.onnx` and `audio_detokenizer.onnx` in the ONNX repository, and their
 [onnx-export](https://github.com/Liquid4All/onnx-export) repository drives them from Python.
 
-**One prompt at a time.** Several clips in one prompt work, but batched prompts are refused. The
+**One prompt at a time, decoded greedily.** Several clips in one prompt work, but batched prompts
+and beam search (`num_beams` above 1) are refused; the reference decodes its text greedily too. The
 clips do not share an encoder run: the published encoder export is traced for a single clip, so each
 one is encoded on its own frames and the results are concatenated in prompt order.
 
+**In ASR mode, two clips in one turn give one transcript.** Asked to transcribe a turn holding two
+clips, the model writes out the last one and leaves the first, whichever order they come in. That is
+the model and not this runtime: the reference implementation, given the same two clips through
+`ChatState.add_audio`, produces the same tokens exactly, in both orders. Transcribe one clip per
+request.
+
 **More than two channels has to be downmixed first.** Mono and stereo are handled: a stereo clip is
 mixed down and reaches the front end at its true length. Wider audio is not — the decoder
 deinterleaves it as though it were stereo, so a six channel clip arrives three times too long and
diff --git a/src/models/multi_modal.cpp b/src/models/multi_modal.cpp
index cc6dbf39a1..a777ac1108 100644
--- a/src/models/multi_modal.cpp
+++ b/src/models/multi_modal.cpp
@@ -604,6 +604,13 @@ DeviceSpan<float> SpeechState::Run(int current_length, DeviceSpan<int32_t>& next
 }
 
 void Lfm2AudioSpeechState::SetExtraInputs(const std::vector<ExtraInput>& extra_inputs, const int64_t num_audio_tokens) {
+  // The feature buffer is one sequence wide, so beams would fail as a shape mismatch inside the
+  // encoder. The reference decodes greedily.
+  if (params_->search.num_beams > 1) {
+    throw std::runtime_error("Lfm2AudioSpeechState: beam search is not supported for lfm2_audio; got num_beams " +
+                             std::to_string(params_->search.num_beams) + ". Set num_beams to 1.");
+  }
+
   SpeechState::SetExtraInputs(extra_inputs, num_audio_tokens);
 
   // audio_sizes holds the decoder tokens each clip contributes; its sum is num_audio_tokens.
@@ -647,13 +654,7 @@ Lfm2AudioSpeechState::SpeechBindings Lfm2AudioSpeechState::ResolveBindings() con
   }
 
   const auto features_info = outputs_[bindings.features_index]->GetTensorTypeAndShapeInfo();
-  const auto features_shape = features_info->GetShape();  // [batch, num_audio_tokens, hidden_size]
-  // Several clips are concatenated into one prompt, which the pipeline only ever builds for a single
-  // sequence; a wider feature buffer would need the whole run repeated per beam.
-  if (features_shape.size() == 3 && features_shape[0] != 1) {
-    throw std::runtime_error("Lfm2AudioSpeechState: several audio clips need a batch size of 1, got " +
-                             std::to_string(features_shape[0]) + ".");
-  }
+  const auto features_shape = features_info->GetShape();  // [1, num_audio_tokens, hidden_size]
   bindings.features_type = features_info->GetElementType();
   bindings.hidden_size = features_shape.back();
   return bindings;
diff --git a/src/models/preprocessing/genai_tokenizer.cpp b/src/models/preprocessing/genai_tokenizer.cpp
index 53309dc134..783c04bf57 100644
--- a/src/models/preprocessing/genai_tokenizer.cpp
+++ b/src/models/preprocessing/genai_tokenizer.cpp
@@ -114,6 +114,12 @@ std::vector<int32_t> Tokenizer::Encode(const char* text) const {
 }
 
 std::string Tokenizer::Decode(std::span<const int32_t> tokens) const {
+  // The detokenizer dies with SIGFPE on an empty sequence, which a generation that stops on its
+  // first token produces.
+  if (tokens.empty()) {
+    return {};
+  }
+
   OrtxPtr<OrtxStringArray> ortx_string_array;
   CheckResult(OrtxDetokenize1D(tokenizer_, reinterpret_cast<const uint32_t*>(tokens.data()), tokens.size(), ortx_string_array.Address()));
 
diff --git a/src/python/py/models/builder.py b/src/python/py/models/builder.py
index 6b2bc0c95d..f2b5ef7c09 100644
--- a/src/python/py/models/builder.py
+++ b/src/python/py/models/builder.py
@@ -102,10 +102,13 @@ def get_hf_details(model_name, input_path, cache_dir, extra_options):
     hf_token = extra_options.get("hf_token", True)
     hf_remote = extra_options.get("hf_remote", False)
 
-    # LFM2-Audio checkpoints have no model_type, so AutoConfig cannot read them: use their nested LFM2 decoder config.
-    config = LFM2AudioModel.load_config(hf_name, token=hf_token, **extra_kwargs)
-    if config is None:
+    try:
         config = AutoConfig.from_pretrained(hf_name, token=hf_token, trust_remote_code=hf_remote, **extra_kwargs)
+    except ValueError:
+        # LFM2-Audio checkpoints have no model_type; their LFM2 decoder config is nested.
+        config = LFM2AudioModel.load_config(hf_name, token=hf_token, **extra_kwargs)
+        if config is None:
+            raise
     tokenizer = AutoTokenizer.from_pretrained(hf_name, token=hf_token, trust_remote_code=hf_remote, **extra_kwargs)
     add_special_token_ids(config, tokenizer)
     if extra_options.get("adapter_path", False):
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index ec793a5fa5..33610263dd 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -210,16 +210,14 @@ def load_config(cls, model_name_or_path, **kwargs):
 
         These checkpoints have no model type and no transformers model class, and their config.json
         nests the decoder config under "lfm" next to the audio encoder, depthformer and mel front-end
-        settings, so AutoConfig cannot read them. The builder calls this before it knows which model
-        class to use, which is why it cannot be an instance method.
+        settings, so AutoConfig cannot read them. The builder calls this once AutoConfig has refused
+        the checkpoint and before it knows which model class to use, which is why it cannot be an
+        instance method.
         """
         from transformers import Lfm2Config, PretrainedConfig
 
         architecture = "Lfm2AudioForConditionalGeneration"
-        try:
-            config_dict, _ = PretrainedConfig.get_config_dict(model_name_or_path, **kwargs)
-        except Exception:  # noqa: BLE001 - not an LFM2-Audio checkpoint, or unreadable: AutoConfig reports the real error
-            return None
+        config_dict, _ = PretrainedConfig.get_config_dict(model_name_or_path, **kwargs)
         if config_dict.get("architectures") != [architecture]:
             return None
 
diff --git a/test/models/lfm2-audio/genai_config.json b/test/models/lfm2-audio/genai_config.json
index 975860af66..9f8d66933e 100644
--- a/test/models/lfm2-audio/genai_config.json
+++ b/test/models/lfm2-audio/genai_config.json
@@ -35,7 +35,11 @@
             ],
             "conv_cache_size": 2
         },
-        "eos_token_id": 7,
+        "eos_token_id": [
+            7,
+            128,
+            130
+        ],
         "pad_token_id": 0,
         "type": "lfm2_audio",
         "vocab_size": 65536,
diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
index 4aabd690aa..ddc9262783 100644
--- a/test/python/builder/test_lfm2_audio.py
+++ b/test/python/builder/test_lfm2_audio.py
@@ -111,10 +111,65 @@ def test_lfm2_audio_load_config_reads_the_nested_decoder_config(tmp_path):
     assert config.tie_word_embeddings
 
 
-def test_lfm2_audio_load_config_leaves_other_checkpoints_to_autoconfig(tmp_path):
+def test_lfm2_audio_load_config_is_none_for_other_checkpoints(tmp_path):
     (tmp_path / "config.json").write_text(json.dumps(DECODER_CONFIG))
     assert LFM2AudioModel.load_config(str(tmp_path)) is None
-    assert LFM2AudioModel.load_config(str(tmp_path / "missing")) is None
+
+
+def _hf_details_with(monkeypatch, auto_config):
+    """`get_hf_details` with AutoConfig and the tokenizer replaced, and the calls it made."""
+    calls = []
+
+    def from_pretrained(name, **kwargs):
+        calls.append("AutoConfig")
+        return auto_config(name)
+
+    def load_config(name, **kwargs):
+        calls.append("load_config")
+        return LFM2AudioModel.load_config(name)
+
+    monkeypatch.setattr(builder_module, "AutoConfig", types.SimpleNamespace(from_pretrained=from_pretrained))
+    monkeypatch.setattr(
+        builder_module, "AutoTokenizer", types.SimpleNamespace(from_pretrained=lambda *args, **kwargs: None)
+    )
+    monkeypatch.setattr(builder_module, "add_special_token_ids", lambda config, tokenizer: None)
+    monkeypatch.setattr(builder_module, "LFM2AudioModel", types.SimpleNamespace(load_config=load_config))
+    return calls
+
+
+def test_lfm2_audio_config_is_read_only_once_autoconfig_refuses_the_checkpoint(monkeypatch, tmp_path):
+    _write_checkpoint(tmp_path)
+
+    def refuse(name):
+        raise ValueError(f"Unrecognized model in {name}. Should have a `model_type` key in its config.json.")
+
+    calls = _hf_details_with(monkeypatch, refuse)
+    details = builder_module.get_hf_details("", str(tmp_path), str(tmp_path / "cache"), {})
+
+    assert calls == ["AutoConfig", "load_config"]
+    assert details["hf_config"].architectures == ["Lfm2AudioForConditionalGeneration"]
+
+
+def test_lfm2_audio_config_lookup_is_skipped_for_every_other_model(monkeypatch, tmp_path):
+    # Every build goes through get_hf_details; other models must not pay for a second config read.
+    recognized = types.SimpleNamespace(architectures=["LlamaForCausalLM"])
+    calls = _hf_details_with(monkeypatch, lambda name: recognized)
+
+    details = builder_module.get_hf_details("", str(tmp_path), str(tmp_path / "cache"), {})
+
+    assert calls == ["AutoConfig"]
+    assert details["hf_config"] is recognized
+
+
+def test_lfm2_audio_config_lookup_keeps_autoconfig_error_for_an_unknown_checkpoint(monkeypatch, tmp_path):
+    (tmp_path / "config.json").write_text(json.dumps({"architectures": ["SomethingElse"]}))
+
+    def refuse(name):
+        raise ValueError("Unrecognized model")
+
+    _hf_details_with(monkeypatch, refuse)
+    with pytest.raises(ValueError, match="Unrecognized model"):
+        builder_module.get_hf_details("", str(tmp_path), str(tmp_path / "cache"), {})
 
 
 def _audio_builder(tmp_path, extra_options=None, model_name_or_path=None):
diff --git a/test/python/builder/test_qmoe_weights.py b/test/python/builder/test_qmoe_weights.py
index 2f0866fc9b..c2216934a4 100644
--- a/test/python/builder/test_qmoe_weights.py
+++ b/test/python/builder/test_qmoe_weights.py
@@ -159,8 +159,6 @@ def _load_builder_cli_module(monkeypatch):
         "WhisperModel",
     ):
         setattr(builders_module, class_name, type(class_name, (), {}))
-    # builder.py calls this before the dispatch, to spot an LFM2-Audio checkpoint.
-    builders_module.LFM2AudioModel.load_config = lambda *args, **kwargs: None
     # Submodule imports (e.g. `from quantization import ...`) must resolve to the
     # real, dependency-free modules rather than the class stubs above.
     builders_module.__path__ = [str(BUILDERS_DIR)]
diff --git a/test/python/create/create_dummy_lfm2_audio_models.py b/test/python/create/create_dummy_lfm2_audio_models.py
index 31321ea825..658775f598 100644
--- a/test/python/create/create_dummy_lfm2_audio_models.py
+++ b/test/python/create/create_dummy_lfm2_audio_models.py
@@ -162,7 +162,7 @@ def create_genai_config(output_path: str):
                 "layer_types": LAYER_TYPES,
                 "conv_cache_size": CONV_CACHE_SIZE,
             },
-            "eos_token_id": 7,
+            "eos_token_id": [7, 128, 130],  # <|im_end|>, <|audio_start|>, <|text_end|>: what the builder writes
             "pad_token_id": 0,
             "type": "lfm2_audio",
             "vocab_size": VOCAB_SIZE,
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index f011d9788f..68a673f35a 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -61,10 +61,13 @@
 
 
 # The tokenizer is checked in zipped: its 4.8 MB of vocabulary compresses to under 1 MB, and no test
-# reads it as text. Refresh it with, from the model directory,
-#   python -c "import zipfile; \
-#              [zipfile.ZipFile('tokenizer.zip', 'w').writestr(n, open(n, 'rb').read()) for n in NAMES]"
-# or take a fresh copy from LiquidAI/LFM2.5-Audio-1.5B.
+# reads it as text. The files come from LiquidAI/LFM2.5-Audio-1.5B. To rebuild the same bytes:
+#   import zipfile
+#   with zipfile.ZipFile("tokenizer.zip", "w", zipfile.ZIP_DEFLATED, compresslevel=9) as archive:
+#       for name in ("tokenizer.json", "tokenizer_config.json"):
+#           info = zipfile.ZipInfo(name, date_time=(1980, 1, 1, 0, 0, 0))
+#           info.compress_type, info.external_attr = zipfile.ZIP_DEFLATED, 0o644 << 16
+#           archive.writestr(info, open(name, "rb").read(), compresslevel=9)
 TOKENIZER_ARCHIVE = "tokenizer.zip"
 TOKENIZER_FILES = ("tokenizer.json", "tokenizer_config.json")
 
@@ -658,6 +661,8 @@ def test_lfm2_audio_features_follow_the_clip_order(test_data_path, tmp_path):
 
 
 def _generate_with_eos(model_path, clip, eos, num_tokens):
+    """Greedy generation from a copied model directory, stopping on the token ids in `eos`."""
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({"eos_token_id": eos}))
     model = og.Model(os.fspath(model_path))
     processor = model.create_multimodal_processor()
     inputs = processor(AUDIO_MARKER, audios=og.Audios.open(clip))
@@ -686,7 +691,6 @@ def test_lfm2_audio_generation_stops_when_the_model_starts_speaking(test_data_pa
 
     stop_token = unrestricted[5]
     first = unrestricted.index(stop_token)
-    _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({"eos_token_id": [stop_token]}))
 
     stopped = _generate_with_eos(model_path, clip, [stop_token], num_tokens=40)
 
@@ -701,10 +705,6 @@ def test_lfm2_audio_modality_stops_leave_normal_stopping_alone(test_data_path, t
     switches = [AUDIO_START_TOKEN_ID, TEXT_END_TOKEN_ID]
 
     without = _generate_with_eos(model_path, clip, [7], num_tokens=25)
-    _edit_json(
-        model_path / "genai_config.json",
-        lambda config: config["model"].update({"eos_token_id": [7, *switches]}),
-    )
     with_switch_stops = _generate_with_eos(model_path, clip, [7, *switches], num_tokens=25)
 
     assert not set(switches) & set(without), "the fixture is not expected to emit the switch tokens"
@@ -717,6 +717,7 @@ def test_lfm2_audio_fixture_config_is_what_the_builder_writes(test_data_path):
     config = json.loads((Path(_model_path(test_data_path)) / "genai_config.json").read_text())
     assert config["model"]["type"] == "lfm2_audio"
     assert config["model"]["audio_token_id"] == AUDIO_TOKEN_ID
+    assert config["model"]["eos_token_id"] == [7, AUDIO_START_TOKEN_ID, TEXT_END_TOKEN_ID]
 
 
 def _two_clip_inputs(model_path, tmp_path):
@@ -764,16 +765,17 @@ def test_lfm2_audio_rejects_inconsistent_speech_inputs(test_data_path, tmp_path,
         _set_inputs(model, inputs)
 
 
-def test_lfm2_audio_several_clips_reject_beam_search(test_data_path, tmp_path):
-    # The clips of one prompt are concatenated into a single feature buffer, which only holds for a
-    # batch of one; a wider buffer would need the whole encoder run repeated per beam.
-    clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(1.5 - 0.5 * i, seed=60 + i)) for i in range(2)]
+@pytest.mark.parametrize("num_clips", [0, 1, 2], ids=["text", "one-clip", "two-clips"])
+def test_lfm2_audio_rejects_beam_search(test_data_path, tmp_path, num_clips):
+    # Without the check a single clip fails with a shape mismatch from inside the encoder.
+    clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(1.5 - 0.5 * i, seed=60 + i)) for i in range(num_clips)]
     model = og.Model(_model_path(test_data_path))
-    inputs = model.create_multimodal_processor()(f"{AUDIO_MARKER} and {AUDIO_MARKER}", audios=og.Audios.open(*clips))
+    prompt = "Hi " + " and ".join([AUDIO_MARKER] * num_clips)
+    inputs = model.create_multimodal_processor()(prompt, audios=og.Audios.open(*clips) if clips else None)
     params = og.GeneratorParams(model)
     params.set_search_options(do_sample=False, max_length=4096, num_beams=2)
 
-    with pytest.raises(RuntimeError, match="need a batch size of 1"):
+    with pytest.raises(RuntimeError, match="beam search is not supported for lfm2_audio; got num_beams 2"):
         og.Generator(model, params).set_inputs(inputs)
 
 
diff --git a/test/python/test_onnxruntime_genai_api.py b/test/python/test_onnxruntime_genai_api.py
index 4f0f851f78..e860f74b75 100644
--- a/test/python/test_onnxruntime_genai_api.py
+++ b/test/python/test_onnxruntime_genai_api.py
@@ -67,6 +67,15 @@ def test_tokenizer_create_from_config_and_path(test_data_path):
     assert tokenizer_from_path.decode(tokenizer_from_config.encode(text)) == text
 
 
+def test_tokenizer_decodes_no_tokens_to_an_empty_string(test_data_path):
+    # What is left after the prompt when generation stops on its first token; it used to SIGFPE.
+    model_path = os.fspath(Path(test_data_path) / "models" / "hf-internal-testing" / "tiny-random-gpt2-fp32")
+    tokenizer = og.Tokenizer(model_path)
+
+    assert tokenizer.decode(np.array([], dtype=np.int32)) == ""
+    assert tokenizer.decode(np.arange(4, dtype=np.int32)[4:]) == ""
+
+
 def test_telemetry_control():
     og.disable_telemetry_events()
     og.enable_telemetry_events()

From 04f8921defd5430bdc8638f373a1d6ad4524b7ea Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Sun, 20 Sep 2026 23:53:58 -0400
Subject: [PATCH 13/23] fix: refuse LoRA adapters for LFM2-Audio instead of
 misapplying them

A fine-tune of these checkpoints keys its adapter "lfm.*", which matches nothing
in the Lfm2ForCausalLM the builder loads. PEFT only warns, so the export carried
untrained LoRA weights and the build still succeeded. The shared load_adapter
step existed only to reach that path, so base.py goes back to what main has.
---
 examples/python/lfm2-audio-tutorial.md |  4 ++++
 src/python/py/models/builders/base.py  |  7 ------
 src/python/py/models/builders/lfm2.py  |  7 +++++-
 test/python/builder/test_lfm2_audio.py | 32 ++++----------------------
 4 files changed, 15 insertions(+), 35 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index bc76d8fe2e..241b04bd56 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -360,6 +360,10 @@ the model and not this runtime: the reference implementation, given the same two
 `ChatState.add_audio`, produces the same tokens exactly, in both orders. Transcribe one clip per
 request.
 
+**LoRA adapters are not supported.** The builder refuses `adapter_path` for these checkpoints: an
+adapter trained on them names the decoder `lfm.*`, which does not match the decoder the builder
+loads. Merge the adapter into the checkpoint first.
+
 **More than two channels has to be downmixed first.** Mono and stereo are handled: a stereo clip is
 mixed down and reaches the front end at its true length. Wider audio is not — the decoder
 deinterleaves it as though it were stereo, so a six channel clip arrives three times too long and
diff --git a/src/python/py/models/builders/base.py b/src/python/py/models/builders/base.py
index 26515544df..9763ca12c6 100644
--- a/src/python/py/models/builders/base.py
+++ b/src/python/py/models/builders/base.py
@@ -5958,13 +5958,6 @@ def load_weights(self, input_path):
                 **extra_kwargs,
             )
 
-        return self.load_adapter(model)
-
-    def load_adapter(self, model):
-        """Wraps a loaded PyTorch model with the LoRA adapter named by `adapter_path`, if any.
-
-        Every `load_weights` override has to end with this, or the adapter is silently dropped.
-        """
         if "adapter_path" in self.extra_options:
             from peft import PeftModel
 
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index 33610263dd..a87a1766a4 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -245,6 +245,11 @@ def update_genai_config(self, genai_config):
         genai_config["model"]["eos_token_id"] = eos_token_ids
 
     def load_weights(self, input_path):
+        # A fine-tune of these checkpoints names the decoder "lfm.*", which matches nothing in the
+        # Lfm2ForCausalLM loaded here: PEFT only warns, and the export would carry untrained LoRA weights.
+        if "adapter_path" in self.extra_options:
+            raise ValueError("LoRA adapters are not supported for LFM2-Audio checkpoints; merge the adapter first.")
+
         if input_path.endswith(".gguf") or self.quant_type is not None:
             return super().load_weights(input_path)
 
@@ -262,7 +267,7 @@ def load_weights(self, input_path):
                 f"The LFM2 decoder of {input_path} does not match its config: missing {sorted(missing)}, unexpected {sorted(unexpected)}."
             )
         model.tie_weights()
-        return self.load_adapter(model.eval())
+        return model.eval()
 
     def load_lfm2_audio_checkpoint(self, input_path):
         """The checkpoint tensors, from a local directory or the Hugging Face Hub."""
diff --git a/test/python/builder/test_lfm2_audio.py b/test/python/builder/test_lfm2_audio.py
index ddc9262783..bac381a762 100644
--- a/test/python/builder/test_lfm2_audio.py
+++ b/test/python/builder/test_lfm2_audio.py
@@ -269,36 +269,14 @@ def fail(*args, **kwargs):
     assert type(loaded).__name__ == "Lfm2ForCausalLM"
 
 
-def test_lfm2_audio_load_weights_applies_the_lora_adapter(monkeypatch, tmp_path):
-    # `load_weights` is overridden for these checkpoints, so it has to apply `adapter_path` itself;
-    # returning the bare decoder would drop the adapter without a word.
+def test_lfm2_audio_load_weights_refuses_a_lora_adapter(tmp_path):
+    # An adapter trained on these checkpoints is keyed "lfm.*" and matches nothing in the decoder
+    # loaded here, so PEFT would attach untrained weights with only a warning.
     _write_checkpoint(tmp_path)
-    wrapped = []
-
-    class FakePeftModel:
-        @classmethod
-        def from_pretrained(cls, model, adapter_path, **kwargs):
-            wrapped.append((model, adapter_path, kwargs))
-            return "adapted"
-
-    peft = types.ModuleType("peft")
-    peft.PeftModel = FakePeftModel
-    monkeypatch.setitem(sys.modules, "peft", peft)
-
     builder = _audio_builder(tmp_path, {"adapter_path": "some/adapter"})
-    loaded = builder.load_weights(str(tmp_path))
-
-    assert loaded == "adapted"
-    model, adapter_path, kwargs = wrapped[0]
-    assert type(model).__name__ == "Lfm2ForCausalLM"
-    assert adapter_path == "some/adapter"
-    assert kwargs == {"cache_dir": builder.cache_dir, "token": True}
 
-
-def test_lfm2_audio_load_weights_without_an_adapter_returns_the_decoder(tmp_path):
-    _write_checkpoint(tmp_path)
-    loaded = _audio_builder(tmp_path).load_weights(str(tmp_path))
-    assert type(loaded).__name__ == "Lfm2ForCausalLM"
+    with pytest.raises(ValueError, match="LoRA adapters are not supported for LFM2-Audio"):
+        builder.load_weights(str(tmp_path))
 
 
 @pytest.mark.parametrize(

From 3b655e5bfef9188c76c942002580bb6c587c6a19 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 00:09:45 -0400
Subject: [PATCH 14/23] fix: resample LFM2-Audio clips the way the reference
 does

The audio decoder's resampler is a different filter from the torchaudio one
liquid-audio uses: on the reference's own 24 kHz Japanese clip the log-mel frames
differed by up to 2 and one homophone came out differently. Clips are now decoded
at their own rate and resampled with a port of torchaudio's windowed sinc, which
matches token for token and also takes audio below 16 kHz.
---
 examples/python/lfm2-audio-tutorial.md        | 13 ++-
 .../preprocessing/lfm2_audio_processor.cpp    | 80 ++++++++++++++++---
 .../preprocessing/lfm2_audio_processor.h      |  5 ++
 test/cpp/lfm2_audio_frames_test.cpp           | 30 +++++++
 test/python/models/test_lfm2_audio_models.py  | 78 +++++++++++++-----
 5 files changed, 167 insertions(+), 39 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 241b04bd56..ec72f8c58e 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -301,6 +301,9 @@ routine of its own. Only the first produces text alone, so only the first runs h
 
 **ASR** is the supported path, and matches the reference token for token.
 
+`LFM2.5-Audio-1.5B-JP` takes its own prompts, `Perform ASR in japanese.` and
+`Perform TTS in japanese.`; the rest of this section applies to it unchanged.
+
 Two tokens mark the turn passing from text to speech, and the builder puts both in `eos_token_id`:
 `<|audio_start|>` when the rest of the answer is spoken, and `<|text_end|>` when the text half of an
 interleaved answer is done. The positions after either are audio codes meant for the depthformer this
@@ -371,10 +374,6 @@ garbled, and transcribes to nonsense. Nothing in the decoder's interface reports
 file had, so this cannot be caught and refused here. Mix down to mono or stereo before passing the
 file in.
 
-**Audio below 16 kHz is refused.** The decoder resamples down to the encoder's rate and mixes to
-mono, but it does not resample upwards, so 8 kHz telephone audio has to be resampled before it gets
-here. The processor says so, naming the clip.
-
 **Audio is not chunked.** The whole clip goes through the encoder in one run, and one hour of audio
 is 450k mel frames, so memory grows with the clip length. The reference implementation has the same
 shape; for long-form transcription, split the audio yourself.
@@ -383,5 +382,11 @@ shape; for long-form transcription, split the audio yourself.
 float32 where the reference uses float64 intermediates in places, and the FFT is a different
 implementation. The difference is far below the audio's own quantization noise.
 
+**Resampling is the reference's.** A clip at any other rate is decoded at its own rate and brought to
+16 kHz with `torchaudio`'s resampler (Hann-windowed sinc), upwards as well as downwards, which is what
+`ChatState.add_audio` does. The audio decoder's own resampler is a different filter and changes the
+log-mel frames by up to about 2, enough to flip a token: on the reference's 24 kHz `asr_jp.wav` it
+turned one homophone, and resampled this way the transcript matches token for token.
+
 **Dither is off, as in the reference's eval mode.** NeMo adds 1e-5 of white noise to the samples
 during training only; the runtime never does.
diff --git a/src/models/preprocessing/lfm2_audio_processor.cpp b/src/models/preprocessing/lfm2_audio_processor.cpp
index e9e00772d5..5c9f578360 100644
--- a/src/models/preprocessing/lfm2_audio_processor.cpp
+++ b/src/models/preprocessing/lfm2_audio_processor.cpp
@@ -10,6 +10,7 @@
 #include <algorithm>
 #include <cmath>
 #include <cstring>
+#include <numeric>
 
 namespace Generators {
 
@@ -91,6 +92,60 @@ int64_t Lfm2AudioNumTokens(int64_t num_mel_frames, int64_t subsampling_factor) {
   return (num_mel_frames + subsampling_factor - 1) / subsampling_factor;
 }
 
+std::vector<float> ResampleLfm2Audio(const float* pcm, int64_t num_samples, int64_t source_rate, int64_t target_rate) {
+  if (source_rate <= 0 || target_rate <= 0) {
+    throw std::runtime_error("Lfm2AudioProcessor: cannot resample from " + std::to_string(source_rate) + " Hz to " +
+                             std::to_string(target_rate) + " Hz.");
+  }
+  if (source_rate == target_rate) {
+    return {pcm, pcm + num_samples};
+  }
+
+  constexpr double kPi = 3.14159265358979323846;
+  constexpr double kLowpassFilterWidth = 6.0;
+  constexpr double kRolloff = 0.99;
+  const int64_t divisor = std::gcd(source_rate, target_rate);
+  const int64_t orig = source_rate / divisor;
+  const int64_t target = target_rate / divisor;
+  const double base_freq = static_cast<double>(std::min(orig, target)) * kRolloff;
+  const int64_t width = static_cast<int64_t>(std::ceil(kLowpassFilterWidth * static_cast<double>(orig) / base_freq));
+  const int64_t kernel_size = 2 * width + orig;
+
+  // One kernel per output phase, built in double and stored in float as torchaudio does.
+  std::vector<float> kernels(static_cast<size_t>(target * kernel_size));
+  for (int64_t phase = 0; phase < target; ++phase) {
+    for (int64_t k = 0; k < kernel_size; ++k) {
+      double t = (-static_cast<double>(phase) / static_cast<double>(target) +
+                  static_cast<double>(k - width) / static_cast<double>(orig)) *
+                 base_freq;
+      t = std::clamp(t, -kLowpassFilterWidth, kLowpassFilterWidth);
+      const double window = std::pow(std::cos(t * kPi / kLowpassFilterWidth / 2.0), 2.0);
+      t *= kPi;
+      const double sinc = t == 0.0 ? 1.0 : std::sin(t) / t;
+      kernels[static_cast<size_t>(phase * kernel_size + k)] =
+          static_cast<float>(sinc * window * base_freq / static_cast<double>(orig));
+    }
+  }
+
+  std::vector<float> padded(static_cast<size_t>(width + num_samples + width + orig), 0.0f);
+  std::copy(pcm, pcm + num_samples, padded.begin() + width);
+
+  // ceil(target * num_samples / orig) samples; the strided convolution makes a few more, which
+  // torchaudio trims.
+  const int64_t num_outputs = (target * num_samples + orig - 1) / orig;
+  std::vector<float> resampled(static_cast<size_t>(num_outputs));
+  for (int64_t i = 0; i < num_outputs; ++i) {
+    const float* frame = padded.data() + (i / target) * orig;
+    const float* kernel = kernels.data() + (i % target) * kernel_size;
+    float value = 0.0f;
+    for (int64_t k = 0; k < kernel_size; ++k) {
+      value += frame[k] * kernel[k];
+    }
+    resampled[static_cast<size_t>(i)] = value;
+  }
+  return resampled;
+}
+
 std::vector<float> ComputeLfm2AudioMel(const float* pcm, int64_t num_samples, const Lfm2AudioMelConfig& config,
                                        int64_t& num_frames) {
   if (config.num_mels <= 0 || config.fft_size <= 0 || config.hop_length <= 0 || config.win_length < 2 ||
@@ -227,19 +282,11 @@ Lfm2AudioProcessor::Lfm2AudioProcessor(Config& config, const SessionInfo& sessio
 }
 
 Lfm2AudioProcessor::ClipMel Lfm2AudioProcessor::ComputeClipMel(const Audios& audios, size_t index) const {
+  // Decoded at the file's own rate and resampled here, the way the reference does it: the decoder's
+  // resampler is a different filter, enough to change a token, and it cannot resample upwards.
   ort_extensions::OrtxObjectPtr<OrtxTensorResult> decoded;
-  // The decoder resamples down to the encoder's rate and mixes to mono, but it cannot upsample:
-  // say which clip it was and what to do about it, rather than passing its bare message through.
-  try {
-    CheckResult(OrtxDecodeAudio(audios.audios_.get(), index, static_cast<int64_t>(mel_config_.sample_rate),
-                                /*stereo_to_mono=*/1, decoded.ToBeAssigned()));
-  } catch (const std::exception& e) {
-    throw std::runtime_error("Lfm2AudioProcessor: could not decode audio clip " + std::to_string(index) + " at " +
-                             std::to_string(mel_config_.sample_rate) +
-                             " Hz. A clip recorded below that rate has to be resampled up to it first, as the "
-                             "decoder only resamples downwards. The decoder reported: " +
-                             e.what());
-  }
+  CheckResult(OrtxDecodeAudio(audios.audios_.get(), index, /*target_sample_rate=*/0, /*stereo_to_mono=*/1,
+                              decoded.ToBeAssigned()));
 
   ort_extensions::OrtxObjectPtr<OrtxTensor> pcm_tensor;
   CheckResult(OrtxTensorResultGetAt(decoded.get(), 0, pcm_tensor.ToBeAssigned()));
@@ -258,8 +305,15 @@ Lfm2AudioProcessor::ClipMel Lfm2AudioProcessor::ComputeClipMel(const Audios& aud
                              std::to_string(index) + ", got a rank " + std::to_string(pcm_dims) + " tensor.");
   }
 
+  ort_extensions::OrtxObjectPtr<OrtxTensor> rate_tensor;
+  CheckResult(OrtxTensorResultGetAt(decoded.get(), 1, rate_tensor.ToBeAssigned()));
+  const int64_t* source_rate{};
+  CheckResult(OrtxGetTensorData(rate_tensor.get(), reinterpret_cast<const void**>(&source_rate), nullptr, nullptr));
+
+  const std::vector<float> samples = ResampleLfm2Audio(pcm, num_samples, *source_rate, mel_config_.sample_rate);
+
   ClipMel clip;
-  clip.mel = ComputeLfm2AudioMel(pcm, num_samples, mel_config_, clip.num_frames);
+  clip.mel = ComputeLfm2AudioMel(samples.data(), static_cast<int64_t>(samples.size()), mel_config_, clip.num_frames);
   clip.num_tokens = Lfm2AudioNumTokens(clip.num_frames, subsampling_factor_);
   return clip;
 }
diff --git a/src/models/preprocessing/lfm2_audio_processor.h b/src/models/preprocessing/lfm2_audio_processor.h
index da9b2a8651..e42d4d611a 100644
--- a/src/models/preprocessing/lfm2_audio_processor.h
+++ b/src/models/preprocessing/lfm2_audio_processor.h
@@ -40,6 +40,11 @@ int64_t Lfm2AudioNumValidMelFrames(int64_t num_samples, const Lfm2AudioMelConfig
 // with ceil rounding (three stride-2 convolutions with padding 1 for the shipped models).
 int64_t Lfm2AudioNumTokens(int64_t num_mel_frames, int64_t subsampling_factor);
 
+// torchaudio.functional.resample with its defaults (Hann-windowed sinc, lowpass_filter_width 6,
+// rolloff 0.99), which is what the reference's ChatState.add_audio brings every clip to the
+// encoder's rate with, upwards as well as downwards.
+std::vector<float> ResampleLfm2Audio(const float* pcm, int64_t num_samples, int64_t source_rate, int64_t target_rate);
+
 // Computes the normalized log-mel spectrogram of one mono clip, time-major [num_frames, num_mels],
 // the layout the encoder's mel_spectrogram input takes. Throws if the clip is too short to
 // normalize (fewer than two valid frames).
diff --git a/test/cpp/lfm2_audio_frames_test.cpp b/test/cpp/lfm2_audio_frames_test.cpp
index a03978fb53..69067c887f 100644
--- a/test/cpp/lfm2_audio_frames_test.cpp
+++ b/test/cpp/lfm2_audio_frames_test.cpp
@@ -154,4 +154,34 @@ TEST(Lfm2AudioPromptTest, RejectsAMarkerCountThatDoesNotMatchTheClips) {
             std::string::npos);
 }
 
+TEST(Lfm2AudioResampleTest, SameRateIsACopy) {
+  const std::vector<float> samples{0.1f, -0.2f, 0.3f};
+  EXPECT_EQ(ResampleLfm2Audio(samples.data(), 3, 16000, 16000), samples);
+}
+
+TEST(Lfm2AudioResampleTest, LengthIsTheCeilingOfTheRateRatio) {
+  // torchaudio trims to ceil(target * num_samples / source), up as well as down.
+  const std::vector<float> samples(1001, 0.0f);
+  EXPECT_EQ(ResampleLfm2Audio(samples.data(), 1001, 44100, 16000).size(), 364u);
+  EXPECT_EQ(ResampleLfm2Audio(samples.data(), 1001, 48000, 16000).size(), 334u);
+  EXPECT_EQ(ResampleLfm2Audio(samples.data(), 1001, 8000, 16000).size(), 2002u);
+}
+
+TEST(Lfm2AudioResampleTest, KeepsAConstantSignalConstantAwayFromTheEdges) {
+  // The kernels of every output phase sum to one, so only the zero padding at the ends shows.
+  const std::vector<float> samples(4410, 0.5f);
+  for (const int64_t source_rate : {8000, 22050, 44100}) {
+    const auto resampled = ResampleLfm2Audio(samples.data(), 4410, source_rate, 16000);
+    for (size_t i = resampled.size() / 4; i < 3 * resampled.size() / 4; ++i) {
+      ASSERT_NEAR(resampled[i], 0.5f, 2e-3f) << source_rate << " Hz, sample " << i;
+    }
+  }
+}
+
+TEST(Lfm2AudioResampleTest, RejectsARateThatIsNotPositive) {
+  const std::vector<float> samples(16, 0.0f);
+  EXPECT_NE(CaptureThrowMessage([&] { ResampleLfm2Audio(samples.data(), 16, 0, 16000); }).find("cannot resample"),
+            std::string::npos);
+}
+
 }  // namespace Generators::test
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index 68a673f35a..75bea66c23 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -196,6 +196,33 @@ def _reference_mel(
     return normalized.astype(np.float32)
 
 
+def _reference_resample(samples: np.ndarray, source_rate: int, target_rate: int = SAMPLE_RATE) -> np.ndarray:
+    """torchaudio.functional.resample with its defaults, which liquid-audio's ChatState.add_audio uses.
+
+    A Hann-windowed sinc with lowpass_filter_width 6 and rolloff 0.99: one kernel per output phase,
+    built in float64 and applied in float32, strided over the zero-padded input.
+    """
+    if source_rate == target_rate:
+        return samples
+    divisor = math.gcd(source_rate, target_rate)
+    orig, new = source_rate // divisor, target_rate // divisor
+    base_freq = min(orig, new) * 0.99
+    width = math.ceil(6 * orig / base_freq)
+    idx = np.arange(-width, width + orig, dtype=np.float64) / orig
+    t = (np.arange(0, -new, -1, dtype=np.float64)[:, None] / new + idx[None, :]) * base_freq
+    t = t.clip(-6, 6)
+    window = np.cos(t * np.pi / 6 / 2) ** 2
+    t *= np.pi
+    kernels = (np.where(t == 0, 1.0, np.sin(t) / np.where(t == 0, 1.0, t)) * window * base_freq / orig).astype(
+        np.float32
+    )
+
+    padded = np.pad(samples.astype(np.float32), (width, width + orig))
+    steps = len(samples) // orig + 1
+    frames = np.lib.stride_tricks.sliding_window_view(padded, 2 * width + orig)[::orig][:steps]
+    return (frames @ kernels.T).reshape(-1)[: math.ceil(new * len(samples) / orig)]
+
+
 def _process(model_path: str, prompt: str, audios):
     model = og.Model(model_path)
     inputs = model.create_multimodal_processor()(prompt, audios=audios)
@@ -298,34 +325,41 @@ def test_lfm2_audio_open_bytes_matches_open(test_data_path, tmp_path):
     np.testing.assert_array_equal(from_bytes["audio_sizes"].as_numpy(), from_path["audio_sizes"].as_numpy())
 
 
-@pytest.mark.parametrize("source_rate", [22050, 44100, 48000])
-def test_lfm2_audio_resamples_to_the_encoder_rate(test_data_path, tmp_path, source_rate):
-    # Whatever the file's rate, the clip reaches the mel front end at the encoder's 16 kHz, so the
-    # frame count follows the resampled length rather than the original sample count.
-    seconds = 0.75
-    samples = _synthetic_signal(seconds, seed=8)[: int(seconds * source_rate)]
-    if source_rate > SAMPLE_RATE:  # _synthetic_signal only makes 16 kHz worth of samples
-        samples = np.interp(
-            np.linspace(0, 1, int(seconds * source_rate)), np.linspace(0, 1, samples.size), samples
-        ).astype(np.float32)
+def _signal_at(source_rate: int, seconds: float, seed: int) -> np.ndarray:
+    """`_synthetic_signal` stretched to another rate, quantized to 16-bit like the WAV that carries it."""
+    base = _synthetic_signal(seconds, seed=seed)
+    stretched = np.interp(np.linspace(0, 1, int(seconds * source_rate)), np.linspace(0, 1, base.size), base)
+    return (np.round(stretched * 32768.0).clip(-32768, 32767) / 32768.0).astype(np.float32)
+
+
+@pytest.mark.parametrize("source_rate", [8000, 22050, 24000, 44100, 48000])
+def test_lfm2_audio_resamples_the_way_the_reference_does(test_data_path, tmp_path, source_rate):
+    # The clip is decoded at its own rate and brought to the encoder's 16 kHz with torchaudio's
+    # resampler, upwards as well as downwards, as ChatState.add_audio does. The decoder's own
+    # resampler is a different filter: near enough to transcribe, far enough to change a token.
+    samples = _signal_at(source_rate, 0.75, seed=8)
     clip = _write_wav(tmp_path / "clip.wav", samples, sample_rate=source_rate)
 
     _, inputs = _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
 
-    resampled_length = round(samples.size * SAMPLE_RATE / source_rate)
-    num_frames = int(inputs["audio_lengths"].as_numpy()[0])
-    # The resampler may land a sample either side of the exact ratio, so allow one hop of slack.
-    assert abs(num_frames - _num_frames(resampled_length)) <= 1, f"{num_frames} frames for {resampled_length} samples"
-    assert inputs["audio_embeds"].as_numpy().shape == (1, num_frames, NUM_MELS)
-    assert int(inputs["audio_sizes"].as_numpy()[0]) == math.ceil(num_frames / SUBSAMPLING_FACTOR)
+    resampled = _reference_resample(samples, source_rate)
+    assert len(resampled) == math.ceil(len(samples) * SAMPLE_RATE / source_rate)
+    np.testing.assert_array_equal(inputs["audio_lengths"].as_numpy(), [_num_frames(len(resampled))])
+    np.testing.assert_array_equal(inputs["audio_sizes"].as_numpy(), [_num_tokens(len(resampled))])
+    np.testing.assert_allclose(inputs["audio_embeds"].as_numpy()[0], _reference_mel(resampled), atol=2e-3)
 
 
-def test_lfm2_audio_rejects_a_clip_recorded_below_the_encoder_rate(test_data_path, tmp_path):
-    # The decoder resamples downwards only, so 8 kHz telephone audio cannot be read as 16 kHz. The
-    # message has to name the clip and say what to do, not just repeat the decoder's complaint.
-    clip = _write_wav(tmp_path / "telephone.wav", _synthetic_signal(0.5, seed=11)[:4000], sample_rate=8000)
-    with pytest.raises(RuntimeError, match="could not decode audio clip 0 at 16000 Hz.*resampled up"):
-        _process(_model_path(test_data_path), AUDIO_MARKER, og.Audios.open(clip))
+@pytest.mark.parametrize("source_rate", [8000, 44100])
+def test_lfm2_audio_reference_resampler_is_torchaudio(source_rate):
+    """The numpy resampler above against the real one, when torchaudio is installed."""
+    torch = pytest.importorskip("torch")
+    torchaudio = pytest.importorskip("torchaudio")
+    samples = _signal_at(source_rate, 0.5, seed=14)
+
+    expected = torchaudio.functional.resample(torch.from_numpy(samples)[None], source_rate, SAMPLE_RATE)[0].numpy()
+
+    # Float32 sums taken in a different order.
+    np.testing.assert_allclose(_reference_resample(samples, source_rate), expected, atol=1e-5)
 
 
 @pytest.mark.parametrize(

From e9143f8b4a65bb5d5d0a311ea3662da65a740689 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 01:01:37 -0400
Subject: [PATCH 15/23] docs: interleaved mode is not usable here, measured
 against the reference

Step-by-step text logits against liquid-audio: ASR and plain chat agree to 6e-4
and every token matches on the reference's own question.wav and typed questions;
TTS matches at its single text step in all four voices. Interleaved matches for
the six tokens before the reference first feeds audio back, then degenerates into
the non-breaking-space token on three of four questions, so the tutorial now says
to leave the system prompt out for a text answer instead.
---
 examples/python/lfm2-audio-tutorial.md | 48 +++++++++++++-------------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index ec72f8c58e..43ca267af8 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -201,7 +201,7 @@ produced. Leave the `decoder` and `search` sections it wrote alone:
 Keep the `eos_token_id` list the builder wrote. Besides `<|im_end|>` (7) it holds `<|audio_start|>`
 (128) and `<|text_end|>` (130), where the model turns from text to speech; without them generation
 runs on into positions meant for the audio head and returns fluent nonsense. See
-[the three modes](#the-models-three-modes).
+[the modes](#the-models-modes).
 
 `filename` is whatever the encoder file is called on disk — keep the name it was published under,
 for the reason in [Choose the precisions](#4-choose-the-precisions).
@@ -288,18 +288,21 @@ Perform ASR.<|im_end|>
 <|im_start|>assistant
 ```
 
-### The model's three modes
+### The model's modes
 
 The system prompt picks the task, and the reference implementation pairs each with a generation
-routine of its own. Only the first produces text alone, so only the first runs here in full:
+routine of its own. Text comes out of two of them here:
 
 | Mode | System prompt | Reference routine | Output | Here |
 | --- | --- | --- | --- | --- |
+| Chat | none | `generate_sequential` | text | works |
 | ASR | `Perform ASR.` | `generate_sequential` | text | works |
 | TTS | `Perform TTS. Use the UK male voice.` (also US male, US female, UK female) | `generate_sequential` | speech | stops immediately |
-| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | the text answer; none of the speech |
+| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | six tokens, then nonsense: do not use |
 
-**ASR** is the supported path, and matches the reference token for token.
+**ASR** and **chat** are the supported paths. A spoken or typed question with no system prompt gets a
+text answer, and both match the reference step for step: on the reference's own `question.wav` and
+three typed questions the text logits agree to within 6e-4 and every token is the same.
 
 `LFM2.5-Audio-1.5B-JP` takes its own prompts, `Perform ASR in japanese.` and
 `Perform TTS in japanese.`; the rest of this section applies to it unchanged.
@@ -313,22 +316,19 @@ generation ends there with whatever text came first.
 **TTS** produces nothing here, and there is no text to be had: asked to speak a sentence, the whole
 text stream the model emits is `<|audio_start|>` followed by `<|im_end|>`, everything in between
 being audio. Generation stops on that first token, and the stop token is not added to the sequence,
-so there is nothing after the prompt to decode: expect an empty string. A sequential answer that begins in text and turns
-to speech part way keeps the text it had before the switch.
-
-**Interleaved** gives the text answer and none of the speech. Asked a spoken question, the model
-writes its answer, emits `<|text_end|>`, and says the rest aloud; generation stops at that token, so
-what comes back is the answer itself. Asked *What is the capital of France?*, this runtime returns
-`The capital of France is Paris` — the same text the reference produced before it began speaking,
-which it then spoke.
-
-Between those text tokens the reference is also emitting audio, six text tokens at a time against
-`interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint), and that speech feeds back into
-the context. So the text can depend on audio this runtime never generates: on a ten second clip with
-no question in it, the reference wrote 29 text tokens with the sampling the model card gives and 54
-with the audio taken greedily, and this runtime matched the first 29 and stopped. Short answers to
-spoken questions come through whole; a long turn is the model's answer as far as it gets, not a
-reproduction of any one interleaved run.
+so there is nothing after the prompt to decode: expect an empty string. The first-step logits match
+the reference to 4e-5 in all four voices.
+
+**Interleaved** is not usable here. The reference writes `interleaved_n_text` text tokens (six),
+then `interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint) whose embeddings go back
+into the context, then six more text tokens, and so on. This runtime has no audio frames to feed, so
+after the sixth token it keeps reading the text head at positions the model means for speech. The
+first six tokens match the reference exactly; from the seventh the output is mostly the
+non-breaking-space token, with the odd word between. Asked *Name three primary colors.*, the reference
+writes *Red, blue, and yellow are the three primary colors…* and this runtime writes
+*Red, blue, and yellow* followed by a hundred `\xa0`. Only an answer that fits in six tokens plus
+`<|text_end|>` survives (*The capital of France is Paris*). For a text answer, leave the system prompt
+out and use chat mode, which is the same model answering the same question in text.
 
 Sampling: the reference generates text greedily in every mode, so `do_sample=False` is right here.
 The temperatures and `top_k` values quoted for the model (`audio_temperature=0.8, audio_top_k=64`
@@ -346,9 +346,9 @@ that sentence exactly, punctuation included. The two voices are different wavefo
 carries more of its energy below 1 kHz than the female one, as it should. Asked the spoken question
 above in interleaved mode, it answered in 41 audio frames ending in the end-of-audio code, 3.2
 seconds that read back as *The capital of France is Paris.* ONNX Runtime GenAI's generation loop
-samples one token stream, so none of that has a home here yet. Generation
-stops at `<|audio_start|>` rather than reading the depthformer's positions off the text head; see
-[the three modes](#the-models-three-modes) for what each prompt gives you. LiquidAI ships
+samples one token stream, so none of that has a home here yet. Generation stops at `<|audio_start|>`
+and `<|text_end|>` rather than reading the depthformer's positions off the text head, and interleaved
+mode is out; see [the modes](#the-models-modes) for what each prompt gives you. LiquidAI ships
 `vocoder_depthformer.onnx` and `audio_detokenizer.onnx` in the ONNX repository, and their
 [onnx-export](https://github.com/Liquid4All/onnx-export) repository drives them from Python.
 

From 0331edf667bded3953bbf539b5cd4e9a274117b6 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 01:34:44 -0400
Subject: [PATCH 16/23] feat: speech output for LFM2-Audio, so TTS and
 interleaved mode work

The depthformer turns each decoder hidden state into a frame of eight audio codes,
and the frame's embedding is the decoder's next input, with liquid-audio's switching
between text and speech: on <|audio_start|> and the end-of-audio code, and by count
with search.audio_interleaved. The sequence keeps one placeholder per frame and the
codes come from get_output("audio_codes").

With the audio taken greedily on both sides every text token and audio frame matches
liquid-audio, once the published depthformer graph is corrected: it never applied its
rotary embedding and built the tables for the wrong theta. The tutorial has the fix.
---
 examples/python/lfm2-audio-tutorial.md        | 247 +++++++++++++----
 src/config.cpp                                |  72 +++++
 src/config.h                                  |  21 ++
 src/models/lfm2_audio_output.cpp              | 255 ++++++++++++++++++
 src/models/lfm2_audio_output.h                |  83 ++++++
 src/models/multi_modal.cpp                    |  59 +++-
 src/models/multi_modal.h                      |  10 +
 .../lfm2-audio/dummy_audio_embedding.onnx     | Bin 0 -> 32472 bytes
 test/models/lfm2-audio/dummy_depthformer.onnx | Bin 0 -> 165323 bytes
 test/models/lfm2-audio/dummy_text.onnx        | Bin 133917 -> 134017 bytes
 .../create/create_dummy_lfm2_audio_models.py  |  95 ++++++-
 .../create/create_dummy_lfm2_vl_models.py     |  16 +-
 test/python/models/test_lfm2_audio_models.py  | 247 +++++++++++++++++
 13 files changed, 1041 insertions(+), 64 deletions(-)
 create mode 100644 src/models/lfm2_audio_output.cpp
 create mode 100644 src/models/lfm2_audio_output.h
 create mode 100644 test/models/lfm2-audio/dummy_audio_embedding.onnx
 create mode 100644 test/models/lfm2-audio/dummy_depthformer.onnx

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 43ca267af8..e544f493ad 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -15,11 +15,10 @@ runs the speech-to-text half of it — ASR and spoken-prompt chat — as three O
 Only the decoder is produced by the model builder in this repository; the speech encoder is
 published as ONNX by LiquidAI, and the embedding model is a small graph you build once.
 
-**Audio output is not supported.** The model can also *speak*: it emits audio codes through an
-RQ-transformer ("depthformer") that ONNX Runtime GenAI has no generation loop for, and those codes
-then need the audio detokenizer and an inverse STFT to become a waveform. The export below drops the
-depthformer and the audio embeddings with it, so the model answers in text only. See
-[Known limitations](#6-known-limitations).
+The model can also *speak*. Two more graphs, both published by LiquidAI, give it a voice here: a
+depthformer that turns a decoder hidden state into a frame of audio codes, and the audio embedding that
+feeds each frame back to the decoder. [Speech output](#6-speech-output) adds them; without them the
+model answers in text only.
 
 ## Steps
 
@@ -28,7 +27,8 @@ depthformer and the audio embeddings with it, so the model answers in text only.
 3. [Write `genai_config.json`](#3-write-genai_configjson)
 4. [Choose the precisions](#4-choose-the-precisions)
 5. [Run the model](#5-run-the-model)
-6. [Known limitations](#6-known-limitations)
+6. [Speech output](#6-speech-output)
+7. [Known limitations](#7-known-limitations)
 
 ## 1. Build the decoder
 
@@ -291,66 +291,197 @@ Perform ASR.<|im_end|>
 ### The model's modes
 
 The system prompt picks the task, and the reference implementation pairs each with a generation
-routine of its own. Text comes out of two of them here:
+routine of its own:
 
-| Mode | System prompt | Reference routine | Output | Here |
-| --- | --- | --- | --- | --- |
-| Chat | none | `generate_sequential` | text | works |
-| ASR | `Perform ASR.` | `generate_sequential` | text | works |
-| TTS | `Perform TTS. Use the UK male voice.` (also US male, US female, UK female) | `generate_sequential` | speech | stops immediately |
-| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | six tokens, then nonsense: do not use |
+| Mode | System prompt | Reference routine | Output | Text-only build | With [speech output](#6-speech-output) |
+| --- | --- | --- | --- | --- | --- |
+| Chat | none | `generate_sequential` | text | works | works |
+| ASR | `Perform ASR.` | `generate_sequential` | text | works | works |
+| TTS | `Perform TTS. Use the UK male voice.` (also US male, US female, UK female) | `generate_sequential` | speech | stops immediately | works |
+| Interleaved | `Respond with interleaved text and audio.` | `generate_interleaved` | text and speech, alternating | do not use | works, with `audio_interleaved=True` |
 
-**ASR** and **chat** are the supported paths. A spoken or typed question with no system prompt gets a
-text answer, and both match the reference step for step: on the reference's own `question.wav` and
-three typed questions the text logits agree to within 6e-4 and every token is the same.
+**ASR** and **chat** need nothing more than the three models above. A spoken or typed question with
+no system prompt gets a text answer, and both match the reference step for step: on the reference's
+own `question.wav` and three typed questions the text logits agree to within 6e-4 and every token is
+the same.
 
 `LFM2.5-Audio-1.5B-JP` takes its own prompts, `Perform ASR in japanese.` and
 `Perform TTS in japanese.`; the rest of this section applies to it unchanged.
 
-Two tokens mark the turn passing from text to speech, and the builder puts both in `eos_token_id`:
-`<|audio_start|>` when the rest of the answer is spoken, and `<|text_end|>` when the text half of an
-interleaved answer is done. The positions after either are audio codes meant for the depthformer this
-runtime does not have; read off the text head they decode to fluent, plausible nonsense, so
-generation ends there with whatever text came first.
-
-**TTS** produces nothing here, and there is no text to be had: asked to speak a sentence, the whole
-text stream the model emits is `<|audio_start|>` followed by `<|im_end|>`, everything in between
-being audio. Generation stops on that first token, and the stop token is not added to the sequence,
-so there is nothing after the prompt to decode: expect an empty string. The first-step logits match
-the reference to 4e-5 in all four voices.
-
-**Interleaved** is not usable here. The reference writes `interleaved_n_text` text tokens (six),
-then `interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint) whose embeddings go back
-into the context, then six more text tokens, and so on. This runtime has no audio frames to feed, so
-after the sixth token it keeps reading the text head at positions the model means for speech. The
-first six tokens match the reference exactly; from the seventh the output is mostly the
-non-breaking-space token, with the odd word between. Asked *Name three primary colors.*, the reference
-writes *Red, blue, and yellow are the three primary colors…* and this runtime writes
-*Red, blue, and yellow* followed by a hundred `\xa0`. Only an answer that fits in six tokens plus
-`<|text_end|>` survives (*The capital of France is Paris*). For a text answer, leave the system prompt
-out and use chat mode, which is the same model answering the same question in text.
+Two tokens mark the turn passing from text to speech: `<|audio_start|>` when the rest of the answer is
+spoken, and `<|text_end|>` when the text half of an interleaved answer is done. In a text-only build
+the builder puts both in `eos_token_id`, because the positions after either are audio codes meant for
+the depthformer, and read off the text head they decode to fluent, plausible nonsense.
+
+**TTS** in a text-only build produces nothing: the whole text stream the model emits is
+`<|audio_start|>` followed by `<|im_end|>`, everything in between being audio. Generation stops on
+that first token, and the stop token is not added to the sequence, so there is nothing after the
+prompt to decode: expect an empty string.
+
+**Interleaved** cannot be used in a text-only build. The reference writes six text tokens, then
+`interleaved_n_audio` audio frames (12, or 9 for the JP checkpoint) whose embeddings go back into the
+context, then six more text tokens, and so on. Without audio frames to feed, the text head is read at
+positions the model means for speech: the first six tokens are right and the rest is mostly the
+non-breaking-space token. For a text answer, leave the system prompt out and use chat mode.
 
 Sampling: the reference generates text greedily in every mode, so `do_sample=False` is right here.
-The temperatures and `top_k` values quoted for the model (`audio_temperature=0.8, audio_top_k=64`
-for TTS, `1.0` and `4` for interleaved) apply only to the audio stream, which this runtime does not
-produce; they have no text-side equivalent to set.
-
-## 6. Known limitations
-
-**Audio output is not supported.** TTS and the speech half of interleaved generation need the
-depthformer, which predicts 8 codebook entries per 80 ms audio frame in an inner autoregressive loop,
-plus the audio detokenizer to turn those codes into a waveform. That speech is worth having, and it
-is good: asked through the reference to say *The quick brown fox jumps over the lazy dog.*, the model
-produced about three seconds of 24 kHz audio per voice, and this runtime's own ASR read both back as
-that sentence exactly, punctuation included. The two voices are different waveforms, and the male one
-carries more of its energy below 1 kHz than the female one, as it should. Asked the spoken question
-above in interleaved mode, it answered in 41 audio frames ending in the end-of-audio code, 3.2
-seconds that read back as *The capital of France is Paris.* ONNX Runtime GenAI's generation loop
-samples one token stream, so none of that has a home here yet. Generation stops at `<|audio_start|>`
-and `<|text_end|>` rather than reading the depthformer's positions off the text head, and interleaved
-mode is out; see [the modes](#the-models-modes) for what each prompt gives you. LiquidAI ships
-`vocoder_depthformer.onnx` and `audio_detokenizer.onnx` in the ONNX repository, and their
-[onnx-export](https://github.com/Liquid4All/onnx-export) repository drives them from Python.
+The temperatures and `top_k` values quoted for the model apply to the audio codes only; see
+[Speech output](#6-speech-output) for where they go.
+
+## 6. Speech output
+
+Speech needs the decoder's hidden states, two more graphs and a few lines of `genai_config.json`.
+
+**Build the decoder with its hidden states.** The depthformer reads the hidden state the logits are
+projected from, so add `include_hidden_states=true`:
+
+```bash
+$ python3 -m onnxruntime_genai.models.builder \
+    -i ./lfm2.5-audio/pytorch -o ./lfm2.5-audio/cpu -p int4 -e cpu \
+    --extra_options exclude_embeds=true include_hidden_states=true
+```
+
+**Get the two graphs** from
+[LiquidAI/LFM2.5-Audio-1.5B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-ONNX):
+`onnx/vocoder_depthformer.onnx` and `onnx/audio_embedding.onnx` (with their `*.onnx_data` files for
+the `fp16` and `q4` variants), and `onnx/audio_detokenizer.onnx` for turning the codes into sound
+afterwards. Like the encoder they belong to `LFM2.5-Audio-1.5B`; the other checkpoints need their own
+export, which LiquidAI's [onnx-export](https://github.com/Liquid4All/onnx-export) produces.
+
+**Fix the depthformer's rotary embedding.** As published, the graph's attention nodes carry rotary
+tables but do not apply them (`do_rotary` is unset), and the tables are computed for `theta` 10000
+where the model uses 1,000,000. The first codebook of each frame sits at position zero and is
+unaffected; the other seven come out with logits up to 2.5 away from the PyTorch model's. The speech
+is intelligible either way, since the first codebook carries most of what is said, but only the
+corrected graph gives the model's own codes: with both fixes every codebook agrees to 1e-5.
+
+```python
+import numpy as np
+import onnx
+from onnx import helper, numpy_helper
+
+model = onnx.load("vocoder_depthformer.onnx")
+for node in model.graph.node:
+    if node.op_type == "GroupQueryAttention" and not any(a.name == "do_rotary" for a in node.attribute):
+        node.attribute.append(helper.make_attribute("do_rotary", 1))
+for table in model.graph.initializer:
+    if table.name.endswith((".gqa_cos", ".gqa_sin")):
+        positions, half = numpy_helper.to_array(table).shape
+        angles = np.outer(np.arange(positions), 1.0 / 1_000_000 ** (np.arange(0, 2 * half, 2) / (2 * half)))
+        values = np.cos(angles) if table.name.endswith("cos") else np.sin(angles)
+        table.CopyFrom(numpy_helper.from_array(values.astype(np.float32), table.name))
+onnx.save(model, "vocoder_depthformer.onnx")
+```
+
+**Add `audio_output` to `genai_config.json`, and take the two switch tokens out of `eos_token_id`.**
+They end a text-only answer; here they are where speech begins, and the model is refused if they are
+still stop tokens:
+
+```json
+{
+    "model": {
+        "eos_token_id": 7,
+        "audio_output": {
+            "depthformer": { "filename": "vocoder_depthformer.onnx" },
+            "embedding": { "filename": "audio_embedding.onnx" }
+        }
+    }
+}
+```
+
+`audio_output` also takes `num_codebooks` (8), `codebook_size` (2049, the last entry being the
+end-of-audio code), `audio_start_token_id` (128), `text_end_token_id` (130), `interleaved_n_text` (6)
+and `interleaved_n_audio` (12). The defaults are the published checkpoints'; set
+`interleaved_n_audio` to 9 for `LFM2.5-Audio-1.5B-JP`.
+
+**Generate.** Three search options steer the speech, next to the usual ones:
+
+| Option | Default | Meaning |
+| --- | --- | --- |
+| `audio_interleaved` | `false` | Alternate text and speech by count, as `generate_interleaved` does. Leave it off for TTS, ASR and chat, which switch on `<|audio_start|>` alone. |
+| `audio_temperature` | `1.0` | Temperature of the audio codes. `0` takes the likeliest code. The model card uses 0.8 for TTS and 1.0 for interleaved. |
+| `audio_top_k` | `4` | Audio codes kept when sampling. `1` takes the likeliest code. The model card uses 64 for TTS and 4 for interleaved. |
+
+The text is still decoded greedily, and `random_seed` seeds the audio codes as well.
+
+```python
+import numpy as np
+import onnxruntime_genai as og
+
+model = og.Model("./lfm2.5-audio/cpu")
+processor = model.create_multimodal_processor()
+tokenizer = og.Tokenizer(model)
+
+prompt = (
+    "<|startoftext|><|im_start|>system\nRespond with interleaved text and audio.<|im_end|>\n"
+    "<|im_start|>user\n<|audio|><|im_end|>\n<|im_start|>assistant\n"
+)
+inputs = processor(prompt, audios=og.Audios.open("question.wav"))
+prompt_length = inputs["input_ids"].as_numpy().shape[1]
+
+params = og.GeneratorParams(model)
+params.set_search_options(
+    do_sample=False, max_length=prompt_length + 512, audio_interleaved=True, audio_temperature=1.0, audio_top_k=4
+)
+generator = og.Generator(model, params)
+generator.set_inputs(inputs)
+while not generator.is_done():
+    generator.generate_next_token()
+
+answer = generator.get_sequence(0)[prompt_length:]
+audio_token_id, audio_start, text_end = 133, 128, 130
+text = tokenizer.decode(np.array([t for t in answer if t not in (audio_token_id, audio_start, text_end)], np.int32))
+codes = generator.get_output("audio_codes")  # [num_frames, 8] int64, 80 ms of speech per frame
+```
+
+Each audio frame takes one place in the sequence, held by `audio_token_id`, so `max_length` counts
+frames as well as text tokens; twelve and a half frames make a second of speech. The frames themselves
+come from `get_output("audio_codes")`, at any point during generation or after it, with the
+end-of-audio frames left out.
+
+**Turn the codes into sound** with `audio_detokenizer.onnx`, which gives the log-magnitude and phase
+of a short-time Fourier transform, six columns per frame, and an inverse transform with the
+reference's "same" padding. This graph needs no correction: it matches the PyTorch detokenizer to
+1e-5.
+
+```python
+import onnxruntime as ort
+import soundfile as sf
+
+n_fft, hop = 1280, 320
+detokenizer = ort.InferenceSession("audio_detokenizer.onnx")
+# Only the first codebook carries the end-of-audio code on purpose; the detokenizer takes 0..2047.
+features = detokenizer.run(None, {"audio_codes": np.minimum(codes, 2047).T[None]})[0][0]
+spectrum = (np.exp(features[:, : n_fft // 2 + 1]) * np.exp(1j * features[:, n_fft // 2 + 1 :])).T
+
+window = 0.5 - 0.5 * np.cos(2 * np.pi * np.arange(n_fft) / n_fft)  # torch.hann_window: periodic
+frames = np.fft.irfft(spectrum, n_fft, axis=0) * window[:, None]
+wave = np.zeros((frames.shape[1] - 1) * hop + n_fft)
+envelope = np.zeros_like(wave)
+for t in range(frames.shape[1]):
+    wave[t * hop : t * hop + n_fft] += frames[:, t]
+    envelope[t * hop : t * hop + n_fft] += window**2
+pad = (n_fft - hop) // 2
+sf.write("answer.wav", (wave[pad:-pad] / envelope[pad:-pad]).astype(np.float32), 24000)
+```
+
+**How it compares with the reference.** With the audio codes taken greedily on both sides
+(`audio_top_k=1`), so that the two can be compared at all, this runtime reproduces liquid-audio
+exactly: every text token and every audio frame of an interleaved answer to the reference's
+`question.wav` (200 items, 149 of them frames), of an interleaved answer to a typed question, and of a
+TTS sentence. With the model card's sampling the speech reads back through this runtime's own ASR as
+the text the model wrote: *Red, blue, and yellow are the three primary colors. Would you like to hear
+how they’re used in art?* comes back word for word, and *The quick brown fox jumps over the lazy dog.*
+comes back exactly in TTS.
+
+## 7. Known limitations
+
+**The waveform is made outside the runtime.** Generation gives audio codes; the detokenizer and the
+inverse transform that turn them into sound are the few lines of Python in
+[Speech output](#6-speech-output), not part of the generation loop.
+
+**Speech output is for one sequence.** A batch size of 1 and no beam search, as for speech input.
+After an answer that ended in speech, a new turn appended to the same generator starts in text.
 
 **One prompt at a time, decoded greedily.** Several clips in one prompt work, but batched prompts
 and beam search (`num_beams` above 1) are refused; the reference decodes its text greedily too. The
diff --git a/src/config.cpp b/src/config.cpp
index 8f2ea33af7..1d320981f9 100644
--- a/src/config.cpp
+++ b/src/config.cpp
@@ -1544,6 +1544,68 @@ struct Speech_Element : JSON::Element {
   SpeechOutputs_Element outputs_{v_.outputs};
 };
 
+struct AudioOutputGraph_Element : JSON::Element {
+  explicit AudioOutputGraph_Element(Config::Model::AudioOutput::Graph& v) : v_{v} {}
+
+  void OnValue(std::string_view name, JSON::Value value) override {
+    if (name == "filename") {
+      v_.filename = JSON::Get<std::string_view>(value);
+    } else {
+      throw JSON::unknown_value_error{};
+    }
+  }
+
+  Element& OnObject(std::string_view name) override {
+    if (name == "session_options") {
+      v_.session_options = Config::SessionOptions{};
+      session_options_ = std::make_unique<SessionOptions_Element>(*v_.session_options);
+      return *session_options_;
+    }
+    throw JSON::unknown_value_error{};
+  }
+
+ private:
+  Config::Model::AudioOutput::Graph& v_;
+  std::unique_ptr<SessionOptions_Element> session_options_;
+};
+
+struct AudioOutput_Element : JSON::Element {
+  explicit AudioOutput_Element(Config::Model::AudioOutput& v) : v_{v} {}
+
+  void OnValue(std::string_view name, JSON::Value value) override {
+    if (name == "num_codebooks") {
+      v_.num_codebooks = SafeDoubleToInt(JSON::Get<double>(value), name);
+    } else if (name == "codebook_size") {
+      v_.codebook_size = SafeDoubleToInt(JSON::Get<double>(value), name);
+    } else if (name == "audio_start_token_id") {
+      v_.audio_start_token_id = SafeDoubleToInt(JSON::Get<double>(value), name);
+    } else if (name == "text_end_token_id") {
+      v_.text_end_token_id = SafeDoubleToInt(JSON::Get<double>(value), name);
+    } else if (name == "interleaved_n_text") {
+      v_.interleaved_n_text = SafeDoubleToInt(JSON::Get<double>(value), name);
+    } else if (name == "interleaved_n_audio") {
+      v_.interleaved_n_audio = SafeDoubleToInt(JSON::Get<double>(value), name);
+    } else {
+      throw JSON::unknown_value_error{};
+    }
+  }
+
+  Element& OnObject(std::string_view name) override {
+    if (name == "depthformer") {
+      return depthformer_;
+    }
+    if (name == "embedding") {
+      return embedding_;
+    }
+    throw JSON::unknown_value_error{};
+  }
+
+ private:
+  Config::Model::AudioOutput& v_;
+  AudioOutputGraph_Element depthformer_{v_.depthformer};
+  AudioOutputGraph_Element embedding_{v_.embedding};
+};
+
 struct JoinerInputs_Element : JSON::Element {
   explicit JoinerInputs_Element(Config::Model::Joiner::Inputs& v) : v_{v} {}
 
@@ -1840,6 +1902,9 @@ struct Model_Element : JSON::Element {
     if (name == "speech") {
       return speech_;
     }
+    if (name == "audio_output") {
+      return audio_output_;
+    }
     if (name == "joiner") {
       return joiner_;
     }
@@ -1873,6 +1938,7 @@ struct Model_Element : JSON::Element {
   Vision_Element vision_{v_.vision};
   Embedding_Element embedding_{v_.embedding};
   Speech_Element speech_{v_.speech};
+  AudioOutput_Element audio_output_{v_.audio_output};
   Joiner_Element joiner_{v_.joiner};
   VAD_Element vad_{v_.vad};
   Mtp_Element mtp_{v_.mtp};
@@ -1986,6 +2052,12 @@ struct Search_Element : JSON::Element {
       v_.early_stopping = JSON::Get<bool>(value);
     } else if (name == "blank_penalty") {
       v_.blank_penalty = static_cast<float>(JSON::Get<double>(value));
+    } else if (name == "audio_interleaved") {
+      v_.audio_interleaved = JSON::Get<bool>(value);
+    } else if (name == "audio_temperature") {
+      v_.audio_temperature = static_cast<float>(JSON::Get<double>(value));
+    } else if (name == "audio_top_k") {
+      v_.audio_top_k = SafeDoubleToInt(JSON::Get<double>(value), name);
     } else {
       throw JSON::unknown_value_error{};
     }
diff --git a/src/config.h b/src/config.h
index 21ef67fb66..e673e552df 100644
--- a/src/config.h
+++ b/src/config.h
@@ -327,6 +327,24 @@ struct Config {
       } outputs;
     } speech;
 
+    // Speech output of LFM2-Audio: the depthformer turns one decoder hidden state into a frame of
+    // audio codes, one per codebook, and the audio embedding turns that frame back into the decoder's
+    // next input. Empty filenames leave the model answering in text only.
+    struct AudioOutput {
+      struct Graph {
+        std::string filename;
+        std::optional<SessionOptions> session_options;
+      };
+      Graph depthformer;
+      Graph embedding;
+      int num_codebooks{8};
+      int codebook_size{2049};        // entries per codebook; the last one is the end-of-audio code
+      int audio_start_token_id{128};  // <|audio_start|>: the rest of a sequential answer is speech
+      int text_end_token_id{130};     // <|text_end|>: the text of an interleaved answer is over
+      int interleaved_n_text{6};      // text tokens per turn of an interleaved answer
+      int interleaved_n_audio{12};    // audio frames per turn of an interleaved answer
+    } audio_output;
+
     struct Joiner {
       std::string filename;
       std::optional<SessionOptions> session_options;
@@ -642,6 +660,9 @@ struct Config {
     int random_seed{-1};               // -1 = Seed with random device, otherwise use value to seed RNG
     std::optional<size_t> chunk_size;  // Chunk size for prefill chunking during context processing. If present, chunking is enabled with the chunk size > 0.
     float blank_penalty{};             // Penalty applied to blank token logits in CTC/RNNT decoding. Default 0 means no penalty.
+    bool audio_interleaved{};          // LFM2-Audio: alternate text tokens and audio frames by count (interleaved mode) rather than switching on <|audio_start|>.
+    float audio_temperature{1.0f};     // LFM2-Audio: temperature the audio codes are sampled with. 0 takes the most likely code.
+    int audio_top_k{4};                // LFM2-Audio: number of most likely audio codes kept when sampling. 1 takes the most likely code.
   } search;
 
   struct Speculative {
diff --git a/src/models/lfm2_audio_output.cpp b/src/models/lfm2_audio_output.cpp
new file mode 100644
index 0000000000..b0a3190846
--- /dev/null
+++ b/src/models/lfm2_audio_output.cpp
@@ -0,0 +1,255 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "generator/generators.h"
+#include "models/lfm2_audio_output.h"
+#include "models/multi_modal.h"
+#include "models/utils.h"
+
+#include <algorithm>
+#include <cstring>
+#include <limits>
+#include <numeric>
+
+namespace Generators {
+
+namespace {
+
+// Names of LiquidAI's depthformer and audio embedding exports (github.com/Liquid4All/onnx-export).
+constexpr const char* kDepthformerInputs[] = {"hidden_states", "depth_slices_in", "step_idx", "prev_token",
+                                              "past_keys", "past_values", "seqlens_k", "total_seq_len"};
+constexpr const char* kDepthformerOutputs[] = {"logits", "depth_slices", "new_keys", "new_values"};
+constexpr const char* kEmbeddingInputs[] = {"audio_codes"};
+constexpr const char* kEmbeddingOutputs[] = {"audio_embeds"};
+
+std::vector<int64_t> InputShape(OrtSession& session, const char* name, const char* graph) {
+  const auto names = session.GetInputNames();
+  for (size_t i = 0; i < names.size(); ++i) {
+    if (names[i] == name) {
+      return session.GetInputTypeInfo(i)->GetTensorTypeAndShapeInfo().GetShape();
+    }
+  }
+  throw std::runtime_error(std::string("Lfm2AudioOutput: the ") + graph + " model has no input named \"" + name + "\".");
+}
+
+template <typename T>
+std::unique_ptr<OrtValue> MakeTensor(std::span<const T> values, std::vector<int64_t> shape) {
+  auto tensor = OrtValue::CreateTensor<T>(Ort::Allocator::GetWithDefaultOptions(), shape);
+  std::copy(values.begin(), values.end(), tensor->template GetTensorMutableData<T>());
+  return tensor;
+}
+
+template <typename T>
+std::unique_ptr<OrtValue> MakeScalar(T value) {
+  return MakeTensor<T>(std::span<const T>{&value, 1}, {});
+}
+
+}  // namespace
+
+Lfm2AudioOutput::Lfm2AudioOutput(const MultiModalLanguageModel& model, const GeneratorParams& params)
+    : model_{model},
+      config_{model.config_->model.audio_output},
+      interleaved_{params.search.audio_interleaved},
+      temperature_{params.search.audio_temperature},
+      top_k_{params.search.audio_top_k},
+      placeholder_token_id_{model.config_->model.audio_token_id},
+      rng_{CreateRandomGenerator(params.search.random_seed)},
+      modality_left_{config_.interleaved_n_text} {
+  if (params.BatchBeamSize() != 1) {
+    throw std::runtime_error("Lfm2AudioOutput: speech output needs a batch size of 1 and no beam search.");
+  }
+
+  const auto hidden_shape = InputShape(*model_.depthformer_session_, "hidden_states", "depthformer");
+  const auto slices_shape = InputShape(*model_.depthformer_session_, "depth_slices_in", "depthformer");
+  const auto keys_shape = InputShape(*model_.depthformer_session_, "past_keys", "depthformer");
+  if (hidden_shape.size() != 2 || slices_shape.size() != 3 || keys_shape.size() != 5 ||
+      slices_shape[1] != config_.num_codebooks) {
+    throw std::runtime_error("Lfm2AudioOutput: the depthformer model does not have the expected inputs for " +
+                             std::to_string(config_.num_codebooks) + " codebooks.");
+  }
+  hidden_size_ = hidden_shape[1];
+  depth_size_ = slices_shape[2];
+  num_layers_ = keys_shape[0];
+  num_kv_heads_ = keys_shape[2];
+  head_size_ = keys_shape[4];
+
+  // The search has to pick the placeholder whatever its sampling settings are.
+  const size_t vocab_size = static_cast<size_t>(model_.config_->model.vocab_size);
+  placeholder_logits_ = model_.p_device_->Allocate<float>(vocab_size);
+  auto logits = placeholder_logits_.CpuSpan();
+  std::fill(logits.begin(), logits.end(), std::numeric_limits<float>::lowest());
+  logits[static_cast<size_t>(placeholder_token_id_)] = 0.0f;
+  placeholder_logits_.CopyCpuToDevice();
+}
+
+void Lfm2AudioOutput::BeginStep(DeviceSpan<int32_t>& next_tokens, bool is_prompt) {
+  if (is_prompt || next_tokens.size() != 1) {
+    // A new turn starts in text, whatever the last one ended in.
+    modality_ = Modality::Text;
+    previous_was_text_ = false;
+    text_done_ = false;
+    modality_left_ = config_.interleaved_n_text;
+    pending_embedding_.clear();
+  } else if (previous_was_text_) {
+    const int32_t token = next_tokens.CopyDeviceToCpu()[0];
+    if (interleaved_) {
+      text_done_ = text_done_ || token == config_.text_end_token_id;
+      if (modality_left_ == 0 || text_done_) {
+        modality_ = Modality::Audio;
+        modality_left_ = config_.interleaved_n_audio;
+      }
+    } else if (token == config_.audio_start_token_id) {
+      modality_ = Modality::Audio;
+    }
+  }
+
+  --modality_left_;
+  previous_was_text_ = modality_ == Modality::Text;
+}
+
+void Lfm2AudioOutput::WritePendingFrame(OrtValue& inputs_embeds) {
+  const auto info = inputs_embeds.GetTensorTypeAndShapeInfo();
+  if (info->GetElementCount() != pending_embedding_.size()) {
+    throw std::runtime_error("Lfm2AudioOutput: expected the decoder to take one embedding of " +
+                             std::to_string(pending_embedding_.size()) + " values after an audio frame, got " +
+                             std::to_string(info->GetElementCount()) + ".");
+  }
+
+  auto bytes = ByteWrapTensor(*model_.p_device_, inputs_embeds);
+  auto cpu = bytes.CpuSpan();
+  if (info->GetElementType() == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
+    std::memcpy(cpu.data(), pending_embedding_.data(), pending_embedding_.size() * sizeof(float));
+  } else if (info->GetElementType() == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16) {
+    auto* half = reinterpret_cast<uint16_t*>(cpu.data());
+    std::transform(pending_embedding_.begin(), pending_embedding_.end(), half, FastFloat32ToFloat16);
+  } else {
+    throw std::runtime_error("Lfm2AudioOutput: the decoder's inputs_embeds must be float or float16.");
+  }
+  bytes.CopyCpuToDevice();
+  pending_embedding_.clear();
+}
+
+DeviceSpan<float> Lfm2AudioOutput::SampleFrame(OrtValue& hidden_states) {
+  const auto info = hidden_states.GetTensorTypeAndShapeInfo();
+  const auto shape = info->GetShape();
+  if (shape.empty() || shape.back() != hidden_size_) {
+    throw std::runtime_error("Lfm2AudioOutput: the decoder's hidden states do not match the depthformer's input of " +
+                             std::to_string(hidden_size_) + " values.");
+  }
+
+  // The last position of [batch, sequence, hidden_size].
+  const size_t count = static_cast<size_t>(hidden_size_);
+  const size_t first = info->GetElementCount() - count;
+  auto bytes = ByteWrapTensor(*model_.p_device_, hidden_states).CopyDeviceToCpu();
+  std::vector<float> hidden(count);
+  if (info->GetElementType() == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
+    std::memcpy(hidden.data(), bytes.data() + first * sizeof(float), count * sizeof(float));
+  } else if (info->GetElementType() == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16) {
+    const auto* half = reinterpret_cast<const uint16_t*>(bytes.data()) + first;
+    std::transform(half, half + count, hidden.begin(), FastFloat16ToFloat32);
+  } else {
+    throw std::runtime_error("Lfm2AudioOutput: the decoder's hidden states must be float or float16.");
+  }
+
+  std::vector<int64_t> frame = RunDepthformer(hidden);
+
+  // As the reference orders it: the count first, then the end-of-audio code, which wins.
+  if (interleaved_ && modality_left_ == 0 && !text_done_) {
+    modality_ = Modality::Text;
+    modality_left_ = config_.interleaved_n_text;
+  }
+  const int64_t end_of_audio = config_.codebook_size - 1;
+  if (frame[0] == end_of_audio) {
+    std::fill(frame.begin(), frame.end(), end_of_audio);
+    modality_ = Modality::Text;
+  } else {
+    audio_codes_.insert(audio_codes_.end(), frame.begin(), frame.end());
+  }
+
+  EmbedFrame(frame);
+  return placeholder_logits_;
+}
+
+std::vector<int64_t> Lfm2AudioOutput::RunDepthformer(std::span<const float> hidden) {
+  const auto hidden_tensor = MakeTensor<float>(hidden, {1, hidden_size_});
+  const std::vector<int64_t> cache_shape{num_layers_, 1, num_kv_heads_, 0, head_size_};
+  auto keys = OrtValue::CreateTensor<float>(Ort::Allocator::GetWithDefaultOptions(), cache_shape);
+  auto values = OrtValue::CreateTensor<float>(Ort::Allocator::GetWithDefaultOptions(), cache_shape);
+  const std::vector<float> no_slices(static_cast<size_t>(config_.num_codebooks * depth_size_), 0.0f);
+  auto slices = MakeTensor<float>(no_slices, {1, config_.num_codebooks, depth_size_});
+
+  // One run per codebook: each code is sampled given the ones before it in the frame. The projection
+  // of the hidden state is computed on the first run and handed back in on the rest.
+  std::vector<int64_t> frame;
+  int64_t previous_code = 0;
+  for (int32_t step = 0; step < config_.num_codebooks; ++step) {
+    const auto step_index = MakeScalar<int64_t>(step);
+    const auto previous = MakeTensor<int64_t>(std::span<const int64_t>{&previous_code, 1}, {1});
+    const auto past_length = MakeTensor<int32_t>(std::span<const int32_t>{&step, 1}, {1});
+    const auto total_length = MakeScalar<int32_t>(step + 1);
+    const OrtValue* inputs[] = {hidden_tensor.get(), slices.get(), step_index.get(), previous.get(),
+                                keys.get(), values.get(), past_length.get(), total_length.get()};
+    auto outputs = model_.depthformer_session_->Run(nullptr, kDepthformerInputs, inputs, std::size(inputs),
+                                                    kDepthformerOutputs, std::size(kDepthformerOutputs));
+
+    const float* logits = outputs[0]->GetTensorData<float>();
+    previous_code = SampleCode({logits, static_cast<size_t>(config_.codebook_size)});
+    frame.push_back(previous_code);
+    if (step == 0) {
+      slices = std::move(outputs[1]);
+    }
+    keys = std::move(outputs[2]);
+    values = std::move(outputs[3]);
+  }
+  return frame;
+}
+
+int64_t Lfm2AudioOutput::SampleCode(std::span<const float> logits) {
+  if (temperature_ <= 0.0f || top_k_ == 1) {
+    return std::max_element(logits.begin(), logits.end()) - logits.begin();
+  }
+
+  std::vector<int64_t> order(logits.size());
+  std::iota(order.begin(), order.end(), int64_t{0});
+  const size_t kept = top_k_ > 0 ? std::min(static_cast<size_t>(top_k_), order.size()) : order.size();
+  std::partial_sort(order.begin(), order.begin() + kept, order.end(),
+                    [&](int64_t a, int64_t b) { return logits[a] > logits[b]; });
+
+  std::vector<double> weights(kept);
+  const float highest = logits[order[0]];
+  for (size_t i = 0; i < kept; ++i) {
+    weights[i] = std::exp(static_cast<double>(logits[order[i]] - highest) / temperature_);
+  }
+  std::discrete_distribution<size_t> distribution(weights.begin(), weights.end());
+  return order[distribution(rng_)];
+}
+
+void Lfm2AudioOutput::EmbedFrame(std::span<const int64_t> frame) {
+  // Every codebook has its own rows of the one embedding table.
+  std::vector<int64_t> rows(frame.size());
+  for (size_t codebook = 0; codebook < frame.size(); ++codebook) {
+    rows[codebook] = frame[codebook] + static_cast<int64_t>(codebook) * config_.codebook_size;
+  }
+  const auto codes = MakeTensor<int64_t>(rows, {1, static_cast<int64_t>(rows.size())});
+  const OrtValue* inputs[] = {codes.get()};
+  auto outputs = model_.audio_embedding_session_->Run(nullptr, kEmbeddingInputs, inputs, std::size(inputs),
+                                                      kEmbeddingOutputs, std::size(kEmbeddingOutputs));
+
+  const auto shape = outputs[0]->GetTensorTypeAndShapeInfo()->GetShape();  // [1, num_codebooks, hidden_size]
+  const size_t width = static_cast<size_t>(shape.back());
+  const float* embeddings = outputs[0]->GetTensorData<float>();
+  pending_embedding_.assign(width, 0.0f);
+  for (size_t codebook = 0; codebook < frame.size(); ++codebook) {
+    for (size_t i = 0; i < width; ++i) {
+      pending_embedding_[i] += embeddings[codebook * width + i];
+    }
+  }
+}
+
+OrtValue* Lfm2AudioOutput::GetAudioCodes() {
+  const int64_t num_frames = static_cast<int64_t>(audio_codes_.size()) / config_.num_codebooks;
+  audio_codes_tensor_ = MakeTensor<int64_t>(audio_codes_, {num_frames, config_.num_codebooks});
+  return audio_codes_tensor_.get();
+}
+
+}  // namespace Generators
diff --git a/src/models/lfm2_audio_output.h b/src/models/lfm2_audio_output.h
new file mode 100644
index 0000000000..f13b8538cd
--- /dev/null
+++ b/src/models/lfm2_audio_output.h
@@ -0,0 +1,83 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+#pragma once
+
+#include <cstdint>
+#include <memory>
+#include <random>
+#include <span>
+#include <vector>
+
+#include "models/model.h"
+
+namespace Generators {
+
+struct MultiModalLanguageModel;
+
+// Speech output of LFM2-Audio, as liquid-audio's generate_sequential and generate_interleaved
+// produce it.
+//
+// The decoder emits one hidden state per step. While the answer is text the search samples a token
+// from the logits as usual; while it is speech the depthformer turns that hidden state into one
+// audio frame of num_codebooks codes instead, and the sum of the codes' embeddings is the decoder's
+// next input. The generator's sequence records each frame as one model.audio_token_id placeholder,
+// so lengths and stopping work unchanged, and the codes themselves are read with
+// generator.get_output("audio_codes").
+//
+// The turn passes to speech on <|audio_start|> and back on the end-of-audio code. With
+// search.audio_interleaved it also alternates by count, interleaved_n_text tokens against
+// interleaved_n_audio frames, until <|text_end|> leaves the rest of the turn to speech.
+struct Lfm2AudioOutput {
+  Lfm2AudioOutput(const MultiModalLanguageModel& model, const GeneratorParams& params);
+
+  // Called at the top of every pipeline run with the tokens the search just chose.
+  void BeginStep(DeviceSpan<int32_t>& next_tokens, bool is_prompt);
+
+  // True when the decoder's next input is the last audio frame rather than a token.
+  bool HasPendingFrame() const { return !pending_embedding_.empty(); }
+  // Overwrites the decoder's [1, 1, hidden_size] input with the last frame's embedding.
+  void WritePendingFrame(OrtValue& inputs_embeds);
+
+  bool InAudio() const { return modality_ == Modality::Audio; }
+  // Samples the next frame from the decoder's last hidden state and returns logits that make the
+  // search pick the placeholder token.
+  DeviceSpan<float> SampleFrame(OrtValue& hidden_states);
+
+  // Every frame so far, [num_frames, num_codebooks] int64, end-of-audio frames left out.
+  OrtValue* GetAudioCodes();
+
+ private:
+  enum struct Modality { Text,
+                         Audio };
+
+  std::vector<int64_t> RunDepthformer(std::span<const float> hidden);
+  int64_t SampleCode(std::span<const float> logits);
+  void EmbedFrame(std::span<const int64_t> frame);
+
+  const MultiModalLanguageModel& model_;
+  const Config::Model::AudioOutput& config_;
+  const bool interleaved_;
+  const float temperature_;
+  const int top_k_;
+  const int32_t placeholder_token_id_;
+  std::mt19937 rng_;
+
+  // Depthformer geometry, read off the graph.
+  int64_t hidden_size_{};
+  int64_t depth_size_{};
+  int64_t num_layers_{};
+  int64_t num_kv_heads_{};
+  int64_t head_size_{};
+
+  Modality modality_{Modality::Text};
+  bool previous_was_text_{};
+  bool text_done_{};
+  int modality_left_{};
+
+  std::vector<float> pending_embedding_;  // the last frame, as the decoder's next input
+  std::vector<int64_t> audio_codes_;      // num_frames * num_codebooks
+  std::unique_ptr<OrtValue> audio_codes_tensor_;
+  DeviceSpan<float> placeholder_logits_;
+};
+
+}  // namespace Generators
diff --git a/src/models/multi_modal.cpp b/src/models/multi_modal.cpp
index a777ac1108..1aedc0a012 100644
--- a/src/models/multi_modal.cpp
+++ b/src/models/multi_modal.cpp
@@ -116,6 +116,28 @@ MultiModalLanguageModel::MultiModalLanguageModel(std::unique_ptr<Config> config,
   embedding_session_ = CreateSession(ort_env, config_->model.embedding.filename, embedding_session_options_.get());
   decoder_session_ = CreateSession(ort_env, config_->model.decoder.filename, session_options_.get());
 
+  const auto& audio_output = config_->model.audio_output;
+  if (!audio_output.depthformer.filename.empty() || !audio_output.embedding.filename.empty()) {
+    if (audio_output.depthformer.filename.empty() || audio_output.embedding.filename.empty()) {
+      throw std::runtime_error("model.audio_output needs both depthformer.filename and embedding.filename.");
+    }
+    // With speech output these tokens hand the turn to the depthformer; as stop tokens they would end
+    // it there instead, which is what a text-only export needs and this one must not have.
+    for (const int token : {audio_output.audio_start_token_id, audio_output.text_end_token_id}) {
+      if (std::find(config_->model.eos_token_id.begin(), config_->model.eos_token_id.end(), token) !=
+          config_->model.eos_token_id.end()) {
+        throw std::runtime_error("model.eos_token_id holds " + std::to_string(token) +
+                                 ", which model.audio_output uses to start speech. Remove it from eos_token_id.");
+      }
+    }
+    depthformer_session_options_ = OrtSessionOptions::Create();
+    CreateSessionOptionsFromConfig(audio_output.depthformer.session_options.has_value() ? audio_output.depthformer.session_options.value() : config_->model.decoder.session_options, *depthformer_session_options_, true, /*disable_graph_capture=*/true);
+    depthformer_session_ = CreateSession(ort_env, audio_output.depthformer.filename, depthformer_session_options_.get());
+    audio_embedding_session_options_ = OrtSessionOptions::Create();
+    CreateSessionOptionsFromConfig(audio_output.embedding.session_options.has_value() ? audio_output.embedding.session_options.value() : config_->model.decoder.session_options, *audio_embedding_session_options_, true, /*disable_graph_capture=*/true);
+    audio_embedding_session_ = CreateSession(ort_env, audio_output.embedding.filename, audio_embedding_session_options_.get());
+  }
+
   session_info_.Add(*decoder_session_);
   session_info_.Add(*embedding_session_);
   if (speech) {
@@ -968,6 +990,9 @@ MultiModalPipelineState::MultiModalPipelineState(const MultiModalLanguageModel&
   }
   embedding_state_ = std::make_unique<EmbeddingState>(model, params);
   decoder_state_ = std::make_unique<DecoderState>(model_, sequence_lengths, params);
+  if (model_.depthformer_session_) {
+    audio_output_ = std::make_unique<Lfm2AudioOutput>(model_, params);
+  }
 
   if (vision_state_ != nullptr && model_.config_->model.vision.adapter_filename.has_value() && num_image_tokens_ > 0) {
     const auto lora_adapter = (model_.config_->config_path / fs::path(*model_.config_->model.vision.adapter_filename)).string();
@@ -1023,6 +1048,9 @@ DeviceSpan<float> MultiModalPipelineState::Run(int current_length, DeviceSpan<in
   //   - input_ids, image_features, audio_features -> |embeddings_model| -> inputs_embeds
   //   - inputs_embeds -> |decoder_model| -> logits
 
+  if (audio_output_) {
+    audio_output_->BeginStep(next_tokens, is_prompt_);
+  }
   embedding_state_->UpdateInputsOutputs(next_tokens, is_prompt_);
 
   // Prefill chunking (search.chunk_size): during the prompt stage the decoder can process the
@@ -1076,15 +1104,36 @@ DeviceSpan<float> MultiModalPipelineState::Run(int current_length, DeviceSpan<in
     if (vision_state_) vision_state_.reset();  // The vision state is no longer needed in generation stage
     if (speech_state_) speech_state_.reset();  // The speech state is no longer needed in generation stage
 
-    return logits;
+    return audio_output_ ? SampleAudioOrText(logits) : logits;
   }
 
   embedding_state_->inputs_embeds_.ReuseEmbeddingsBuffer(decoder_state_->inputs_embeds_);
   if (embedding_state_->per_layer_inputs_ && decoder_state_->per_layer_inputs_) {
     embedding_state_->per_layer_inputs_->ReuseEmbeddingsBuffer(*decoder_state_->per_layer_inputs_);
   }
-  embedding_state_->Run(current_length, next_tokens, next_indices);
-  return decoder_state_->Run(current_length, next_tokens, next_indices);
+  if (audio_output_ && audio_output_->HasPendingFrame()) {
+    // The token is only the placeholder of the last audio frame: the decoder takes the frame itself,
+    // and the embedding model would look for audio features to put in the placeholder's place.
+    audio_output_->WritePendingFrame(*decoder_state_->inputs_embeds_.Get());
+  } else {
+    embedding_state_->Run(current_length, next_tokens, next_indices);
+  }
+  auto logits = decoder_state_->Run(current_length, next_tokens, next_indices);
+  return audio_output_ ? SampleAudioOrText(logits) : logits;
+}
+
+DeviceSpan<float> MultiModalPipelineState::SampleAudioOrText(DeviceSpan<float> logits) {
+  if (!audio_output_->InAudio()) {
+    return logits;
+  }
+  const std::string& configured = model_.config_->model.decoder.outputs.hidden_states;
+  const std::string name = configured.empty() ? std::string(Config::Defaults::HiddenStatesName) : configured;
+  OrtValue* hidden_states = decoder_state_->GetOutput(name.c_str());
+  if (!hidden_states) {
+    throw std::runtime_error("Speech output needs the decoder's \"" + name +
+                             "\" output. Build the decoder with --extra_options include_hidden_states=true.");
+  }
+  return audio_output_->SampleFrame(*hidden_states);
 }
 
 OrtValue* MultiModalPipelineState::GetInput(const char* name) {
@@ -1124,6 +1173,10 @@ OrtValue* MultiModalPipelineState::GetInput(const char* name) {
 };
 
 OrtValue* MultiModalPipelineState::GetOutput(const char* name) {
+  if (audio_output_ && std::strcmp(name, "audio_codes") == 0) {
+    return audio_output_->GetAudioCodes();
+  }
+
   if (vision_state_) {
     // Check if output name is in vision state's outputs
     for (size_t i = 0; i < vision_state_->output_names_.size(); i++) {
diff --git a/src/models/multi_modal.h b/src/models/multi_modal.h
index 1b6ff60419..570e2fff24 100644
--- a/src/models/multi_modal.h
+++ b/src/models/multi_modal.h
@@ -19,6 +19,7 @@
 #include "models/io/position_inputs.h"
 #include "model_type.h"
 #include "models/io/recurrent_state.h"
+#include "models/lfm2_audio_output.h"
 
 namespace Generators {
 
@@ -37,6 +38,12 @@ struct MultiModalLanguageModel : Model {
   std::unique_ptr<OrtSessionOptions> vision_session_options_;
   std::unique_ptr<OrtSessionOptions> speech_session_options_;
   std::unique_ptr<OrtSessionOptions> embedding_session_options_;
+
+  // LFM2-Audio speech output, present when model.audio_output names the two graphs.
+  std::unique_ptr<OrtSession> depthformer_session_;      // hidden_states -> one audio code per run, a frame in num_codebooks runs
+  std::unique_ptr<OrtSession> audio_embedding_session_;  // audio_codes -> audio_embeds, summed into the decoder's next input
+  std::unique_ptr<OrtSessionOptions> depthformer_session_options_;
+  std::unique_ptr<OrtSessionOptions> audio_embedding_session_options_;
 };
 
 // Base VisionState: runs vision.onnx with a single State::Run() call.
@@ -264,6 +271,8 @@ struct MultiModalPipelineState : State {
  private:
   void UpdateInputsOutputs(const DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices,
                            int current_length);
+  // The decoder's logits while the answer is text, or the next audio frame's placeholder while it is speech.
+  DeviceSpan<float> SampleAudioOrText(DeviceSpan<float> logits);
 
   const MultiModalLanguageModel& model_;
   int64_t num_image_tokens_{};
@@ -273,6 +282,7 @@ struct MultiModalPipelineState : State {
   std::unique_ptr<SpeechState> speech_state_;
   std::unique_ptr<EmbeddingState> embedding_state_;
   std::unique_ptr<DecoderState> decoder_state_;
+  std::unique_ptr<Lfm2AudioOutput> audio_output_;  // LFM2-Audio speech output, when the model has it
   std::shared_ptr<Adapters> adapters_;
   bool is_prompt_{true};
 
diff --git a/test/models/lfm2-audio/dummy_audio_embedding.onnx b/test/models/lfm2-audio/dummy_audio_embedding.onnx
new file mode 100644
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HcmV?d00001

diff --git a/test/models/lfm2-audio/dummy_text.onnx b/test/models/lfm2-audio/dummy_text.onnx
index 50c167b43b46baf389a4922b73d682c93ff97298..299c2d07ee050dedcc3b43edc85f9cb6aa558e68 100644
GIT binary patch
delta 96
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oC7C6a&2_Bpb*zlr>sXm)vI->GaG@xi9>~t5$L7Ss#ULOE02}um?f?J)

delta 35
rcmZpi&M|iy#{_AnpHnx=CbBj!VQpW+%D8<AE7MF?HYXM?1_40;=p725

diff --git a/test/python/create/create_dummy_lfm2_audio_models.py b/test/python/create/create_dummy_lfm2_audio_models.py
index 658775f598..4fc22a52f6 100644
--- a/test/python/create/create_dummy_lfm2_audio_models.py
+++ b/test/python/create/create_dummy_lfm2_audio_models.py
@@ -21,6 +21,14 @@
                         convolution over past.%d.conv, so generation only matches between runs when the
                         conv state is carried correctly.
 
+  dummy_depthformer.onnx, dummy_audio_embedding.onnx
+                        Speech output, with the contract of LiquidAI's vocoder_depthformer.onnx and
+                        audio_embedding.onnx. The depthformer gives one codebook's logits per run; they
+                        depend on the hidden state, the codebook, the code before it and the cache of
+                        the runs before, so a frame only comes out right when all four are carried.
+                        The decoder has the hidden_states output they read. genai_config.json leaves
+                        them out: the tests that use them add model.audio_output themselves.
+
 The tokenizer is checked in separately, and zipped: tokenizer.zip holds tokenizer.json and
 tokenizer_config.json from LiquidAI/LFM2.5-Audio-1.5B (LFM2-Audio-1.5B ships the same files), whose
 4.8 MB compresses to under 1 MB. test_lfm2_audio_models.py unpacks it into a temporary directory
@@ -131,6 +139,89 @@ def create_dummy_embedding_model(output_path: str, rng: np.random.Generator):
     _save(helper.make_graph(nodes, "embedding", inputs, outputs, initializer=[table]), output_path)
 
 
+NUM_CODEBOOKS = 8
+CODEBOOK_SIZE = 2049  # 2048 codes and the end-of-audio code
+DEPTH_SIZE = 16
+TABLE_ROWS = 251  # the embedding tables are folded to keep the files small; prime, so codebooks do not collide
+
+
+def create_dummy_depthformer_model(output_path: str, rng: np.random.Generator):
+    """One codebook's logits per run, with the inputs and outputs of LiquidAI's vocoder_depthformer.onnx"""
+    cache_shape = [1, "batch", 1, "past_len", DEPTH_SIZE]
+    inputs = [
+        helper.make_tensor_value_info("hidden_states", TensorProto.FLOAT, ["batch", HIDDEN_SIZE]),
+        helper.make_tensor_value_info("depth_slices_in", TensorProto.FLOAT, ["batch", NUM_CODEBOOKS, DEPTH_SIZE]),
+        helper.make_tensor_value_info("step_idx", TensorProto.INT64, []),
+        helper.make_tensor_value_info("prev_token", TensorProto.INT64, ["batch"]),
+        helper.make_tensor_value_info("past_keys", TensorProto.FLOAT, cache_shape),
+        helper.make_tensor_value_info("past_values", TensorProto.FLOAT, cache_shape),
+        helper.make_tensor_value_info("seqlens_k", TensorProto.INT32, ["batch"]),
+        helper.make_tensor_value_info("total_seq_len", TensorProto.INT32, []),
+    ]
+    new_cache_shape = [1, "batch", 1, "new_len", DEPTH_SIZE]
+    outputs = [
+        helper.make_tensor_value_info("logits", TensorProto.FLOAT, ["batch", CODEBOOK_SIZE]),
+        helper.make_tensor_value_info("depth_slices", TensorProto.FLOAT, ["batch", NUM_CODEBOOKS, DEPTH_SIZE]),
+        helper.make_tensor_value_info("new_keys", TensorProto.FLOAT, new_cache_shape),
+        helper.make_tensor_value_info("new_values", TensorProto.FLOAT, new_cache_shape),
+    ]
+    initializers = [
+        numpy_helper.from_array(
+            rng.standard_normal((HIDDEN_SIZE, NUM_CODEBOOKS * DEPTH_SIZE)).astype(np.float32), name="depth_linear"
+        ),
+        numpy_helper.from_array(rng.standard_normal((TABLE_ROWS, DEPTH_SIZE)).astype(np.float32), name="code_table"),
+        numpy_helper.from_array(rng.standard_normal((DEPTH_SIZE, CODEBOOK_SIZE)).astype(np.float32), name="to_logits"),
+    ]
+    nodes = [
+        _const("slices_shape", [-1, NUM_CODEBOOKS, DEPTH_SIZE]),
+        _const("cache_entry_shape", [1, -1, 1, 1, DEPTH_SIZE]),
+        _const("zero", [0], dims=[]),
+        _const("past_axis", [3]),
+        _const("table_rows", [TABLE_ROWS]),
+        # The projection of the hidden state is only computed on the first codebook's run; later runs
+        # are handed it back, as the real graph does to skip the projection.
+        helper.make_node("MatMul", ["hidden_states", "depth_linear"], ["projected"]),
+        helper.make_node("Reshape", ["projected", "slices_shape"], ["computed_slices"]),
+        helper.make_node("Equal", ["step_idx", "zero"], ["is_first"]),
+        helper.make_node("Where", ["is_first", "computed_slices", "depth_slices_in"], ["depth_slices"]),
+        helper.make_node("Gather", ["depth_slices", "step_idx"], ["slice"], axis=1),
+        helper.make_node("Mod", ["prev_token", "table_rows"], ["previous_row"]),
+        helper.make_node("Gather", ["code_table", "previous_row"], ["previous"], axis=0),
+        helper.make_node("Add", ["slice", "previous"], ["position"]),
+        # What the earlier codebooks of this frame left in the cache.
+        helper.make_node("ReduceSum", ["past_keys", "past_axis"], ["history_5d"], keepdims=0),
+        helper.make_node("Reshape", ["history_5d", _shape_name("history")], ["history"]),
+        helper.make_node("Add", ["position", "history"], ["mixed"]),
+        helper.make_node("MatMul", ["mixed", "to_logits"], ["logits"]),
+        helper.make_node("Reshape", ["position", "cache_entry_shape"], ["cache_entry"]),
+        helper.make_node("Concat", ["past_keys", "cache_entry"], ["new_keys"], axis=3),
+        helper.make_node("Concat", ["past_values", "cache_entry"], ["new_values"], axis=3),
+    ]
+    nodes.insert(0, _const(_shape_name("history"), [-1, DEPTH_SIZE]))
+    _save(helper.make_graph(nodes, "depthformer", inputs, outputs, initializer=initializers), output_path)
+
+
+def _shape_name(name: str) -> str:
+    return f"{name}_shape"
+
+
+def create_dummy_audio_embedding_model(output_path: str, rng: np.random.Generator):
+    """audio_codes [batch, length], each offset into its codebook's rows -> audio_embeds [batch, length, hidden]"""
+    inputs = [helper.make_tensor_value_info("audio_codes", TensorProto.INT64, ["batch_size", "audio_length"])]
+    outputs = [
+        helper.make_tensor_value_info("audio_embeds", TensorProto.FLOAT, ["batch_size", "audio_length", HIDDEN_SIZE])
+    ]
+    table = numpy_helper.from_array(
+        rng.standard_normal((TABLE_ROWS, HIDDEN_SIZE)).astype(np.float32), name="audio_table"
+    )
+    nodes = [
+        _const("table_rows", [TABLE_ROWS]),
+        helper.make_node("Mod", ["audio_codes", "table_rows"], ["rows"]),
+        helper.make_node("Gather", ["audio_table", "rows"], ["audio_embeds"], axis=0),
+    ]
+    _save(helper.make_graph(nodes, "audio_embedding", inputs, outputs, initializer=[table]), output_path)
+
+
 def create_genai_config(output_path: str):
     session_options = {"log_id": "onnxruntime-genai", "provider_options": []}
     config = {
@@ -214,7 +305,9 @@ def main():
     rng = np.random.default_rng(42)
     create_dummy_speech_model(os.path.join(args.output, "dummy_speech.onnx"), rng)
     create_dummy_embedding_model(os.path.join(args.output, "dummy_embedding.onnx"), rng)
-    create_dummy_decoder_model(os.path.join(args.output, "dummy_text.onnx"), rng)
+    create_dummy_decoder_model(os.path.join(args.output, "dummy_text.onnx"), rng, hidden_states_output=True)
+    create_dummy_depthformer_model(os.path.join(args.output, "dummy_depthformer.onnx"), rng)
+    create_dummy_audio_embedding_model(os.path.join(args.output, "dummy_audio_embedding.onnx"), rng)
     create_genai_config(os.path.join(args.output, "genai_config.json"))
     print(f"Wrote dummy LFM2-Audio models to {args.output}")
 
diff --git a/test/python/create/create_dummy_lfm2_vl_models.py b/test/python/create/create_dummy_lfm2_vl_models.py
index d64d1ce977..cabb39713a 100644
--- a/test/python/create/create_dummy_lfm2_vl_models.py
+++ b/test/python/create/create_dummy_lfm2_vl_models.py
@@ -119,8 +119,12 @@ def create_dummy_embedding_model(output_path: str, rng: np.random.Generator):
     _save(helper.make_graph(nodes, "embedding", inputs, outputs, initializer=[table]), output_path)
 
 
-def create_dummy_decoder_model(output_path: str, rng: np.random.Generator):
-    """inputs_embeds, attention_mask, KV cache, conv state -> logits, present KV cache, present conv state"""
+def create_dummy_decoder_model(output_path: str, rng: np.random.Generator, hidden_states_output: bool = False):
+    """inputs_embeds, attention_mask, KV cache, conv state -> logits, present KV cache, present conv state
+
+    With hidden_states_output the values the logits are projected from come out too, as a decoder built
+    with include_hidden_states=true gives them.
+    """
     conv_layers = [i for i, t in enumerate(LAYER_TYPES) if t == "conv"]
     attention_layers = [i for i, t in enumerate(LAYER_TYPES) if t == "full_attention"]
     kv_shape_past = ["batch_size", NUM_KV_HEADS, "past_sequence_length", HEAD_SIZE]
@@ -189,6 +193,14 @@ def create_dummy_decoder_model(output_path: str, rng: np.random.Generator):
                 helper.make_node("Concat", [f"past_key_values.{i}.{kind}", "kv_new"], [f"present.{i}.{kind}"], axis=2)
             )
 
+    if hidden_states_output:
+        outputs.append(
+            helper.make_tensor_value_info(
+                "hidden_states", TensorProto.FLOAT, ["batch_size", "sequence_length", HIDDEN_SIZE]
+            )
+        )
+        nodes.append(helper.make_node("Identity", ["mixed_bsh"], ["hidden_states"]))
+
     _save(helper.make_graph(nodes, "decoder", inputs, outputs, initializer=initializers), output_path)
 
 
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index 75bea66c23..f944be4af8 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -754,6 +754,253 @@ def test_lfm2_audio_fixture_config_is_what_the_builder_writes(test_data_path):
     assert config["model"]["eos_token_id"] == [7, AUDIO_START_TOKEN_ID, TEXT_END_TOKEN_ID]
 
 
+# Speech output. The fixture's depthformer and audio embedding have the contract of LiquidAI's exports,
+# and its decoder has the hidden_states output they read; model.audio_output switches them on.
+NUM_CODEBOOKS = 8
+CODEBOOK_SIZE = 2049
+INTERLEAVED_N_TEXT = 6
+INTERLEAVED_N_AUDIO = 12
+
+
+def _speech_model(test_data_path, tmp_path, **audio_output) -> Path:
+    model_path = _copy_model(test_data_path, tmp_path)
+
+    def edit(config):
+        # The modality-switch tokens must not be stop tokens once there is speech to switch to.
+        config["model"]["eos_token_id"] = 7
+        config["model"]["audio_output"] = {
+            "depthformer": {"filename": "dummy_depthformer.onnx"},
+            "embedding": {"filename": "dummy_audio_embedding.onnx"},
+            **audio_output,
+        }
+
+    _edit_json(model_path / "genai_config.json", edit)
+    return model_path
+
+
+def _generate_speech(model_path, prompt, audios, num_items, **search):
+    model = og.Model(os.fspath(model_path))
+    inputs = model.create_multimodal_processor()(prompt, audios=audios)
+    prompt_length = inputs["input_ids"].as_numpy().shape[1]
+    params = og.GeneratorParams(model)
+    params.set_search_options(do_sample=False, max_length=prompt_length + num_items, **search)
+    generator = og.Generator(model, params)
+    generator.set_inputs(inputs)
+    while not generator.is_done():
+        generator.generate_next_token()
+    return inputs, generator.get_sequence(0)[prompt_length:].tolist(), generator.get_output("audio_codes")
+
+
+def _reference_speech(model_path, inputs, num_items, interleaved, audio_start=128, codebook_size=CODEBOOK_SIZE):
+    """liquid-audio's generate_sequential / generate_interleaved over the fixture, audio taken greedily.
+
+    Returns the generated sequence, with AUDIO_TOKEN_ID standing for each audio frame as the runtime
+    records it, and the frames themselves without the end-of-audio ones.
+    """
+    ort = pytest.importorskip("onnxruntime")
+    config = json.loads((Path(model_path) / "genai_config.json").read_text())["model"]
+    decoder_config = config["decoder"]
+    session = lambda name: ort.InferenceSession(os.path.join(model_path, name))  # noqa: E731
+    speech, embedding, decoder = (session(config[k]["filename"]) for k in ("speech", "embedding", "decoder"))
+    depthformer, audio_embedding = session("dummy_depthformer.onnx"), session("dummy_audio_embedding.onnx")
+    hidden_size = decoder_config["hidden_size"]
+    no_features = np.zeros((0, hidden_size), np.float32)
+
+    features = no_features
+    if inputs["audio_sizes"].as_numpy().sum() > 0:
+        mel, frames = inputs["audio_embeds"].as_numpy(), inputs["audio_lengths"].as_numpy()
+        features = np.concatenate(
+            [
+                speech.run(None, {"mel_spectrogram": mel[i : i + 1, :n], "mel_lengths": frames[i : i + 1]})[0][0]
+                for i, n in enumerate(frames)
+            ]
+        )
+    embeds = embedding.run(
+        None, {"input_ids": inputs["input_ids"].as_numpy().astype(np.int64), "audio_features": features}
+    )[0]
+
+    state = {}
+    for layer, layer_type in enumerate(decoder_config["layer_types"]):
+        if layer_type == "conv":
+            state[f"past.{layer}.conv"] = np.zeros((1, hidden_size, decoder_config["conv_cache_size"]), np.float32)
+        else:
+            for kind in ("key", "value"):
+                state[f"past_key_values.{layer}.{kind}"] = np.zeros(
+                    (1, decoder_config["num_key_value_heads"], 0, decoder_config["head_size"]), np.float32
+                )
+    output_names = [output.name for output in decoder.get_outputs()]
+
+    def sample_frame(hidden):
+        keys = np.zeros((1, 1, 1, 0, 16), np.float32)
+        values, slices, previous, frame = keys.copy(), np.zeros((1, NUM_CODEBOOKS, 16), np.float32), 0, []
+        for step in range(NUM_CODEBOOKS):
+            logits, new_slices, keys, values = depthformer.run(
+                ["logits", "depth_slices", "new_keys", "new_values"],
+                {
+                    "hidden_states": hidden[None],
+                    "depth_slices_in": slices,
+                    "step_idx": np.array(step, np.int64),
+                    "prev_token": np.array([previous], np.int64),
+                    "past_keys": keys,
+                    "past_values": values,
+                    "seqlens_k": np.array([step], np.int32),
+                    "total_seq_len": np.array(step + 1, np.int32),
+                },
+            )
+            slices = new_slices if step == 0 else slices
+            previous = int(logits[0, :codebook_size].argmax())
+            frame.append(previous)
+        return np.array(frame, np.int64)
+
+    sequence, frames, total = [], [], 0
+    in_audio, left, text_done = False, INTERLEAVED_N_TEXT, False
+    for _ in range(num_items):
+        left -= 1
+        total += embeds.shape[1]
+        feeds = {"inputs_embeds": embeds, "attention_mask": np.ones((1, total), np.int64), **state}
+        outputs = dict(zip(output_names, decoder.run(None, feeds), strict=True))
+        for name in state:
+            state[name] = outputs[name.replace("past_key_values.", "present.").replace("past.", "present.")]
+
+        if not in_audio:
+            token = int(outputs["logits"][0, -1].argmax())
+            sequence.append(token)
+            if token == 7:
+                break
+            if interleaved:
+                text_done = text_done or token == 130
+                if left == 0 or text_done:
+                    in_audio, left = True, INTERLEAVED_N_AUDIO
+            elif token == audio_start:
+                in_audio = True
+            embeds = embedding.run(None, {"input_ids": np.array([[token]], np.int64), "audio_features": no_features})[0]
+        else:
+            frame = sample_frame(outputs["hidden_states"][0, -1])
+            if interleaved and left == 0 and not text_done:
+                in_audio, left = False, INTERLEAVED_N_TEXT
+            if frame[0] == codebook_size - 1:
+                frame[:] = codebook_size - 1
+                in_audio = False
+            else:
+                frames.append(frame.copy())
+            sequence.append(AUDIO_TOKEN_ID)
+            rows = (frame + np.arange(NUM_CODEBOOKS) * codebook_size)[None]
+            embeds = audio_embedding.run(None, {"audio_codes": rows})[0].sum(axis=1, keepdims=True)
+    return sequence, np.array(frames, np.int64).reshape(-1, NUM_CODEBOOKS)
+
+
+@pytest.mark.parametrize("num_clips", [0, 1], ids=["typed", "spoken"])
+def test_lfm2_audio_interleaved_speech_matches_reference(test_data_path, tmp_path, num_clips):
+    # Six text tokens, twelve audio frames, and round again: each frame is eight codes from the
+    # depthformer, and its embedding rather than a token's is what the decoder reads next. The
+    # sequence keeps one placeholder per frame, and the codes come out on their own.
+    model_path = _speech_model(test_data_path, tmp_path)
+    clips = [_write_wav(tmp_path / "question.wav", _synthetic_signal(0.9, seed=70))] * num_clips
+    prompt = "<|startoftext|>Answer aloud. " + AUDIO_MARKER * num_clips
+
+    inputs, sequence, codes = _generate_speech(
+        model_path, prompt, og.Audios.open(*clips) if clips else None, 45, audio_interleaved=True, audio_top_k=1
+    )
+    expected_sequence, expected_codes = _reference_speech(os.fspath(model_path), inputs, 45, interleaved=True)
+
+    layout = "".join("A" if token == AUDIO_TOKEN_ID else "T" for token in sequence)
+    assert layout.startswith("T" * INTERLEAVED_N_TEXT + "A" * INTERLEAVED_N_AUDIO + "T" * INTERLEAVED_N_TEXT)
+    assert sequence == expected_sequence
+    np.testing.assert_array_equal(codes, expected_codes)
+    assert codes.shape == (layout.count("A"), NUM_CODEBOOKS)
+
+
+def _text_stream(test_data_path, tmp_path, num_tokens):
+    """What the fixture writes when nothing turns it to speech."""
+    clip = _write_wav(tmp_path / "probe.wav", _synthetic_signal(0.5, seed=71))
+    return _generate_with_eos(_copy_model(test_data_path, tmp_path / "probe"), clip, [7], num_tokens)
+
+
+def test_lfm2_audio_sequential_speech_starts_at_audio_start_and_ends_at_end_of_audio(test_data_path, tmp_path):
+    # TTS and the like: text until <|audio_start|>, speech until the end-of-audio code, then text
+    # again. The fixture emits neither on its own, so the start token is one it does write, and the
+    # codebook is cut down to sixteen entries so that the last of them, end-of-audio, comes up.
+    text = _text_stream(test_data_path, tmp_path, 12)
+    audio_start = text[3]
+    model_path = _speech_model(test_data_path, tmp_path, audio_start_token_id=audio_start, codebook_size=16)
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5, seed=71))
+
+    inputs, sequence, codes = _generate_speech(model_path, AUDIO_MARKER, og.Audios.open(clip), 60, audio_top_k=1)
+    expected_sequence, expected_codes = _reference_speech(
+        os.fspath(model_path), inputs, 60, interleaved=False, audio_start=audio_start, codebook_size=16
+    )
+
+    assert sequence[: text.index(audio_start) + 1] == text[: text.index(audio_start) + 1]
+    assert sequence[text.index(audio_start) + 1] == AUDIO_TOKEN_ID, "speech starts right after <|audio_start|>"
+    assert sequence == expected_sequence
+    np.testing.assert_array_equal(codes, expected_codes)
+    # End-of-audio frames hold a place in the sequence but are not speech.
+    assert 0 < len(codes) < sequence.count(AUDIO_TOKEN_ID)
+    assert codes[:, 0].max() < 15, "only the first codebook carries the end-of-audio code"
+    after_speech = sequence[text.index(audio_start) + 1 :]
+    assert any(token != AUDIO_TOKEN_ID for token in after_speech), "the turn goes back to text after end-of-audio"
+
+
+def test_lfm2_audio_speech_output_leaves_a_text_answer_alone(test_data_path, tmp_path):
+    text = _text_stream(test_data_path, tmp_path, 20)
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5, seed=71))
+
+    _, sequence, codes = _generate_speech(
+        _speech_model(test_data_path, tmp_path), AUDIO_MARKER, og.Audios.open(clip), 20
+    )
+
+    assert sequence == text
+    assert codes.shape == (0, NUM_CODEBOOKS)
+
+
+def test_lfm2_audio_speech_sampling_follows_the_seed(test_data_path, tmp_path):
+    model_path = _speech_model(test_data_path, tmp_path)
+    # The fixture's logits are far apart, so it takes a high temperature for the four likeliest to share the odds.
+    options = {"audio_interleaved": True, "audio_temperature": 100.0, "audio_top_k": 4}
+    run = lambda **search: _generate_speech(model_path, "Say something.", None, 30, **options, **search)[2]  # noqa: E731
+
+    first, again, other = run(random_seed=3), run(random_seed=3), run(random_seed=4)
+    greedy = _generate_speech(model_path, "Say something.", None, 30, audio_interleaved=True, audio_top_k=1)[2]
+
+    np.testing.assert_array_equal(first, again)
+    assert not np.array_equal(first, other)
+    assert not np.array_equal(first, greedy)
+    # The first frame follows text only, so it is sampled from the same logits whatever the seed:
+    # with top_k 4 every code in it has to be one of the four likeliest.
+    assert first.shape == greedy.shape and first.min() >= 0 and first.max() < CODEBOOK_SIZE
+
+
+@pytest.mark.parametrize(
+    "edit,expected",
+    [
+        (lambda m: m.update({"eos_token_id": [7, 128]}), "eos_token_id holds 128.*Remove it"),
+        (lambda m: m.update({"eos_token_id": [7, 130]}), "eos_token_id holds 130.*Remove it"),
+        (lambda m: m["audio_output"].pop("embedding"), "needs both depthformer.filename and embedding.filename"),
+        (lambda m: m["audio_output"].update({"num_codebooks": 4}), "expected inputs for 4 codebooks"),
+    ],
+    ids=["audio-start-is-a-stop-token", "text-end-is-a-stop-token", "one-graph-missing", "codebook-count"],
+)
+def test_lfm2_audio_rejects_a_speech_output_config_that_cannot_work(test_data_path, tmp_path, edit, expected):
+    model_path = _speech_model(test_data_path, tmp_path)
+    _edit_json(model_path / "genai_config.json", lambda config: edit(config["model"]))
+
+    with pytest.raises(RuntimeError, match=expected):
+        model = og.Model(os.fspath(model_path))
+        og.Generator(model, og.GeneratorParams(model))
+
+
+def test_lfm2_audio_speech_output_needs_the_decoder_hidden_states(test_data_path, tmp_path):
+    onnx = pytest.importorskip("onnx")
+    model_path = _speech_model(test_data_path, tmp_path)
+    decoder_path = model_path / "dummy_text.onnx"
+    decoder = onnx.load(decoder_path)
+    del decoder.graph.output[[output.name for output in decoder.graph.output].index("hidden_states")]
+    onnx.save(decoder, decoder_path)
+
+    with pytest.raises(RuntimeError, match="include_hidden_states=true"):
+        _generate_speech(model_path, "Say something.", None, 12, audio_interleaved=True)
+
+
 def _two_clip_inputs(model_path, tmp_path):
     clips = [_write_wav(tmp_path / f"{i}.wav", _synthetic_signal(1.5 - 0.5 * i, seed=50 + i)) for i in range(2)]
     model = og.Model(os.fspath(model_path))

From fb4699b7558040e025791bdd4d1bd5a83e54443c Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 08:14:05 -0400
Subject: [PATCH 17/23] docs: say which published speech graphs to take, and
 where parity was measured

All four published depthformers have the rotary defect and take the same fix; the
q4 depthformer and the q4 and q8 detokenizers lose enough to quantization that fp32
or fp16 are the ones to use, and the q4 and q8 audio embeddings are not quantized at
all. Greedy-audio parity with liquid-audio holds item for item on Linux x64 and on
macOS arm64.
---
 examples/python/lfm2-audio-tutorial.md | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index e544f493ad..293aeb9187 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -373,6 +373,21 @@ for table in model.graph.initializer:
 onnx.save(model, "vocoder_depthformer.onnx")
 ```
 
+The same defect is in all four published depthformers (`fp16`, `q4` and `q8` too), and the same fix
+applies to each. Which variants are worth taking, measured against the PyTorch model:
+
+| Graph | fp32 | fp16 | q8 | q4 |
+| --- | --- | --- | --- | --- |
+| `vocoder_depthformer` (fixed), largest logit difference | 9e-6 | 7e-3 | not measured | 2.0, and 37 of 48 codes agree |
+| `audio_detokenizer`, waveform SNR on real speech | 112 dB | 51 dB | 18 dB | 3 dB |
+| `audio_embedding`, against the checkpoint | exact | 6e-8 | exact | exact |
+
+Take `fp32` or `fp16`. The `q4` depthformer loses more to quantization than the rotary fix gains, and
+the `q4` detokenizer's waveform is barely correlated with the model's, though the speech is still
+understood: all four detokenizers read back word for word through ASR. The `q4` and `q8` audio
+embeddings are the `fp32` table under another name, so they save nothing. LiquidAI's own `decoder*.onnx`
+graphs, which this pipeline does not use, do apply their rotary embedding.
+
 **Add `audio_output` to `genai_config.json`, and take the two switch tokens out of `eos_token_id`.**
 They end a text-only answer; here they are where speech begins, and the model is refused if they are
 still stop tokens:
@@ -468,8 +483,8 @@ sf.write("answer.wav", (wave[pad:-pad] / envelope[pad:-pad]).astype(np.float32),
 **How it compares with the reference.** With the audio codes taken greedily on both sides
 (`audio_top_k=1`), so that the two can be compared at all, this runtime reproduces liquid-audio
 exactly: every text token and every audio frame of an interleaved answer to the reference's
-`question.wav` (200 items, 149 of them frames), of an interleaved answer to a typed question, and of a
-TTS sentence. With the model card's sampling the speech reads back through this runtime's own ASR as
+`question.wav` (200 items, 149 of them frames), of two interleaved answers to typed questions, and of a
+TTS sentence, on Linux x64 and on macOS arm64 alike. With the model card's sampling the speech reads back through this runtime's own ASR as
 the text the model wrote: *Red, blue, and yellow are the three primary colors. Would you like to hear
 how they’re used in art?* comes back word for word, and *The quick brown fox jumps over the lazy dog.*
 comes back exactly in TTS.

From 4507fbb482690960fdb55ee06df64e53945561da Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 10:04:54 -0400
Subject: [PATCH 18/23] test: rebuild the tokenizer archive in code rather than
 in a comment

The recipe for refreshing tokenizer.zip sat in a comment, which CodeQL reads as
commented-out code. It is now a helper, with a test that rebuilding twice gives the
same bytes and the contents that are checked in.
---
 test/python/models/test_lfm2_audio_models.py | 37 ++++++++++++++++----
 1 file changed, 30 insertions(+), 7 deletions(-)

diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index f944be4af8..418888bdcf 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -61,16 +61,22 @@
 
 
 # The tokenizer is checked in zipped: its 4.8 MB of vocabulary compresses to under 1 MB, and no test
-# reads it as text. The files come from LiquidAI/LFM2.5-Audio-1.5B. To rebuild the same bytes:
-#   import zipfile
-#   with zipfile.ZipFile("tokenizer.zip", "w", zipfile.ZIP_DEFLATED, compresslevel=9) as archive:
-#       for name in ("tokenizer.json", "tokenizer_config.json"):
-#           info = zipfile.ZipInfo(name, date_time=(1980, 1, 1, 0, 0, 0))
-#           info.compress_type, info.external_attr = zipfile.ZIP_DEFLATED, 0o644 << 16
-#           archive.writestr(info, open(name, "rb").read(), compresslevel=9)
+# reads it as text. The files come from LiquidAI/LFM2.5-Audio-1.5B; _write_tokenizer_archive rebuilds it.
 TOKENIZER_ARCHIVE = "tokenizer.zip"
 TOKENIZER_FILES = ("tokenizer.json", "tokenizer_config.json")
 
+
+def _write_tokenizer_archive(directory: Path, archive_path: Path):
+    """Zips the tokenizer files in `directory`, with the timestamp and mode pinned so that refreshing
+    the archive from the same files does not show up as a changed binary."""
+    with zipfile.ZipFile(archive_path, "w", zipfile.ZIP_DEFLATED, compresslevel=9) as archive:
+        for name in TOKENIZER_FILES:
+            info = zipfile.ZipInfo(name, date_time=(1980, 1, 1, 0, 0, 0))
+            info.compress_type = zipfile.ZIP_DEFLATED
+            info.external_attr = 0o644 << 16
+            archive.writestr(info, (directory / name).read_bytes(), compresslevel=9)
+
+
 # One assembled directory per source, kept alive (and so undeleted) for the whole session.
 _MODEL_DIRECTORIES: dict[str, tempfile.TemporaryDirectory] = {}
 
@@ -101,6 +107,23 @@ def _model_path(test_data_path) -> str:
     return _MODEL_DIRECTORIES[key].name
 
 
+def test_lfm2_audio_tokenizer_archive_can_be_rebuilt(test_data_path, tmp_path):
+    # Rebuilding from the unpacked files gives the same archive every time, holding what is checked in.
+    unpacked = Path(_model_path(test_data_path))
+    _write_tokenizer_archive(unpacked, tmp_path / "first.zip")
+    _write_tokenizer_archive(unpacked, tmp_path / "second.zip")
+
+    assert (tmp_path / "first.zip").read_bytes() == (tmp_path / "second.zip").read_bytes()
+    with (
+        zipfile.ZipFile(tmp_path / "first.zip") as rebuilt,
+        zipfile.ZipFile(Path(test_data_path) / "lfm2-audio" / TOKENIZER_ARCHIVE) as checked_in,
+    ):
+        assert rebuilt.namelist() == checked_in.namelist() == list(TOKENIZER_FILES)
+        for name in TOKENIZER_FILES:
+            assert rebuilt.read(name) == checked_in.read(name)
+            assert rebuilt.getinfo(name).date_time == checked_in.getinfo(name).date_time
+
+
 def _copy_model(test_data_path, tmp_path) -> Path:
     destination = tmp_path / "lfm2-audio"
     shutil.copytree(_model_path(test_data_path), destination)

From d15d15ba8552329d7bab64926b6e7b477e0f48c8 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 11:28:11 -0400
Subject: [PATCH 19/23] fix: read the decoder's tensors from the device they
 are really on

Speech output crashed the moment it sampled its first audio frame on any
execution provider that does not keep the decoder's tensors in device
memory. SampleFrame and WritePendingFrame wrapped hidden_states and
inputs_embeds with the model's device interface, but genai allocates
those from p_device_inputs_, which is plain CPU memory for WebGPU
without graph capture. The EP was then handed a host pointer as if it
were one of its buffers and aborted inside its own copy.

Pick the interface from the tensor's memory info instead, the way
nemotron_speech.cpp already does for encoder outputs, as a shared
DeviceForTensor next to ByteWrapTensor.

Measured on an A10 (Linux) and an M3 Ultra (macOS): interleaved mode and
TTS on WebGPU now match the liquid-audio reference token for token and
frame for frame, and CPU is unchanged.
---
 src/generator/generators.h       | 6 ++++++
 src/models/lfm2_audio_output.cpp | 4 ++--
 src/models/utils.cpp             | 5 +++++
 3 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/src/generator/generators.h b/src/generator/generators.h
index 4a865a374f..40a1ee4734 100644
--- a/src/generator/generators.h
+++ b/src/generator/generators.h
@@ -70,6 +70,12 @@ DeviceSpan<T> WrapTensor(DeviceInterface& device, OrtValue& value) {
 
 DeviceSpan<uint8_t> ByteWrapTensor(DeviceInterface& device, OrtValue& value);
 
+// The interface that matches where a tensor's memory actually is. A model that runs on a device does
+// not necessarily keep its tensors there: WebGPU without graph capture leaves them host-side, and ORT
+// places some outputs on the CPU whatever the EP. Wrapping such a tensor with the device interface
+// hands a host pointer to the EP's copy, which crashes inside it.
+DeviceInterface& DeviceForTensor(const OrtValue& value, DeviceInterface& device);
+
 // OgaSequences are a vector of int32 vectors
 using TokenSequences = std::vector<std::vector<int32_t>>;
 
diff --git a/src/models/lfm2_audio_output.cpp b/src/models/lfm2_audio_output.cpp
index b0a3190846..9a5694b723 100644
--- a/src/models/lfm2_audio_output.cpp
+++ b/src/models/lfm2_audio_output.cpp
@@ -115,7 +115,7 @@ void Lfm2AudioOutput::WritePendingFrame(OrtValue& inputs_embeds) {
                              std::to_string(info->GetElementCount()) + ".");
   }
 
-  auto bytes = ByteWrapTensor(*model_.p_device_, inputs_embeds);
+  auto bytes = ByteWrapTensor(DeviceForTensor(inputs_embeds, *model_.p_device_inputs_), inputs_embeds);
   auto cpu = bytes.CpuSpan();
   if (info->GetElementType() == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
     std::memcpy(cpu.data(), pending_embedding_.data(), pending_embedding_.size() * sizeof(float));
@@ -140,7 +140,7 @@ DeviceSpan<float> Lfm2AudioOutput::SampleFrame(OrtValue& hidden_states) {
   // The last position of [batch, sequence, hidden_size].
   const size_t count = static_cast<size_t>(hidden_size_);
   const size_t first = info->GetElementCount() - count;
-  auto bytes = ByteWrapTensor(*model_.p_device_, hidden_states).CopyDeviceToCpu();
+  auto bytes = ByteWrapTensor(DeviceForTensor(hidden_states, *model_.p_device_inputs_), hidden_states).CopyDeviceToCpu();
   std::vector<float> hidden(count);
   if (info->GetElementType() == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
     std::memcpy(hidden.data(), bytes.data() + first * sizeof(float), count * sizeof(float));
diff --git a/src/models/utils.cpp b/src/models/utils.cpp
index 60c1f86bbf..0a270664c3 100644
--- a/src/models/utils.cpp
+++ b/src/models/utils.cpp
@@ -10,6 +10,11 @@ DeviceSpan<uint8_t> ByteWrapTensor(DeviceInterface& device, OrtValue& value) {
   return device.WrapMemory(std::span<uint8_t>{value.GetTensorMutableData<uint8_t>(), info->GetElementCount() * Ort::SizeOf(info->GetElementType())});
 }
 
+DeviceInterface& DeviceForTensor(const OrtValue& value, DeviceInterface& device) {
+  const bool on_cpu = value.GetTensorMemoryInfo().GetDeviceType() == OrtMemoryInfoDeviceType_CPU;
+  return on_cpu ? *GetDeviceInterface(DeviceType::CPU) : device;
+}
+
 const char* TypeToString(ONNXTensorElementDataType type) {
   switch (type) {
     case Ort::TypeToTensorType<uint8_t>:

From b8ed7bf56b8f3f003d0138b9b669248541ea2b92 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 11:38:22 -0400
Subject: [PATCH 20/23] docs: say which execution providers the model runs on,
 having measured them

CPU was the only provider the tutorial spoke for. CUDA matches it once
every graph runs on the GPU, WebGPU only for the modes that take no
audio, and a half-and-half configuration crashes rather than falling
back, so say all of that where the other limitations are.
---
 examples/python/lfm2-audio-tutorial.md | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 293aeb9187..63165273d2 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -534,5 +534,21 @@ implementation. The difference is far below the audio's own quantization noise.
 log-mel frames by up to about 2, enough to flip a token: on the reference's 24 kHz `asr_jp.wav` it
 turned one homophone, and resampled this way the transcript matches token for token.
 
+**CPU is the verified path; a GPU needs every graph on it.** All three modes were checked against
+the reference on CPU, x64 and arm64, and match token for token and frame for frame. On **CUDA** they
+match too, but only once *every* graph runs there: build the model for CUDA, or leave the embedding
+and speech entries without their own `session_options` so they inherit the decoder's provider. A
+mixed configuration — the decoder on the GPU and the embedding or encoder left on CPU, which is what
+a CPU export's `genai_config.json` describes — crashes, because the runtime keeps the pipeline's
+buffers in device memory and a CPU kernel cannot write them. The encoder's convolutions need cuDNN
+on the library path. Expect the audio codes to drift a little against a CPU run: on one of the test
+clips two of 1192 codes differed, both in the last two codebooks, with every text token identical.
+
+**WebGPU runs the text-in modes only.** Interleaved and TTS match the reference once the
+`audio_output` graphs keep their own CPU `session_options`: ONNX Runtime's WebGPU
+`GroupQueryAttention` cannot start the depthformer from an empty KV cache. Speech *input* does not
+work there at all — the WebGPU EP fails to create a session for the encoder, in its fused `Conv`
+kernel. Both are ONNX Runtime issues, reproducible without this runtime.
+
 **Dither is off, as in the reference's eval mode.** NeMo adds 1e-5 of white noise to the samples
 during training only; the runtime never does.

From bdfafc58ee90ef139859a867d1e72425d84a0073 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Mon, 21 Sep 2026 12:29:04 -0400
Subject: [PATCH 21/23] docs: correct what actually stops the GPU providers,
 now each is filed

Two of the three claims in the providers note were wrong once the
failures were reduced to minimal cases. The WebGPU attention failure is
not about starting from an empty cache but about the present outgrowing
its buffer, on every step. Speech input is not blocked by the encoder
graph, which loads and runs on WebGPU at ORT_ENABLE_BASIC; it is blocked
by this runtime putting the features buffer on the decoder's device.

Point at the two issues so a reader can see where each fix has to land.
---
 examples/python/lfm2-audio-tutorial.md | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 63165273d2..560b99a6e4 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -539,16 +539,22 @@ the reference on CPU, x64 and arm64, and match token for token and frame for fra
 match too, but only once *every* graph runs there: build the model for CUDA, or leave the embedding
 and speech entries without their own `session_options` so they inherit the decoder's provider. A
 mixed configuration — the decoder on the GPU and the embedding or encoder left on CPU, which is what
-a CPU export's `genai_config.json` describes — crashes, because the runtime keeps the pipeline's
-buffers in device memory and a CPU kernel cannot write them. The encoder's convolutions need cuDNN
+a CPU export's `genai_config.json` describes — corrupts memory and usually crashes: the encoder is
+handed an output buffer that lives on the decoder's device and writes host bytes into it
+([onnxruntime-genai#2604](https://github.com/microsoft/onnxruntime-genai/issues/2604)). The encoder's convolutions need cuDNN
 on the library path. Expect the audio codes to drift a little against a CPU run: on one of the test
 clips two of 1192 codes differed, both in the last two codebooks, with every text token identical.
 
-**WebGPU runs the text-in modes only.** Interleaved and TTS match the reference once the
-`audio_output` graphs keep their own CPU `session_options`: ONNX Runtime's WebGPU
-`GroupQueryAttention` cannot start the depthformer from an empty KV cache. Speech *input* does not
-work there at all — the WebGPU EP fails to create a session for the encoder, in its fused `Conv`
-kernel. Both are ONNX Runtime issues, reproducible without this runtime.
+**WebGPU runs the text-in modes.** Interleaved and TTS match the reference once the `audio_output`
+graphs keep their own CPU `session_options`: ONNX Runtime's WebGPU `GroupQueryAttention` fails
+whenever the present KV cache has to grow past the buffer it was given, which is every step of the
+depthformer's loop ([onnxruntime#32716](https://github.com/microsoft/onnxruntime/issues/32716)).
+Speech *input* does not work there yet, for a reason of this runtime's own rather than the
+encoder's: the features buffer lands on the wrong device
+([onnxruntime-genai#2604](https://github.com/microsoft/onnxruntime-genai/issues/2604)). Note also
+that the shipped `onnxruntime-ep-webgpu` 0.3.0 cannot load the encoder at the default optimisation
+level — it rejects the fused `Conv` activation that a current `onnxruntime` emits — but does load
+and run it at `ORT_ENABLE_BASIC`.
 
 **Dither is off, as in the reference's eval mode.** NeMo adds 1e-5 of white noise to the samples
 during training only; the runtime never does.

From 2ba2489f36ca480b40828d78c5ff237717d6f225 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Wed, 23 Sep 2026 12:26:16 -0400
Subject: [PATCH 22/23] fix: check LFM2-Audio config values and sub-model
 placement before touching memory

Review feedback on #2601:

- audio_token_id must lie inside the vocabulary: Lfm2AudioOutput indexes
  the placeholder logits with it and the processor writes it into
  input_ids. Only its sign was checked.
- codebook_size must be positive, and each depthformer run checks it
  against the logits the graph actually produced, since the width can
  be dynamic. A negative value became a huge span length and an
  oversized one read past the logits. The audio embedding output is
  now also checked to hold one embedding per codebook before
  EmbedFrame sums them.
- A speech or embedding graph that its own session_options leave on
  CPU is refused when a buffer it would be handed is device memory,
  rather than writing host bytes over it (#2604). The embedding graph
  is checked at load against the decoder's inputs; with audio, both
  graphs are checked against the features buffer. WebGPU without graph
  capture keeps its inputs on the host, so its text-only path with CPU
  sub-models is still accepted. OpenVINO and QNN allocate memory a CPU
  session can use and are not refused.
- The builder sets config.name_or_path instead of the private field.
---
 examples/python/lfm2-audio-tutorial.md        |  9 +--
 src/models/lfm2_audio_output.cpp              | 33 +++++++--
 src/models/multi_modal.cpp                    | 45 ++++++++++++
 src/models/multi_modal.h                      |  7 ++
 .../preprocessing/lfm2_audio_processor.cpp    |  4 ++
 src/python/py/models/builders/lfm2.py         |  2 +-
 test/cpp/lfm2_audio_frames_test.cpp           | 68 +++++++++++++++++++
 test/python/models/test_lfm2_audio_models.py  | 47 ++++++++++++-
 8 files changed, 205 insertions(+), 10 deletions(-)

diff --git a/examples/python/lfm2-audio-tutorial.md b/examples/python/lfm2-audio-tutorial.md
index 560b99a6e4..9bec5a9347 100644
--- a/examples/python/lfm2-audio-tutorial.md
+++ b/examples/python/lfm2-audio-tutorial.md
@@ -539,9 +539,9 @@ the reference on CPU, x64 and arm64, and match token for token and frame for fra
 match too, but only once *every* graph runs there: build the model for CUDA, or leave the embedding
 and speech entries without their own `session_options` so they inherit the decoder's provider. A
 mixed configuration — the decoder on the GPU and the embedding or encoder left on CPU, which is what
-a CPU export's `genai_config.json` describes — corrupts memory and usually crashes: the encoder is
-handed an output buffer that lives on the decoder's device and writes host bytes into it
-([onnxruntime-genai#2604](https://github.com/microsoft/onnxruntime-genai/issues/2604)). The encoder's convolutions need cuDNN
+a CPU export's `genai_config.json` describes — is refused with an error naming the entry to remove:
+the CPU graph would be handed buffers that live on the decoder's device and write host bytes into
+them ([onnxruntime-genai#2604](https://github.com/microsoft/onnxruntime-genai/issues/2604)). The encoder's convolutions need cuDNN
 on the library path. Expect the audio codes to drift a little against a CPU run: on one of the test
 clips two of 1192 codes differed, both in the last two codebooks, with every text token identical.
 
@@ -551,7 +551,8 @@ whenever the present KV cache has to grow past the buffer it was given, which is
 depthformer's loop ([onnxruntime#32716](https://github.com/microsoft/onnxruntime/issues/32716)).
 Speech *input* does not work there yet, for a reason of this runtime's own rather than the
 encoder's: the features buffer lands on the wrong device
-([onnxruntime-genai#2604](https://github.com/microsoft/onnxruntime-genai/issues/2604)). Note also
+([onnxruntime-genai#2604](https://github.com/microsoft/onnxruntime-genai/issues/2604)), so a prompt
+with audio is refused while the encoder or embedding keeps its own CPU `session_options`. Note also
 that the shipped `onnxruntime-ep-webgpu` 0.3.0 cannot load the encoder at the default optimisation
 level — it rejects the fused `Conv` activation that a current `onnxruntime` emits — but does load
 and run it at `ORT_ENABLE_BASIC`.
diff --git a/src/models/lfm2_audio_output.cpp b/src/models/lfm2_audio_output.cpp
index 9a5694b723..48899e686d 100644
--- a/src/models/lfm2_audio_output.cpp
+++ b/src/models/lfm2_audio_output.cpp
@@ -63,7 +63,7 @@ Lfm2AudioOutput::Lfm2AudioOutput(const MultiModalLanguageModel& model, const Gen
   const auto slices_shape = InputShape(*model_.depthformer_session_, "depth_slices_in", "depthformer");
   const auto keys_shape = InputShape(*model_.depthformer_session_, "past_keys", "depthformer");
   if (hidden_shape.size() != 2 || slices_shape.size() != 3 || keys_shape.size() != 5 ||
-      slices_shape[1] != config_.num_codebooks) {
+      config_.num_codebooks <= 0 || slices_shape[1] != config_.num_codebooks) {
     throw std::runtime_error("Lfm2AudioOutput: the depthformer model does not have the expected inputs for " +
                              std::to_string(config_.num_codebooks) + " codebooks.");
   }
@@ -73,9 +73,20 @@ Lfm2AudioOutput::Lfm2AudioOutput(const MultiModalLanguageModel& model, const Gen
   num_kv_heads_ = keys_shape[2];
   head_size_ = keys_shape[4];
 
+  // RunDepthformer checks it against the logits the depthformer produces.
+  if (config_.codebook_size <= 0) {
+    throw std::runtime_error("Lfm2AudioOutput: model.audio_output.codebook_size " +
+                             std::to_string(config_.codebook_size) + " must be positive.");
+  }
+
   // The search has to pick the placeholder whatever its sampling settings are.
-  const size_t vocab_size = static_cast<size_t>(model_.config_->model.vocab_size);
-  placeholder_logits_ = model_.p_device_->Allocate<float>(vocab_size);
+  const int vocab_size = model_.config_->model.vocab_size;
+  if (placeholder_token_id_ <= 0 || placeholder_token_id_ >= vocab_size) {
+    throw std::runtime_error("Lfm2AudioOutput: model.audio_token_id " + std::to_string(placeholder_token_id_) +
+                             " must be set to a token inside the vocabulary of " + std::to_string(vocab_size) +
+                             " tokens.");
+  }
+  placeholder_logits_ = model_.p_device_->Allocate<float>(static_cast<size_t>(vocab_size));
   auto logits = placeholder_logits_.CpuSpan();
   std::fill(logits.begin(), logits.end(), std::numeric_limits<float>::lowest());
   logits[static_cast<size_t>(placeholder_token_id_)] = 0.0f;
@@ -192,6 +203,15 @@ std::vector<int64_t> Lfm2AudioOutput::RunDepthformer(std::span<const float> hidd
     auto outputs = model_.depthformer_session_->Run(nullptr, kDepthformerInputs, inputs, std::size(inputs),
                                                     kDepthformerOutputs, std::size(kDepthformerOutputs));
 
+    // The code is sampled from the first codebook_size logits, so there have to be at least that many.
+    const auto logits_info = outputs[0]->GetTensorTypeAndShapeInfo();
+    const auto logits_shape = logits_info->GetShape();
+    if (logits_info->GetElementType() != ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT || logits_shape.empty() ||
+        logits_shape.back() < config_.codebook_size) {
+      throw std::runtime_error("Lfm2AudioOutput: model.audio_output.codebook_size is " +
+                               std::to_string(config_.codebook_size) + ", but the depthformer produces " +
+                               std::to_string(logits_shape.empty() ? 0 : logits_shape.back()) + " float logits.");
+    }
     const float* logits = outputs[0]->GetTensorData<float>();
     previous_code = SampleCode({logits, static_cast<size_t>(config_.codebook_size)});
     frame.push_back(previous_code);
@@ -235,7 +255,12 @@ void Lfm2AudioOutput::EmbedFrame(std::span<const int64_t> frame) {
   auto outputs = model_.audio_embedding_session_->Run(nullptr, kEmbeddingInputs, inputs, std::size(inputs),
                                                       kEmbeddingOutputs, std::size(kEmbeddingOutputs));
 
-  const auto shape = outputs[0]->GetTensorTypeAndShapeInfo()->GetShape();  // [1, num_codebooks, hidden_size]
+  const auto info = outputs[0]->GetTensorTypeAndShapeInfo();
+  const auto shape = info->GetShape();  // [1, num_codebooks, hidden_size]
+  if (info->GetElementType() != ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT || shape.empty() ||
+      info->GetElementCount() != frame.size() * static_cast<size_t>(shape.back())) {
+    throw std::runtime_error("Lfm2AudioOutput: the audio embedding model must produce one float embedding per codebook.");
+  }
   const size_t width = static_cast<size_t>(shape.back());
   const float* embeddings = outputs[0]->GetTensorData<float>();
   pending_embedding_.assign(width, 0.0f);
diff --git a/src/models/multi_modal.cpp b/src/models/multi_modal.cpp
index 1aedc0a012..c1982fb235 100644
--- a/src/models/multi_modal.cpp
+++ b/src/models/multi_modal.cpp
@@ -95,8 +95,51 @@ int64_t GetImageFeatureBatchSize(const std::vector<ExtraInput>& extra_inputs) {
 
 }  // namespace
 
+void CheckLfm2AudioSessionDevices(const Config& config, DeviceType decoder_device, DeviceType inputs_device,
+                                  bool with_audio) {
+  // Whether a buffer allocated on the device is device memory. OpenVINO allocates from the CPU and
+  // QNN from shared memory, both of which a CPU session can use.
+  const auto is_device_memory = [](DeviceType device) {
+    switch (device) {
+      case DeviceType::CUDA:
+      case DeviceType::DML:
+      case DeviceType::WEBGPU:
+      case DeviceType::NvTensorRtRtx:
+      case DeviceType::RyzenAI:
+      case DeviceType::AMDGPU:
+        return true;
+      default:
+        return false;
+    }
+  };
+  const auto check = [](const std::optional<Config::SessionOptions>& options, const char* graph,
+                        const char* buffers, DeviceType device) {
+    // Without session_options of its own a graph follows the decoder; with them, no provider but CPU
+    // leaves it on CPU.
+    if (!options.has_value() || !std::all_of(options->providers.begin(), options->providers.end(),
+                                             [](const std::string& provider) { return provider == "CPU"; })) {
+      return;
+    }
+    throw std::runtime_error(std::string("lfm2_audio: model.") + graph + ".session_options run the " + graph +
+                             " model on CPU, but " + buffers + " in " + to_string(device) +
+                             " memory, which it cannot use. Remove model." + graph +
+                             ".session_options so that it runs on the decoder's device.");
+  };
+
+  if (is_device_memory(inputs_device)) {
+    check(config.model.embedding.session_options, "embedding", "the decoder takes its inputs", inputs_device);
+  }
+  if (with_audio && is_device_memory(decoder_device)) {
+    check(config.model.speech.session_options, "speech", "the audio features are passed", decoder_device);
+    check(config.model.embedding.session_options, "embedding", "the audio features are passed", decoder_device);
+  }
+}
+
 MultiModalLanguageModel::MultiModalLanguageModel(std::unique_ptr<Config> config, OrtEnv& ort_env, bool vision, bool speech)
     : Model(std::move(config)) {
+  if (config_->model.type == "lfm2_audio") {
+    CheckLfm2AudioSessionDevices(*config_, p_device_->GetType(), p_device_inputs_->GetType(), /*with_audio=*/false);
+  }
   // The non-decoder models don't support graph capture because of control flow nodes, so disable graph capture for them
   if (vision) {
     vision_session_options_ = OrtSessionOptions::Create();
@@ -716,6 +759,8 @@ std::unique_ptr<OrtValue> Lfm2AudioSpeechState::RunClip(const SpeechBindings& bi
 }
 
 DeviceSpan<float> Lfm2AudioSpeechState::Run(int current_length, DeviceSpan<int32_t>& next_tokens, DeviceSpan<int32_t> next_indices) {
+  CheckLfm2AudioSessionDevices(*model_.config_, model_.p_device_->GetType(), model_.p_device_inputs_->GetType(),
+                               /*with_audio=*/true);
   if (model_.config_->model.speech.run_options.has_value()) {
     State::SetRunOptions(model_.config_->model.speech.run_options.value());
   }
diff --git a/src/models/multi_modal.h b/src/models/multi_modal.h
index 570e2fff24..35d0554b0c 100644
--- a/src/models/multi_modal.h
+++ b/src/models/multi_modal.h
@@ -23,6 +23,13 @@
 
 namespace Generators {
 
+// lfm2_audio exchanges tensors with its embedding and speech sessions in buffers allocated on the
+// decoder's devices. Throws if either has session_options of its own that leave it on CPU while a
+// buffer it would be handed is device memory, which the session would treat as host memory and
+// corrupt: the decoder's inputs for the embedding session, and with_audio, the audio features for both.
+void CheckLfm2AudioSessionDevices(const Config& config, DeviceType decoder_device, DeviceType inputs_device,
+                                  bool with_audio);
+
 struct MultiModalLanguageModel : Model {
   MultiModalLanguageModel(std::unique_ptr<Config> config, OrtEnv& ort_env, bool vision, bool speech);
   MultiModalLanguageModel(const MultiModalLanguageModel&) = delete;
diff --git a/src/models/preprocessing/lfm2_audio_processor.cpp b/src/models/preprocessing/lfm2_audio_processor.cpp
index 5c9f578360..11343f1f8c 100644
--- a/src/models/preprocessing/lfm2_audio_processor.cpp
+++ b/src/models/preprocessing/lfm2_audio_processor.cpp
@@ -262,6 +262,10 @@ Lfm2AudioProcessor::Lfm2AudioProcessor(Config& config, const SessionInfo& sessio
         "Lfm2AudioProcessor: model.audio_token_id must be set in genai_config.json to the id "
         "the embedding model replaces with the audio features.");
   }
+  if (audio_token_id_ >= config.model.vocab_size) {
+    throw std::runtime_error("Lfm2AudioProcessor: model.audio_token_id " + std::to_string(audio_token_id_) +
+                             " is outside the vocabulary of " + std::to_string(config.model.vocab_size) + " tokens.");
+  }
 
   // The mel front end defaults to the settings of the published checkpoints; a genai_config.json can
   // override any of them with the same top-level fields the Parakeet and Nemotron models use.
diff --git a/src/python/py/models/builders/lfm2.py b/src/python/py/models/builders/lfm2.py
index a87a1766a4..f0e053296a 100644
--- a/src/python/py/models/builders/lfm2.py
+++ b/src/python/py/models/builders/lfm2.py
@@ -223,7 +223,7 @@ def load_config(cls, model_name_or_path, **kwargs):
 
         config = Lfm2Config(**config_dict["lfm"])
         config.architectures = [architecture]
-        config._name_or_path = model_name_or_path
+        config.name_or_path = model_name_or_path
         return config
 
     def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
diff --git a/test/cpp/lfm2_audio_frames_test.cpp b/test/cpp/lfm2_audio_frames_test.cpp
index 69067c887f..16672a3848 100644
--- a/test/cpp/lfm2_audio_frames_test.cpp
+++ b/test/cpp/lfm2_audio_frames_test.cpp
@@ -7,6 +7,8 @@
 #include <string>
 #include <vector>
 
+#include "generator/generators.h"
+#include "models/multi_modal.h"
 #include "models/preprocessing/lfm2_audio_processor.h"
 
 // LFM2-Audio turns a clip into log-mel frames, one per 10 ms hop, and the encoder subsamples those
@@ -184,4 +186,70 @@ TEST(Lfm2AudioResampleTest, RejectsARateThatIsNotPositive) {
             std::string::npos);
 }
 
+// The embedding and speech sessions are handed buffers allocated on the decoder's devices, so neither
+// may be left on CPU by session_options of its own while such a buffer is device memory.
+namespace {
+
+Config CpuSubModelsConfig() {
+  Config config;  // as a CPU export writes it: both graphs have session_options with no provider
+  config.model.speech.session_options = Config::SessionOptions{};
+  config.model.embedding.session_options = Config::SessionOptions{};
+  return config;
+}
+
+}  // namespace
+
+TEST(Lfm2AudioSessionDevicesTest, ACpuEmbeddingCannotTakeTheInputsOfADecoderOnCuda) {
+  const auto message = CaptureThrowMessage([] {
+    CheckLfm2AudioSessionDevices(CpuSubModelsConfig(), DeviceType::CUDA, DeviceType::CUDA, /*with_audio=*/false);
+  });
+  EXPECT_NE(message.find("model.embedding.session_options run the embedding model on CPU, but the decoder takes "
+                         "its inputs in CUDA memory"),
+            std::string::npos)
+      << message;
+}
+
+TEST(Lfm2AudioSessionDevicesTest, CpuSubModelsTakeTextOnlyPromptsWhenTheInputsAreOnTheHost) {
+  // WebGPU without graph capture keeps the decoder's inputs on the host, so text-only prompts run.
+  EXPECT_NO_THROW(
+      CheckLfm2AudioSessionDevices(CpuSubModelsConfig(), DeviceType::WEBGPU, DeviceType::CPU, /*with_audio=*/false));
+
+  // Audio features are allocated on the decoder's device, which a CPU encoder cannot write.
+  const auto message = CaptureThrowMessage([] {
+    CheckLfm2AudioSessionDevices(CpuSubModelsConfig(), DeviceType::WEBGPU, DeviceType::CPU, /*with_audio=*/true);
+  });
+  EXPECT_NE(message.find("model.speech.session_options run the speech model on CPU, but the audio features are "
+                         "passed in WebGPU memory"),
+            std::string::npos)
+      << message;
+
+  // Nor can a CPU embedding read them.
+  auto config = CpuSubModelsConfig();
+  config.model.speech.session_options.reset();
+  EXPECT_NE(CaptureThrowMessage([&] {
+              CheckLfm2AudioSessionDevices(config, DeviceType::WEBGPU, DeviceType::CPU, /*with_audio=*/true);
+            }).find("model.embedding.session_options"),
+            std::string::npos);
+}
+
+TEST(Lfm2AudioSessionDevicesTest, SubModelsThatCanUseTheBuffersAreAccepted) {
+  // Without session_options of their own both graphs follow the decoder.
+  EXPECT_NO_THROW(CheckLfm2AudioSessionDevices(Config{}, DeviceType::CUDA, DeviceType::CUDA, /*with_audio=*/true));
+
+  auto config = CpuSubModelsConfig();
+  config.model.speech.session_options->providers = {"cuda"};
+  config.model.embedding.session_options->providers = {"cuda"};
+  EXPECT_NO_THROW(CheckLfm2AudioSessionDevices(config, DeviceType::CUDA, DeviceType::CUDA, /*with_audio=*/true));
+
+  // An explicit CPU provider is still CPU.
+  config.model.speech.session_options->providers = {"CPU"};
+  EXPECT_THROW(CheckLfm2AudioSessionDevices(config, DeviceType::CUDA, DeviceType::CUDA, /*with_audio=*/true),
+               std::runtime_error);
+
+  // Everything on CPU, and a decoder whose buffers a CPU session can use: OpenVINO allocates from the CPU.
+  for (const auto device : {DeviceType::CPU, DeviceType::OpenVINO}) {
+    EXPECT_NO_THROW(CheckLfm2AudioSessionDevices(CpuSubModelsConfig(), device, device, /*with_audio=*/true));
+  }
+}
+
 }  // namespace Generators::test
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index 418888bdcf..ae3b1096e2 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -589,6 +589,25 @@ def test_lfm2_audio_requires_audio_token_id(test_data_path, tmp_path):
         og.Model(os.fspath(model_path)).create_multimodal_processor()
 
 
+def test_lfm2_audio_requires_audio_token_id_inside_the_vocabulary(test_data_path, tmp_path):
+    model_path = _copy_model(test_data_path, tmp_path)
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"].update({"audio_token_id": 65536}))
+    with pytest.raises(RuntimeError, match="audio_token_id 65536 is outside the vocabulary of 65536 tokens"):
+        og.Model(os.fspath(model_path)).create_multimodal_processor()
+
+
+@pytest.mark.skipif(not og.is_cuda_available(), reason="needs a CUDA decoder next to the CPU sub-models")
+def test_lfm2_audio_rejects_cpu_sub_models_next_to_a_cuda_decoder(test_data_path, tmp_path):
+    # The fixture's speech and embedding entries have session_options of their own, as a CPU export
+    # writes them, so only the decoder moves to CUDA: the embedding session would be handed its inputs
+    # in CUDA memory and write host bytes over them.
+    config = og.Config(os.fspath(_copy_model(test_data_path, tmp_path)))
+    config.clear_providers()
+    config.append_provider("cuda")
+    with pytest.raises(RuntimeError, match="embedding.session_options run the embedding model on CPU"):
+        og.Model(config)
+
+
 def _generate(model_path: str, prompt: str, audios, num_tokens: int, chunk_size: int | None = None):
     model = og.Model(model_path)
     processor = model.create_multimodal_processor()
@@ -1000,8 +1019,23 @@ def test_lfm2_audio_speech_sampling_follows_the_seed(test_data_path, tmp_path):
         (lambda m: m.update({"eos_token_id": [7, 130]}), "eos_token_id holds 130.*Remove it"),
         (lambda m: m["audio_output"].pop("embedding"), "needs both depthformer.filename and embedding.filename"),
         (lambda m: m["audio_output"].update({"num_codebooks": 4}), "expected inputs for 4 codebooks"),
+        (lambda m: m["audio_output"].update({"num_codebooks": -8}), "expected inputs for -8 codebooks"),
+        (lambda m: m["audio_output"].update({"codebook_size": -1}), "codebook_size -1 must be positive"),
+        (lambda m: m["audio_output"].update({"codebook_size": 0}), "codebook_size 0 must be positive"),
+        (lambda m: m.update({"audio_token_id": 65536}), "audio_token_id 65536 .* vocabulary of 65536"),
+        (lambda m: m.update({"audio_token_id": -1}), "audio_token_id -1 .* vocabulary of 65536"),
+    ],
+    ids=[
+        "audio-start-is-a-stop-token",
+        "text-end-is-a-stop-token",
+        "one-graph-missing",
+        "codebook-count",
+        "negative-codebook-count",
+        "negative-codebook-size",
+        "zero-codebook-size",
+        "audio-token-past-the-vocabulary",
+        "negative-audio-token",
     ],
-    ids=["audio-start-is-a-stop-token", "text-end-is-a-stop-token", "one-graph-missing", "codebook-count"],
 )
 def test_lfm2_audio_rejects_a_speech_output_config_that_cannot_work(test_data_path, tmp_path, edit, expected):
     model_path = _speech_model(test_data_path, tmp_path)
@@ -1012,6 +1046,17 @@ def test_lfm2_audio_rejects_a_speech_output_config_that_cannot_work(test_data_pa
         og.Generator(model, og.GeneratorParams(model))
 
 
+def test_lfm2_audio_rejects_a_codebook_size_past_the_depthformer_logits(test_data_path, tmp_path):
+    # The codes are sampled from the first codebook_size logits; one more than there are would read
+    # past the end of them.
+    model_path = _speech_model(test_data_path, tmp_path, codebook_size=CODEBOOK_SIZE + 1)
+    with pytest.raises(
+        RuntimeError,
+        match=f"codebook_size is {CODEBOOK_SIZE + 1}, but the depthformer produces {CODEBOOK_SIZE} float logits",
+    ):
+        _generate_speech(model_path, "<|startoftext|>Answer aloud. ", None, 10, audio_interleaved=True, audio_top_k=1)
+
+
 def test_lfm2_audio_speech_output_needs_the_decoder_hidden_states(test_data_path, tmp_path):
     onnx = pytest.importorskip("onnx")
     model_path = _speech_model(test_data_path, tmp_path)

From 754198da4fd1ce1447303df08e771d01d6c561c8 Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <yuri@liquid.ai>
Date: Wed, 23 Sep 2026 12:41:08 -0400
Subject: [PATCH 23/23] test: cover every LFM2-Audio review check, the GPU
 placements included

- Depthformer logits that are not float get their own error instead of
  being reported as too few "float logits".
- New cases for the checks that had none: float16 logits, an audio
  embedding that returns fewer rows than codebooks, and a negative
  codebook count that a symbolic codebook dimension would otherwise
  match.
- The audio-time placement check now runs through the real pipeline
  rather than only the C++ matrix. On CUDA, text-only prompts run with
  the encoder left on CPU and an audio prompt is refused. On WebGPU, a
  CPU export's config keeps running text-only prompts and refuses
  audio. Both run where the EP is installed.
---
 src/models/lfm2_audio_output.cpp             |   9 +-
 test/python/models/test_lfm2_audio_models.py | 121 ++++++++++++++++++-
 2 files changed, 126 insertions(+), 4 deletions(-)

diff --git a/src/models/lfm2_audio_output.cpp b/src/models/lfm2_audio_output.cpp
index 48899e686d..f3d728450c 100644
--- a/src/models/lfm2_audio_output.cpp
+++ b/src/models/lfm2_audio_output.cpp
@@ -205,12 +205,15 @@ std::vector<int64_t> Lfm2AudioOutput::RunDepthformer(std::span<const float> hidd
 
     // The code is sampled from the first codebook_size logits, so there have to be at least that many.
     const auto logits_info = outputs[0]->GetTensorTypeAndShapeInfo();
+    if (logits_info->GetElementType() != ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
+      throw std::runtime_error(std::string("Lfm2AudioOutput: the depthformer's logits must be float, got ") +
+                               TypeToString(logits_info->GetElementType()) + ".");
+    }
     const auto logits_shape = logits_info->GetShape();
-    if (logits_info->GetElementType() != ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT || logits_shape.empty() ||
-        logits_shape.back() < config_.codebook_size) {
+    if (logits_shape.empty() || logits_shape.back() < config_.codebook_size) {
       throw std::runtime_error("Lfm2AudioOutput: model.audio_output.codebook_size is " +
                                std::to_string(config_.codebook_size) + ", but the depthformer produces " +
-                               std::to_string(logits_shape.empty() ? 0 : logits_shape.back()) + " float logits.");
+                               std::to_string(logits_shape.empty() ? 0 : logits_shape.back()) + " logits.");
     }
     const float* logits = outputs[0]->GetTensorData<float>();
     previous_code = SampleCode({logits, static_cast<size_t>(config_.codebook_size)});
diff --git a/test/python/models/test_lfm2_audio_models.py b/test/python/models/test_lfm2_audio_models.py
index ae3b1096e2..0ca15ce9fa 100644
--- a/test/python/models/test_lfm2_audio_models.py
+++ b/test/python/models/test_lfm2_audio_models.py
@@ -30,6 +30,7 @@
 import numpy as np
 import onnxruntime_genai as og
 import pytest
+from _test_utils import is_webgpu_ep_available
 
 AUDIO_MARKER = "<|audio|>"
 AUDIO_START_TOKEN_ID = 128  # <|audio_start|>: the answer is spoken from here on
@@ -608,6 +609,58 @@ def test_lfm2_audio_rejects_cpu_sub_models_next_to_a_cuda_decoder(test_data_path
         og.Model(config)
 
 
+def _run_prompt(model, prompt, audios, num_tokens):
+    inputs = model.create_multimodal_processor()(prompt, audios=audios)
+    params = og.GeneratorParams(model)
+    params.set_search_options(do_sample=False, max_length=inputs["input_ids"].as_numpy().shape[1] + num_tokens)
+    generator = og.Generator(model, params)
+    generator.set_inputs(inputs)
+    while not generator.is_done():
+        generator.generate_next_token()
+    return generator.get_sequence(0)
+
+
+def _model_on(model_path, provider):
+    config = og.Config(os.fspath(model_path))
+    config.clear_providers()
+    config.append_provider(provider)
+    return og.Model(config)
+
+
+@pytest.mark.skipif(not og.is_cuda_available(), reason="needs a CUDA decoder next to a CPU encoder")
+def test_lfm2_audio_refuses_audio_for_a_cpu_encoder_next_to_a_cuda_decoder(test_data_path, tmp_path):
+    # With the embedding following the decoder only the encoder is left on CPU. Text-only prompts never
+    # run it; a prompt with audio is refused before it writes its features into CUDA memory.
+    model_path = _copy_model(test_data_path, tmp_path)
+    _edit_json(model_path / "genai_config.json", lambda config: config["model"]["embedding"].pop("session_options"))
+    model = _model_on(model_path, "cuda")
+
+    prompt = "<|startoftext|>Transcribe. "
+    assert len(_run_prompt(model, prompt, None, 4)) > 0
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5, seed=72))
+    with pytest.raises(
+        RuntimeError,
+        match="speech.session_options run the speech model on CPU, but the audio features are passed in CUDA memory",
+    ):
+        _run_prompt(model, prompt + AUDIO_MARKER, og.Audios.open(clip), 4)
+
+
+@pytest.mark.skipif(not is_webgpu_ep_available(), reason="needs the WebGPU plug-in EP")
+def test_lfm2_audio_keeps_text_prompts_on_webgpu_with_cpu_sub_models(test_data_path, tmp_path):
+    # WebGPU without graph capture keeps the decoder's inputs on the host, so a CPU export's config still
+    # runs text-only prompts. Only a prompt with audio, whose features are in WebGPU memory, is refused.
+    model = _model_on(_copy_model(test_data_path, tmp_path), "webgpu")
+
+    prompt = "<|startoftext|>Transcribe. "
+    assert len(_run_prompt(model, prompt, None, 4)) > 0
+    clip = _write_wav(tmp_path / "clip.wav", _synthetic_signal(0.5, seed=73))
+    with pytest.raises(
+        RuntimeError,
+        match="speech.session_options run the speech model on CPU, but the audio features are passed in WebGPU memory",
+    ):
+        _run_prompt(model, prompt + AUDIO_MARKER, og.Audios.open(clip), 4)
+
+
 def _generate(model_path: str, prompt: str, audios, num_tokens: int, chunk_size: int | None = None):
     model = og.Model(model_path)
     processor = model.create_multimodal_processor()
@@ -1052,11 +1105,77 @@ def test_lfm2_audio_rejects_a_codebook_size_past_the_depthformer_logits(test_dat
     model_path = _speech_model(test_data_path, tmp_path, codebook_size=CODEBOOK_SIZE + 1)
     with pytest.raises(
         RuntimeError,
-        match=f"codebook_size is {CODEBOOK_SIZE + 1}, but the depthformer produces {CODEBOOK_SIZE} float logits",
+        match=f"codebook_size is {CODEBOOK_SIZE + 1}, but the depthformer produces {CODEBOOK_SIZE} logits",
     ):
         _generate_speech(model_path, "<|startoftext|>Answer aloud. ", None, 10, audio_interleaved=True, audio_top_k=1)
 
 
+def _replace_graph_output(onnx, path: Path, name: str, build) -> None:
+    """Has build(raw) -> (nodes, initializers, elem_type) produce the graph output `name` from what did."""
+    model = onnx.load(path)
+    raw = f"{name}_raw"
+    for node in model.graph.node:
+        node.output[:] = [raw if output == name else output for output in node.output]
+    nodes, initializers, elem_type = build(raw)
+    model.graph.node.extend(nodes)
+    model.graph.initializer.extend(initializers)
+    output = next(output for output in model.graph.output if output.name == name)
+    output.type.tensor_type.ClearField("shape")
+    output.type.tensor_type.elem_type = elem_type
+    onnx.save(model, path)
+
+
+def test_lfm2_audio_rejects_depthformer_logits_that_are_not_float(test_data_path, tmp_path):
+    onnx = pytest.importorskip("onnx")
+    model_path = _speech_model(test_data_path, tmp_path)
+    _replace_graph_output(
+        onnx,
+        model_path / "dummy_depthformer.onnx",
+        "logits",
+        lambda raw: (
+            [onnx.helper.make_node("Cast", [raw], ["logits"], to=onnx.TensorProto.FLOAT16)],
+            [],
+            onnx.TensorProto.FLOAT16,
+        ),
+    )
+    with pytest.raises(RuntimeError, match="depthformer's logits must be float, got float16"):
+        _generate_speech(model_path, "<|startoftext|>Answer aloud. ", None, 10, audio_interleaved=True, audio_top_k=1)
+
+
+def test_lfm2_audio_rejects_an_audio_embedding_short_of_a_codebook(test_data_path, tmp_path):
+    # A frame's embedding is the sum of one row per codebook; fewer rows than codes would be read past.
+    onnx = pytest.importorskip("onnx")
+    model_path = _speech_model(test_data_path, tmp_path)
+    ints = onnx.numpy_helper.from_array
+
+    def keep_half_the_codebooks(raw):
+        slice_bounds = [
+            ints(np.array([0], np.int64), "keep_starts"),
+            ints(np.array([NUM_CODEBOOKS // 2], np.int64), "keep_ends"),
+            ints(np.array([1], np.int64), "keep_axes"),
+        ]
+        node = onnx.helper.make_node("Slice", [raw, "keep_starts", "keep_ends", "keep_axes"], ["audio_embeds"])
+        return [node], slice_bounds, onnx.TensorProto.FLOAT
+
+    _replace_graph_output(onnx, model_path / "dummy_audio_embedding.onnx", "audio_embeds", keep_half_the_codebooks)
+    with pytest.raises(RuntimeError, match="audio embedding model must produce one float embedding per codebook"):
+        _generate_speech(model_path, "<|startoftext|>Answer aloud. ", None, 10, audio_interleaved=True, audio_top_k=1)
+
+
+def test_lfm2_audio_rejects_a_negative_codebook_count_the_graph_leaves_open(test_data_path, tmp_path):
+    # With the codebook dimension symbolic the graph reports it as -1, which a count of -1 would match.
+    onnx = pytest.importorskip("onnx")
+    model_path = _speech_model(test_data_path, tmp_path, num_codebooks=-1)
+    depthformer = onnx.load(model_path / "dummy_depthformer.onnx")
+    slices = next(value for value in depthformer.graph.input if value.name == "depth_slices_in")
+    slices.type.tensor_type.shape.dim[1].dim_param = "num_codebooks"
+    onnx.save(depthformer, model_path / "dummy_depthformer.onnx")
+
+    with pytest.raises(RuntimeError, match="expected inputs for -1 codebooks"):
+        model = og.Model(os.fspath(model_path))
+        og.Generator(model, og.GeneratorParams(model))
+
+
 def test_lfm2_audio_speech_output_needs_the_decoder_hidden_states(test_data_path, tmp_path):
     onnx = pytest.importorskip("onnx")
     model_path = _speech_model(test_data_path, tmp_path)