deepgeek/CosyVoice
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optimize vc code

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lyuxiang.lx
2025-04-15 16:15:20 +08:00
parent c07cd3d730
commit 37e48dd318
2 changed files with 17 additions and 62 deletions
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2
cosyvoice/cli/cosyvoice.py
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@@ -128,7 +128,7 @@ class CosyVoice:
def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
start_time = time.time()
for model_output in self.model.vc(**model_input, stream=stream, speed=speed):
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output

77
cosyvoice/cli/model.py
View File

@@ -122,6 +122,10 @@ class CosyVoiceModel:
self.tts_speech_token_dict[uuid].append(i)
self.llm_end_dict[uuid] = True
def vc_job(self, source_speech_token, uuid):
self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist()
self.llm_end_dict[uuid] = True
def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
with torch.cuda.amp.autocast(self.fp16):
tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device),
@@ -162,11 +166,11 @@ class CosyVoiceModel:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
return tts_speech
def tts(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
prompt_text=torch.zeros(1, 0, dtype=torch.int32),
llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs):
prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
# this_uuid is used to track variables related to this inference thread
this_uuid = str(uuid.uuid1())
with self.lock:
@@ -174,7 +178,10 @@ class CosyVoiceModel:
self.hift_cache_dict[this_uuid] = None
self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
if source_speech_token.shape[1] == 0:
p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
else:
p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
p.start()
if stream is True:
token_hop_len = self.token_min_hop_len
@@ -226,61 +233,6 @@ class CosyVoiceModel:
self.flow_cache_dict.pop(this_uuid)
torch.cuda.empty_cache()
def vc(self, source_speech_token, flow_prompt_speech_token, prompt_speech_feat, flow_embedding, stream=False, speed=1.0, **kwargs):
# this_uuid is used to track variables related to this inference thread
this_uuid = str(uuid.uuid1())
with self.lock:
self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = source_speech_token.flatten().tolist(), True
self.hift_cache_dict[this_uuid] = None
self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
if stream is True:
token_hop_len = self.token_min_hop_len
while True:
if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \
.unsqueeze(dim=0)
this_tts_speech = self.token2wav(token=this_tts_speech_token,
prompt_token=flow_prompt_speech_token,
prompt_feat=prompt_speech_feat,
embedding=flow_embedding,
uuid=this_uuid,
finalize=False)
yield {'tts_speech': this_tts_speech.cpu()}
with self.lock:
self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
# increase token_hop_len for better speech quality
token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len:
break
# deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
this_tts_speech = self.token2wav(token=this_tts_speech_token,
prompt_token=flow_prompt_speech_token,
prompt_feat=prompt_speech_feat,
embedding=flow_embedding,
uuid=this_uuid,
finalize=True)
yield {'tts_speech': this_tts_speech.cpu()}
else:
# deal with all tokens
this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
this_tts_speech = self.token2wav(token=this_tts_speech_token,
prompt_token=flow_prompt_speech_token,
prompt_feat=prompt_speech_feat,
embedding=flow_embedding,
uuid=this_uuid,
finalize=True,
speed=speed)
yield {'tts_speech': this_tts_speech.cpu()}
with self.lock:
self.tts_speech_token_dict.pop(this_uuid)
self.llm_end_dict.pop(this_uuid)
self.mel_overlap_dict.pop(this_uuid)
self.hift_cache_dict.pop(this_uuid)
self.flow_cache_dict.pop(this_uuid)
torch.cuda.empty_cache()
class CosyVoice2Model(CosyVoiceModel):
@@ -386,18 +338,21 @@ class CosyVoice2Model(CosyVoiceModel):
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
return tts_speech
def tts(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
prompt_text=torch.zeros(1, 0, dtype=torch.int32),
llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs):
prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
# this_uuid is used to track variables related to this inference thread
this_uuid = str(uuid.uuid1())
with self.lock:
self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
self.hift_cache_dict[this_uuid] = None
self.flow_cache_dict[this_uuid] = self.init_flow_cache()
p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
if source_speech_token.shape[1] == 0:
p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
else:
p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
p.start()
if stream is True:
assert self.use_flow_cache is True, "set use_flow_cache=True if you want to use stream inference to avoid OOM"