deepgeek/CosyVoice
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refine code

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lyuxiang.lx
2025-12-30 09:24:52 +00:00
parent cfa1c115b2
commit e15222b17c
1 changed files with 19 additions and 25 deletions
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44
cosyvoice/cli/model.py
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@@ -103,35 +103,29 @@ class CosyVoiceModel:
with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False): with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False):
if isinstance(text, Generator): if isinstance(text, Generator):
assert (self.__class__.__name__ != 'CosyVoiceModel') and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2/3 and do not support vllm!' assert (self.__class__.__name__ != 'CosyVoiceModel') and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2/3 and do not support vllm!'
for i in self.llm.inference_bistream(text=text, token_generator = self.llm.inference_bistream(text=text,
prompt_text=prompt_text.to(self.device),
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device))
else:
token_generator = self.llm.inference(text=text.to(self.device),
text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
prompt_text=prompt_text.to(self.device), prompt_text=prompt_text.to(self.device),
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device), prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device)): embedding=llm_embedding.to(self.device),
if i in self.silent_tokens: uuid=uuid)
cur_silent_token_num += 1 for i in token_generator:
if cur_silent_token_num > max_silent_token_num: if i in self.silent_tokens:
continue cur_silent_token_num += 1
else: if cur_silent_token_num > max_silent_token_num:
cur_silent_token_num = 0 continue
self.tts_speech_token_dict[uuid].append(i) else:
else: cur_silent_token_num = 0
for i in self.llm.inference(text=text.to(self.device), self.tts_speech_token_dict[uuid].append(i)
text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
prompt_text=prompt_text.to(self.device),
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device),
uuid=uuid):
if i in self.silent_tokens:
cur_silent_token_num += 1
if cur_silent_token_num > max_silent_token_num:
continue
else:
cur_silent_token_num = 0
self.tts_speech_token_dict[uuid].append(i)
self.llm_end_dict[uuid] = True self.llm_end_dict[uuid] = True
def vc_job(self, source_speech_token, uuid): def vc_job(self, source_speech_token, uuid):