[Fix] [Qwen3-TTS] Qwen3-TTS streaming chunk-boundary artifacts

tests/dfx/perf/stage_configs/qwen3_tts.yaml

@@ -88,7 +88,7 @@ runtime:
         connector_get_max_wait_first_chunk: 3000
         connector_get_max_wait: 300
         codec_chunk_frames: 25
-        codec_left_context_frames: 25
+        codec_left_context_frames: 72
 
   edges:
     - from: 0

tests/model_executor/models/qwen3_tts/test_qwen3_tts_code2wav.py

@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+import torch.nn as nn
+
+from vllm_omni.model_executor.models.qwen3_tts.qwen3_tts_code2wav import Qwen3TTSCode2Wav
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class _FakeDecoder(nn.Module):
+    def __init__(self, total_upsample: int = 4):
+        super().__init__()
+        self.total_upsample = total_upsample
+
+    def chunked_decode(self, codes: torch.Tensor) -> torch.Tensor:
+        frames = codes.shape[-1]
+        wav_len = frames * self.total_upsample + 6
+        wav = torch.arange(wav_len, dtype=torch.float32)
+        return wav.view(1, 1, -1)
+
+
+def _make_model() -> Qwen3TTSCode2Wav:
+    model = Qwen3TTSCode2Wav(
+        vllm_config=SimpleNamespace(
+            model_config=SimpleNamespace(model="unused"),
+            device_config=SimpleNamespace(device=torch.device("cpu")),
+        )
+    )
+    model._decoder = _FakeDecoder()
+    model._num_quantizers = 2
+    model._output_sample_rate = 24000
+    model._total_upsample = 4
+    model._ensure_speech_tokenizer_loaded = lambda: None
+    return model
+
+
+def test_forward_trims_context_on_exact_frame_boundaries():
+    model = _make_model()
+
+    out = model.forward(
+        input_ids=torch.arange(12, dtype=torch.long),
+        runtime_additional_information=[{"left_context_size": 2}],
+    )
+
+    audio = out.multimodal_outputs["model_outputs"][0]
+    expected = torch.arange(8, 24, dtype=torch.float32)
+    torch.testing.assert_close(audio, expected)
+
+
+def test_forward_trims_trailing_padding_without_context():
+    model = _make_model()
+
+    out = model.forward(
+        input_ids=torch.arange(12, dtype=torch.long),
+        runtime_additional_information=[{"left_context_size": 0}],
+    )
+
+    audio = out.multimodal_outputs["model_outputs"][0]
+    expected = torch.arange(24, dtype=torch.float32)
+    torch.testing.assert_close(audio, expected)

vllm_omni/model_executor/models/qwen3_tts/pipeline.yaml

@@ -84,7 +84,8 @@ connectors:
       connector_get_max_wait_first_chunk: 3000
       connector_get_max_wait: 300
       codec_chunk_frames: 25
-      codec_left_context_frames: 25
+      # Match the decoder sliding attention window to avoid chunk-boundary noise.
+      codec_left_context_frames: 72
 
 edges:
   - from: 0

vllm_omni/model_executor/models/qwen3_tts/qwen3_tts_code2wav.py

@@ -41,6 +41,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self._num_quantizers: int | None = None
         self._output_sample_rate: int | None = None
         self._total_upsample: int | None = None
+        self._decoder_sliding_window: int | None = None
         self._logged_codec_stats = False
 
     @staticmethod
@@ -106,6 +107,7 @@ def _ensure_speech_tokenizer_loaded(self) -> None:
         self._num_quantizers = num_q
         self._output_sample_rate = out_sr
         self._total_upsample = int(decoder.total_upsample)
+        self._decoder_sliding_window = int(getattr(dec_cfg, "sliding_window", 0) or 0)
 
         # Precompute SnakeBeta exp caches (benefits both Triton and eager paths)
         if hasattr(decoder, "precompute_snake_caches"):
@@ -128,6 +130,20 @@ def _ensure_speech_tokenizer_loaded(self) -> None:
                     if isinstance(extra_cfg, dict):
                         chunk_frames = int(extra_cfg.get("codec_chunk_frames") or 0)
                         left_frames = int(extra_cfg.get("codec_left_context_frames") or 0)
+                        if (
+                            chunk_frames > 0
+                            and left_frames > 0
+                            and self._decoder_sliding_window
+                            and left_frames < self._decoder_sliding_window
+                        ):
+                            logger.warning(
+                                "Qwen3-TTS streaming codec_left_context_frames=%d is smaller than "
+                                "decoder sliding_window=%d; chunk-boundary distortion may occur. "
+                                "Increase codec_left_context_frames to at least %d for streaming.",
+                                left_frames,
+                                self._decoder_sliding_window,
+                                self._decoder_sliding_window,
+                            )
 
                     decoder.enable_cudagraph(
                         device=device,
@@ -289,21 +305,17 @@ def forward(
         for j, idx in enumerate(valid_indices):
             ctx_frames, actual_frames = parsed[idx]
             wav = wav_tensors[j]
-            # Drop the ref_code prefix from the decoded waveform, keeping only newly generated audio.
-            if ctx_frames <= 0:
-                expected_len = actual_frames * upsample
-                if wav.shape[0] > expected_len:
-                    wav = wav[:expected_len]
-            else:
-                cut = int(ctx_frames / max(actual_frames, 1) * wav.shape[0])
-                if cut >= wav.shape[0]:
-                    logger.warning(
-                        "Context trim %d >= decoded length %d; returning empty audio.",
-                        cut,
-                        wav.shape[0],
-                    )
-                    continue
-                wav = wav[cut:]
+            # Slice on exact codec-frame boundaries instead of proportionally.
+            start = max(0, ctx_frames * upsample)
+            end = max(start, actual_frames * upsample)
+            if start >= wav.shape[0]:
+                logger.warning(
+                    "Context trim start %d >= decoded length %d; returning empty audio.",
+                    start,
+                    wav.shape[0],
+                )
+                continue
+            wav = wav[start : min(end, wav.shape[0])]
             if wav.shape[0] > 0:
                 audios[idx] = wav.to(dtype=torch.float32).reshape(-1)
 

vllm_omni/model_executor/stage_configs/qwen3_tts.yaml

@@ -89,9 +89,9 @@ runtime:
         connector_get_sleep_s: 0.01
         connector_get_max_wait_first_chunk: 3000
         connector_get_max_wait: 300
-        # Align with Omni: small chunks with sufficient context overlap.
+        # Match the decoder sliding attention window to avoid chunk-boundary noise.
         codec_chunk_frames: 25
-        codec_left_context_frames: 25
+        codec_left_context_frames: 72
 
   edges:
     - from: 0

vllm_omni/model_executor/stage_configs/qwen3_tts_batch.yaml

@@ -90,9 +90,9 @@ runtime:
         connector_get_sleep_s: 0.01
         connector_get_max_wait_first_chunk: 3000
         connector_get_max_wait: 300
-        # Align with Omni: small chunks with sufficient context overlap.
+        # Match the decoder sliding attention window to avoid chunk-boundary noise.
         codec_chunk_frames: 25
-        codec_left_context_frames: 25
+        codec_left_context_frames: 72
 
   edges:
     - from: 0

vllm_omni/platforms/npu/stage_configs/qwen3_tts.yaml

@@ -88,7 +88,7 @@ runtime:
         connector_get_max_wait: 300
         # Align with Omni: small chunks with sufficient context overlap.
         codec_chunk_frames: 25
-        codec_left_context_frames: 25
+        codec_left_context_frames: 72
 
   edges:
     - from: 0