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sleep(0.1) can reduce efficiency in stream way, replace with semaphore #1252

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sleep(0.1) can reduce efficiency in stream way, replace with semaphore #1252

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14 changes: 12 additions & 2 deletions cosyvoice/cli/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,7 @@ def __init__(self,
self.mel_overlap_dict = {}
self.flow_cache_dict = {}
self.hift_cache_dict = {}
self.tts_speech_semaphore_dict = {}

def load(self, llm_model, flow_model, hift_model):
self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True)
Expand Down Expand Up @@ -111,6 +112,7 @@ def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uui
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device)):
self.tts_speech_token_dict[uuid].append(i)
self.tts_speech_semaphore_dict[uuid].release()
else:
for i in self.llm.inference(text=text.to(self.device),
text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
Expand All @@ -120,11 +122,14 @@ def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uui
prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
embedding=llm_embedding.to(self.device)):
self.tts_speech_token_dict[uuid].append(i)
self.tts_speech_semaphore_dict[uuid].release()
self.llm_end_dict[uuid] = True
self.tts_speech_semaphore_dict[uuid].release()

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
self.tts_speech_semaphore_dict[uuid].release()

def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
with torch.cuda.amp.autocast(self.fp16):
Expand Down Expand Up @@ -178,6 +183,7 @@ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.ze
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)
self.tts_speech_semaphore_dict[this_uuid]=threading.Semaphore(0)
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:
Expand All @@ -186,7 +192,7 @@ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.ze
if stream is True:
token_hop_len = self.token_min_hop_len
while True:
time.sleep(0.1)
self.tts_speech_semaphore_dict[this_uuid].acquire()
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)
Expand Down Expand Up @@ -231,6 +237,7 @@ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.ze
self.mel_overlap_dict.pop(this_uuid)
self.hift_cache_dict.pop(this_uuid)
self.flow_cache_dict.pop(this_uuid)
self.tts_speech_semaphore_dict.pop(this_uuid)
torch.cuda.empty_cache()


Expand Down Expand Up @@ -267,6 +274,7 @@ def __init__(self,
self.llm_end_dict = {}
self.flow_cache_dict = {}
self.hift_cache_dict = {}
self.tts_speech_semaphore_dict = {}

def init_flow_cache(self):
encoder_cache = {'offset': 0,
Expand Down Expand Up @@ -349,6 +357,7 @@ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.ze
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()
self.tts_speech_semaphore_dict[this_uuid]=threading.Semaphore(0)
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:
Expand All @@ -360,7 +369,7 @@ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.ze
flow_prompt_speech_token = flow_prompt_speech_token[:, -int(self.flow_decoder_required_cache_size / self.flow.token_mel_ratio):]
prompt_speech_feat = prompt_speech_feat[:, -self.flow_decoder_required_cache_size:]
while True:
time.sleep(0.1)
self.tts_speech_semaphore_dict[this_uuid].acquire()
if len(self.tts_speech_token_dict[this_uuid]) >= self.token_hop_len + self.flow.pre_lookahead_len:
this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:self.token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0)
this_tts_speech = self.token2wav(token=this_tts_speech_token,
Expand Down Expand Up @@ -405,4 +414,5 @@ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.ze
self.llm_end_dict.pop(this_uuid)
self.hift_cache_dict.pop(this_uuid)
self.flow_cache_dict.pop(this_uuid)
self.tts_speech_semaphore_dict.pop(this_uuid)
torch.cuda.empty_cache()