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fix(async_cosyvoice): 恢复原本文本令牌处理逻辑

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- 在 Frontend 中，恢复原本逐个生成文本令牌
- 在 Model 类中，移除了不必要的日志信息和断言，简化了文本令牌的处理流程
This commit is contained in:
qihua
2025-03-08 16:03:35 +08:00
parent ab5b8eb160
commit c0f6a474f3
2 changed files with 2 additions and 14 deletions
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5
cosyvoice/cli/frontend.py
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@@ -102,9 +102,8 @@ class CosyVoiceFrontEnd:
def _extract_text_token_generator(self, text_generator):
for text in text_generator:
text_token, _ = self._extract_text_token(text)
# for i in range(text_token.shape[1]):
# yield text_token[:, i: i + 1]
yield text_token
for i in range(text_token.shape[1]):
yield text_token[:, i: i + 1]
def _extract_speech_token(self, speech):
assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s'

11
cosyvoice/llm/llm_vllm.py
View File

@@ -149,8 +149,6 @@ class VllmQwen2LM(Qwen2LM):
need_add_tokens = output.token_ids[:-1]
else:
need_add_tokens = output.token_ids
# 单个token 循环处理比较耗时，建议是在model中进行批量（extend）处理，减少循环
# yield need_add_tokens
for token in need_add_tokens:
yield token
@@ -186,18 +184,14 @@ class VllmQwen2LM(Qwen2LM):
text_tokens_cache = text_tokens_cache[self.mix_ratio[0]:]
prompt_speech_token = prompt_speech_token[self.mix_ratio[1]:]
else:
logging.info('not enough text token to decode, wait for more')
break
if len(prompt_speech_token) == 0:
if (len(last_tokens) > 0 and last_tokens[-1] == 6563) or len(prompt_token_ids) == 1:
logging.info('get fill token, need to append more text token')
if len(text_tokens_cache) >= self.mix_ratio[0]:
text_tokens_temp = text_tokens_cache[:self.mix_ratio[0]]
prompt_token_ids += text_tokens_temp
logging.info('append {} text token'.format(len(text_tokens_temp)))
text_tokens_cache = text_tokens_cache[self.mix_ratio[0]:]
else:
logging.info('not enough text token to decode, wait for more')
continue
for output in self.llm_inference(prompt_token_ids, stop_token_ids=[6563]):
last_tokens = output.token_ids
@@ -205,19 +199,14 @@ class VllmQwen2LM(Qwen2LM):
need_add_tokens = last_tokens[:-1]
else:
need_add_tokens = last_tokens
# 单个token 循环处理比较耗时，建议是在model中进行批量（extend）处理，减少循环
# yield need_add_tokens
for token in need_add_tokens:
yield token
prompt_token_ids.extend(need_add_tokens)
prompt_token_ids += text_tokens_cache + [self.task_token_id]
logging.info('no more text token, decode until met eos')
for output in self.llm_inference(prompt_token_ids, stop_token_ids=[6561]):
if output.token_ids[-1] == 6561:
need_add_tokens = output.token_ids[:-1]
else:
need_add_tokens = output.token_ids
# 单个token 循环处理比较耗时，建议是在model中进行批量（extend）处理，减少循环
# yield need_add_tokens
for token in need_add_tokens:
yield token