deepgeek/CosyVoice
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clean code

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This commit is contained in:
Yuekai Zhang
2025-07-27 23:33:10 -07:00
parent 5427c274e3
commit 178da09993
6 changed files with 23 additions and 29 deletions
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14
runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py
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@@ -199,8 +199,6 @@ class TritonPythonModel:
Returns:
Generated waveform tensor
"""
print(prompt_speech_tokens.shape, prompt_speech_feat.shape, prompt_spk_embedding.shape, target_speech_tokens.shape)
# Convert tensors to Triton format
prompt_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_tokens", to_dlpack(prompt_speech_tokens))
prompt_speech_feat_tensor = pb_utils.Tensor.from_dlpack("prompt_speech_feat", to_dlpack(prompt_speech_feat))
prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack("prompt_spk_embedding", to_dlpack(prompt_spk_embedding))
@@ -228,9 +226,7 @@ class TritonPythonModel:
prompt = self.prompt_template.format(input_text=total_text)
input_ids = self.tokenizer.encode(prompt)
input_ids = torch.tensor([input_ids], dtype=torch.int32)
print(input_ids.shape, "before cat")
input_ids = torch.cat([input_ids, prompt_speech_tokens], dim=1)
print(input_ids.shape, "after cat", prompt_speech_tokens.shape)
return input_ids
def _extract_spk_embedding(self, speech):
@@ -271,23 +267,15 @@ class TritonPythonModel:
prompt_speech_tokens = self.forward_audio_tokenizer(wav, wav_len)
prompt_speech_tokens = prompt_speech_tokens.unsqueeze(0)
# TODO: FIX ME
wav_tensor = wav.as_numpy()
print(wav_tensor.shape, "wav_tensor")
wav_tensor = torch.from_numpy(wav_tensor)[:, :wav_len.as_numpy()[0][0]]
print(wav_tensor.shape, "wav_tensor after")
prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=24000)(wav_tensor)
speech_feat = self._extract_speech_feat(prompt_speech_resample)
print(speech_feat.shape, "speech_feat")
print(prompt_speech_tokens.shape, "prompt_speech_tokens here")
token_len = min(int(speech_feat.shape[1] / 2), prompt_speech_tokens.shape[-1])
prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half()
prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous()
print(prompt_speech_tokens.shape, "prompt_speech_tokens after")
print(speech_feat.shape, "speech_feat after")
print(token_len, "token_len")
# Extract text inputs
reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
reference_text = reference_text[0][0].decode('utf-8')