deepgeek/CosyVoice
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fix lint

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This commit is contained in:
Yuekai Zhang
2025-09-03 03:45:17 -07:00
parent 633b991290
commit ad257b06e3
4 changed files with 14 additions and 20 deletions
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19
runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py
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@@ -41,11 +41,11 @@ from transformers import AutoTokenizer
import torchaudio
from matcha.utils.audio import mel_spectrogram
torch.set_num_threads(1)
class TritonPythonModel:
"""Triton Python model for Spark TTS.
@@ -65,7 +65,7 @@ class TritonPythonModel:
parameters = self.model_config['parameters']
model_params = {k: v["string_value"] for k, v in parameters.items()}
self.logger.log_info(f"model_params:{model_params}")
self.dynamic_chunk_strategy = model_params.get("dynamic_chunk_strategy", "exponential") # "exponential" or "time_based"
self.dynamic_chunk_strategy = model_params.get("dynamic_chunk_strategy", "exponential") # "exponential" or "time_based"
self.logger.log_info(f"Using dynamic chunk strategy: {self.dynamic_chunk_strategy}")
# Initialize tokenizer
@@ -193,7 +193,6 @@ class TritonPythonModel:
return prompt_speech_tokens
def forward_speaker_embedding(self, wav):
"""Forward pass through the speaker embedding component.
@@ -219,7 +218,6 @@ class TritonPythonModel:
return prompt_spk_embedding
def forward_token2wav(
self,
prompt_speech_tokens: torch.Tensor,
@@ -254,7 +252,6 @@ class TritonPythonModel:
inputs_tensor.append(token_offset_tensor)
inputs_tensor.append(finalize_tensor)
# Create and execute inference request
inference_request = pb_utils.InferenceRequest(
model_name='token2wav',
@@ -281,7 +278,6 @@ class TritonPythonModel:
input_ids = torch.cat([input_ids, prompt_speech_tokens], dim=1)
return input_ids
def _extract_speech_feat(self, speech):
speech_feat = mel_spectrogram(
speech,
@@ -299,7 +295,6 @@ class TritonPythonModel:
speech_feat = speech_feat.unsqueeze(dim=0)
return speech_feat
def _llm_gen_thread(self, generated_ids_iter, semantic_token_ids_arr, llm_is_done_flag):
for generated_ids in generated_ids_iter:
generated_ids = generated_ids.tolist()
@@ -338,9 +333,8 @@ class TritonPythonModel:
prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half()
prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous()
flow_prompt_speech_token_len = prompt_speech_tokens.shape[-1]
reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
reference_text = reference_text[0][0].decode('utf-8')
@@ -385,7 +379,9 @@ class TritonPythonModel:
this_tts_speech_token = semantic_token_ids_arr[:token_offset + this_token_hop_len + self.flow_pre_lookahead_len]
this_tts_speech_token = torch.tensor(this_tts_speech_token).unsqueeze(dim=0).to(torch.int32).to(self.device)
sub_tts_speech = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, this_tts_speech_token, request_id, token_offset, False)
sub_tts_speech = self.forward_token2wav(
prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding,
this_tts_speech_token, request_id, token_offset, False)
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
@@ -413,7 +409,6 @@ class TritonPythonModel:
else:
this_token_hop_len = self.token_hop_len
this_token_hop_len = max(self.token_hop_len, this_token_hop_len)
chunk_index += 1
else:
time.sleep(0.02)
@@ -423,7 +418,7 @@ class TritonPythonModel:
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
response_sender.send(inference_response)
llm_thread.join()
response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL)
self.logger.log_info("send tritonserver_response_complete_final to end")