Update MagpieTTS model with latest changes

.github/workflows/cicd-main-speech.yml

@@ -189,6 +189,16 @@ jobs:
           - runner: self-hosted-azure
             script: SPEECHLM_HF_Training_SALM
             timeout: 20
+          - runner: self-hosted-azure
+            script: L2_TTS_Fast_dev_runs_Magpietts_DecoderContext
+          - runner: self-hosted-azure
+            script: L2_TTS_Fast_dev_runs_Magpietts_MultiEncoder
+          - runner: self-hosted-azure
+            script: L2_TTS_Fast_dev_runs_Magpietts_OnlinePO
+          - runner: self-hosted-azure
+            script: L2_TTS_InferEvaluate_Magpietts_ZeroShot
+          - runner: self-hosted-azure
+            script: L2_TTS_InferEvaluate_Magpietts_SeenSpeakers
     needs: [unit-tests]
     runs-on: ${{ matrix.runner }}
     name: ${{ matrix.is-optional && 'PLEASEFIXME_' || '' }}${{ matrix.script }}

examples/tts/README_frame_stacking.md

@@ -0,0 +1,26 @@
+# Overview
+This PR introduces frame-stacking implementation in Magpie-TTS. Frame-stacking is disabled by default. It can be enabled by setting a `frame_stacking_factor` > 1 in the YAML config.
+
+# Frame-stacking
+
+## Overview
+Frame-stacking is a technique that allows the Magpie-TTS **base decoder** (also known as the "main" for "first stage" decoder) to **process multiple consecutive audio frames in a single forward pass**, leaving the job of generating individual frames and codebooks to a second, smaller, "Local Transformer" ("LT") decoder. The goal is to accelerate inference by reducing the number of generation steps of the base decoder. In this two-stage approach:
+
+1. The base decoder processes multiple frames at once, producing a single latent representation for each group (stack) of frames
+2. The Local Transformer then generates the individual `frames * codebooks` tokens.
+
+The Local Transformer is much faster than the base decoder, making this two-stage approach significantly faster than generating each frame with the base decoder. The speed improvement comes from two factors:
+* **Fewer parameters**: The LT decoder is lightweight compared to the base decoder
+* **Shorter sequences**: The LT decoder only attends to the current frame stack and the latent, not the entire frame sequence
+
+The base decoder can also generate audio codes directly without a LT, but when frame-stacking is enabled using the LT decoder is typically necessary to achieve high-quality synthesis.
+
+## Design and Implementation
+* The `frame_stacking_factor` is the parameter that controls the number of frames to stack. The default is 1, which means no frame-stacking. We have tested values up to `4`.
+* For each codebooks, we keep a separate embedding table for at each frame within the stack. At the input to the decoder, the embeddings are averages across codebooks (as usual) and also frames within the stack. The embedding tables are shared between the base and LT decoders.
+
+## Limitations
+This is still WIP with more work to be done. Specifically, the following are not yet implemented / tested:
+* Online code extraction combined with frame-stacking.
+* Alignment encoder with frame-stacking.
+* CTC loss with frame-stacking.

examples/tts/README_magpietts_legacy_checkpoints.md

@@ -0,0 +1,95 @@
+# Background
+Magpie-TTS uses special tokens like AUDIO_BOS and AUDIO_EOS for its operation. The indices of these tokens are after the audio codec tokens, at the end of the embedding table.
+
+In April 2025 we changed the layout of the embedding table in a non-backwards compatible way:
+
+## Old Layout (until April 16)
+With the most common codec configuration (2016 codes), the layout used to look like this:
+```
+| Index   | Token Description    | Comments                                                                                                  |
+|---------|----------------------|-----------------------------------------------------------------------------------------------------------|
+| [0]     | Codec Token 0        |                                                                                                           |
+| [1]     | Codec Token 1        |                                                                                                           |
+| [2]     | Codec Token 2        |                                                                                                           |
+| ...     | ...                  |                                                                                                           |
+| [2015]  | Codec Token 2015     |                                                                                                           |
+| [2016]  | <Unused>             |                                                                                                           |
+| [2017]  | <Unused>             |                                                                                                           |
+| [2018]  | <Unused>             |                                                                                                           |
+| ...     |                      |                                                                                                           |
+| [2044]  | Context Audio BOS    | if model_type == `decoder_context_tts`                                                                    |
+| [2045]  | Context Audio EOS    | if model_type == `decoder_context_tts`                                                                    |
+| [2046]  | Audio BOS            | also used for Context Audio BOS if model_type == `multi_encoder_context_tts` or `single_encoder_sv_tts`   |
+| [2047]  | Audio EOS            | also used for Context Audio EOS if model_type == `multi_encoder_context_tts` or `single_encoder_sv_tts`   |
+```
+
+## New Layout```
+The new layout for the same codec configuration is:
+```
+| Index   | Token Description    | Comments  |
+---------------------------------------------|
+| [0]     | Codec Token 0        |           |
+| [1]     | Codec Token 1        |           |
+| [2]     | Codec Token 2        |           |
+| ...     | ...                  |           |
+| [2015]  | Codec Token 2015     |           |
+| [2016]  | Audio BOS            |           |
+| [2017]  | Audio EOS            |           |
+| [2018]  | Context Audio BOS    |           |
+| [2019]  | Context Audio EOS    |           |
+| [2020]  | MASK token (MaskGit) |           |
+| [2021]  | RESERVED_1           |           |
+| [2022]  | RESERVED_2           |           |
+| [2023]  | RESERVED_3           |           |
+```
+
+# How to Train and Load a New Checkpoint
+For new trainings and inference all configuration is automatic:
+* The number of codebooks, codec codebooks size, and codec downsampling rate are all read from the codec checkpoint rather than configured in Magpie.
+* The embedding table size is automatically set to codec_codebook_size + number_of_special_tokens (currently 2016+8=2024). There is no risk of accidentally stepping on codec tokens since the table sizes gets automatically sized with enough room for the special tokens.
+
+# How to Load Old Checkpoints
+For checkpoints created before the change you can force legacy codebook layout in one of these ways:
+
+## If using `infer_and_evaluate.py`
+Just set the `--legacy_codebooks` command line option. No need to update your YAML file – The script will automatically add the overrides.
+
+## If using a Hydra command line
+This scenario would happen when either finetuning with an old checkpoint or doing data generation with an old checkpoint.
+
+You have two options:
+### Add these to your command line
+```
+# decoder context model
++model.forced_num_all_tokens_per_codebook=2048 +model.forced_audio_eos_id=2047 +model.forced_audio_bos_id=2046  +model.forced_context_audio_eos_id=2045 +model.forced_context_audio_bos_id=2044
+
+# multi encoder context and any other model type
++model.forced_num_all_tokens_per_codebook=2048 +model.forced_audio_eos_id=2047 +model.forced_audio_bos_id=2046  +model.forced_context_audio_eos_id=2047 +model.forced_context_audio_bos_id=2046
+```
+# Or, add these overrides to your YAML file
+```
+forced_num_all_tokens_per_codebook: 2048
+forced_audio_eos_id: ${sum:${model.forced_num_all_tokens_per_codebook}, -1}           # 2047
+forced_audio_bos_id: ${sum:${model.forced_num_all_tokens_per_codebook}, -2}           # 2046
+
+# Depending on the old model type, the context_audio_bos_id and context_audio_eos_id will be different (choose one of the pairs below)
+
+# For `multi_encoder_context_tts`, `single_encoder_sv_tts`:
+#forced_context_audio_eos_id: ${sum:${model.forced_num_all_tokens_per_codebook}, -1}  # 2047
+#forced_context_audio_bos_id: ${sum:${model.forced_num_all_tokens_per_codebook}, -2}  # 2046
+
+# For `decoder_context_tts` models:
+#forced_context_audio_eos_id: ${sum:${model.forced_num_all_tokens_per_codebook}, -3}   # 2045
+#forced_context_audio_bos_id: ${sum:${model.forced_num_all_tokens_per_codebook}, -4}   # 2044
+```
+
+# Additional Details
+Over the last few weeks we have gone through a few embedding table layouts. When using an old checkpoint it's important to know which layout your checkpoint was trained with and configuring the system accordingly.
+
+* Layout 1: used until April 16 (described in the table above). Add `--legacy-codebooks` to the `infer_and_evaluate.py` command line to inference using this layout.
+
+* Layout 2: after the [config changes](https://github.com/blisc/NeMo/commit/7e2cdca74a866ecefdbe01c0076ad9b5d140ac61): 2018 tokens with special tokens at the end 2017, 2016, 2015, 2014 (the last two being overwrites of codec tokens). This is an invalid layout and these checkpoints should not be used.
+
+* Layout 3: after the [bugfix](https://github.com/blisc/NeMo/commit/23e299a0bd14b666543b4bbcc7783f783acb0bd3) but before the [refactoring](https://github.com/blisc/NeMo/commit/8ba55061a0ebb161abff4b329e402d5307f4af98): 2024 tokens with special tokens at the end (2023, 2022, 2021, 2020). There are no automatic options provided for using this layout but it can be manually configured by updating the `hparams.yaml` file with the `forced_*` options. Set `forced_num_all_tokens_per_codebook` to `2024` and set the rest of the overrides as defined under section `# Or, add these overrides to your YAML file` above.
+
+* Layout 4: The new layout, [from this commit onwards](https://github.com/blisc/NeMo/commit/8ba55061a0ebb161abff4b329e402d5307f4af98): 2024 tokens but with special tokens immediately after codec tokens (2016, 2017, 2018, 2019). Training and inference with the latest version of the code automatically use this layout.

examples/tts/conf/magpietts/magpietts.yaml

@@ -0,0 +1,199 @@
+name: Magpie-TTS
+
+max_epochs: ???
+# Adjust batch size based on GPU memory
+batch_size: 16
+# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch.
+# If null, then weighted sampling is disabled.
+weighted_sampling_steps_per_epoch: null
+
+# Dataset metadata for each manifest
+# See DatasetMeta in https://github.com/NVIDIA-NeMo/NeMo/blob/main/nemo/collections/tts/data/text_to_speech_dataset.py
+train_ds_meta: ???
+val_ds_meta: ???
+
+model:
+  model_type: "decoder_ce" # decoder_context_tts or decoder_ce
+  use_text_conditioning_encoder: true # Enable or disable text context. Audio context is always enabled
+  text_conditioning_tokenizer_name: text_ce_tokenizer # The tokenizer to be used for text contexts
+  context_duration_min: 5.0
+  context_duration_max: 5.0
+  load_cached_codes_if_available: true
+  prior_scaling_factor: 0.5
+  prior_end_step: 12000
+  prior_scaledown_start_step: 8000
+  indefinite_prior_prob: 0.0 # If > 0, then prior will be applied after prior_end_step with this probability.
+  alignment_loss_scale: 0.002
+  embedding_dim: 768
+  codecmodel_path: ???
+  max_epochs: ${max_epochs}
+  steps_per_epoch: ${weighted_sampling_steps_per_epoch}
+  cfg_unconditional_prob: 0.1
+
+  # Alignment encoder parameters, to binarize the prior
+  # This is used for attention-constrained training and inference
+  use_alignment_encoder: false
+  # Below args are only relevant if use_alignment_encoder is true
+  use_prior_for_aligner: true # Whether to use the beta-binomial prior to train the alignment encoder
+  alignment_encoder_loss_scale: 1.0
+  binarize_prior_after_step: 10000 # Switch from beta-binomial prior to binarized prior after this step.
+  binarize_attn_method: "nemo_binarize" # nemo_binarize or argmax.
+  prior_future_context: 2 # Future window of the binarized prior.
+  prior_past_context: 2 # Past window of the binarized prior.
+  prior_future_decay: 0.8 # Decay factor for future context
+  prior_past_decay: 0.5 # Decay factor for past context
+  binarize_repeat_audio_factor: 2 # Temporally increase audio timesteps, for nemo_binarize to work better. Increase this for low frame rate codecs
+  binarized_prior_epsilon: 0.0
+  aligner_encoder_train_steps: 50000
+
+  # Local transformer parameters for autoregressive codebook prediction within a frame
+  local_transformer_type: "autoregressive" # "none", "autoregressive", "maskgit"
+  # Below args are only relevant if use_local_transformer is true
+  local_transformer_loss_scale: 1.0
+  local_transformer_n_layers: 1
+  local_transformer_n_heads: 1
+  local_transformer_hidden_dim: 256
+
+  text_context_remapping_json: null # JSON file defining mapping of multiple text contexts to a single text context. Does not need to cover all text contexts.
+  text_context_remapping_prob: 0.0 # Probability of remapping the original text context to a remapped text context.
+
+  text_tokenizers:
+    english_phoneme:
+      _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer
+      punct: true
+      apostrophe: true
+      pad_with_space: false
+      g2p:
+        _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p
+        phoneme_dict: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt"
+        heteronyms: "scripts/tts_dataset_files/heteronyms-052722"
+        phoneme_probability: 0.8
+        ignore_ambiguous_words: false
+        use_chars: true
+        use_stresses: true
+    text_ce_tokenizer:  # Used for text context
+      _target_: AutoTokenizer
+      pretrained_model: "google/byt5-small"
+    ### For additional languages, consider adding a generic byt5 tokenizer like the one below
+    # french_chartokenizer:  # Used for text context
+    #     _target_: AutoTokenizer
+    #     pretrained_model: "google/byt5-small"
+
+  train_ds:
+    dataset:
+      _target_: nemo.collections.tts.data.text_to_speech_dataset.MagpieTTSDataset
+      dataset_meta: ${train_ds_meta}
+      weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch}
+      min_duration: 0.2
+      max_duration: 20.0
+
+    dataloader_params:
+      batch_size: ${batch_size}
+      num_workers: 4
+      drop_last: true
+      pin_memory: true
+
+  validation_ds:
+    dataset:
+      _target_: nemo.collections.tts.data.text_to_speech_dataset.MagpieTTSDataset
+      dataset_meta: ${val_ds_meta}
+      min_duration: 0.2
+      max_duration: 20.0
+
+    dataloader_params:
+      batch_size: ${batch_size}
+      num_workers: 4
+      pin_memory: true
+
+  encoder:
+    n_layers: 6
+    d_model: 768
+    d_ffn: 3072
+    sa_n_heads: 12
+    kernel_size: 3
+    p_dropout: 0.1
+    p_dropout_out: 0.0
+    has_xattn: false
+    is_causal: true
+    apply_norm_out: true
+    max_length_causal_mask: 2048
+    use_learnable_pos_emb: true
+
+  context_encoder: # Only used for decoder_ce (and multi_encoder_context_tts), ignored otherwise
+    n_layers: 1
+    d_model: 768
+    d_ffn: 3072
+    sa_n_heads: 12
+    kernel_size: 3
+    p_dropout: 0.1
+    p_dropout_out: 0.0
+    has_xattn: false
+    is_causal: false
+    apply_norm_out: true
+    max_length_causal_mask: 2048
+    use_learnable_pos_emb: true
+
+  decoder:
+    n_layers: 12
+    d_model: 768
+    d_ffn: 3072
+    sa_n_heads: 12
+    kernel_size: 1
+    p_dropout: 0.1
+    p_dropout_out: 0.0
+    has_xattn: true
+    xa_d_head: 128
+    xa_d_memory: 768
+    xa_n_heads: 1
+    is_causal: true
+    apply_norm_to_cond: true
+    apply_norm_out: true
+    max_length_causal_mask: 2048
+    use_learnable_pos_emb: true
+    make_prior_window_strict: true
+
+  optim:
+    _target_: torch.optim.AdamW
+    lr: 2e-4
+
+    sched:
+      name: ExponentialLR
+      gamma: 0.998
+
+trainer:
+  num_nodes: 1
+  devices: -1
+  accelerator: gpu
+  strategy: ddp_find_unused_parameters_true
+  precision: 32
+  max_epochs: ${max_epochs}
+  accumulate_grad_batches: 1
+  enable_checkpointing: False # Provided by exp_manager
+  logger: false # Provided by exp_manager
+  log_every_n_steps: 100
+  check_val_every_n_epoch: 1
+  num_sanity_val_steps: 0
+  benchmark: false
+  gradient_clip_val: 2.5
+
+exp_manager:
+  exp_dir: null
+  name: ${name}
+  create_tensorboard_logger: true
+  create_wandb_logger: false
+  wandb_logger_kwargs:
+    entity: null
+    project: null
+    group: null
+    name: ${name}
+    resume: true  # enable resume to ensure continuous training log metrics merged on the previous run id.
+  create_checkpoint_callback: true
+  checkpoint_callback_params:
+    monitor: val_loss
+    mode: min
+    save_top_k: 5
+    save_best_model: true
+    always_save_nemo: true
+    filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.4f}-{step}'
+  resume_if_exists: true
+  resume_ignore_no_checkpoint: true