mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
fix vocoder speech overlap
This commit is contained in:
lyuxiang.lx
@@ -31,18 +31,25 @@ class CosyVoiceModel:
|
||||
self.flow = flow
|
||||
self.hift = hift
|
||||
self.token_min_hop_len = 100
|
||||
self.token_max_hop_len = 400
|
||||
self.token_max_hop_len = 200
|
||||
self.token_overlap_len = 20
|
||||
self.speech_overlap_len = 34 * 256
|
||||
self.window = np.hamming(2 * self.speech_overlap_len)
|
||||
# mel fade in out
|
||||
self.mel_overlap_len = 34
|
||||
self.mel_window = np.hamming(2 * self.mel_overlap_len)
|
||||
# hift cache
|
||||
self.mel_cache_len = 20
|
||||
self.source_cache_len = int(self.mel_cache_len * 256)
|
||||
# rtf and decoding related
|
||||
self.stream_scale_factor = 1
|
||||
assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
|
||||
self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
|
||||
self.flow_hift_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
|
||||
self.lock = threading.Lock()
|
||||
# dict used to store session related variable
|
||||
self.tts_speech_token = {}
|
||||
self.llm_end = {}
|
||||
self.tts_speech_token_dict = {}
|
||||
self.llm_end_dict = {}
|
||||
self.mel_overlap_dict = {}
|
||||
self.hift_cache_dict = {}
|
||||
|
||||
def load(self, llm_model, flow_model, hift_model):
|
||||
self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
|
||||
@@ -64,102 +71,108 @@ class CosyVoiceModel:
|
||||
self.flow.decoder.estimator = xxx
|
||||
self.flow.decoder.session = xxx
|
||||
|
||||
def llm_job(self, text, text_len, prompt_text, prompt_text_len, llm_prompt_speech_token, llm_prompt_speech_token_len, llm_embedding, this_uuid):
|
||||
def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
|
||||
with self.llm_context:
|
||||
for i in self.llm.inference(text=text.to(self.device),
|
||||
text_len=text_len.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_len=prompt_text_len.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=llm_prompt_speech_token_len.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).half(),
|
||||
sampling=25,
|
||||
max_token_text_ratio=30,
|
||||
min_token_text_ratio=3):
|
||||
self.tts_speech_token[this_uuid].append(i)
|
||||
self.llm_end[this_uuid] = True
|
||||
self.tts_speech_token_dict[uuid].append(i)
|
||||
self.llm_end_dict[uuid] = True
|
||||
|
||||
def token2wav(self, token, prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, embedding):
|
||||
def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False):
|
||||
with self.flow_hift_context:
|
||||
tts_mel = self.flow.inference(token=token.to(self.device),
|
||||
token_len=torch.tensor([token.size(1)], dtype=torch.int32).to(self.device),
|
||||
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
|
||||
prompt_token=prompt_token.to(self.device),
|
||||
prompt_token_len=prompt_token_len.to(self.device),
|
||||
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
|
||||
prompt_feat=prompt_feat.to(self.device),
|
||||
prompt_feat_len=prompt_feat_len.to(self.device),
|
||||
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
|
||||
embedding=embedding.to(self.device))
|
||||
tts_speech = self.hift.inference(mel=tts_mel).cpu()
|
||||
# mel overlap fade in out
|
||||
if self.mel_overlap_dict[uuid] is not None:
|
||||
tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
|
||||
# append hift cache
|
||||
if self.hift_cache_dict[uuid] is not None:
|
||||
hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
|
||||
tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
|
||||
else:
|
||||
hift_cache_source = torch.zeros(1, 1, 0)
|
||||
# keep overlap mel and hift cache
|
||||
if finalize is False:
|
||||
self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
|
||||
tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
|
||||
tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
|
||||
self.hift_cache_dict[uuid] = {'source': tts_source[:, :, -self.source_cache_len:], 'mel': tts_mel[:, :, -self.mel_cache_len:]}
|
||||
tts_speech = tts_speech[:, :-self.source_cache_len]
|
||||
else:
|
||||
tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
|
||||
return tts_speech
|
||||
|
||||
def inference(self, text, text_len, flow_embedding, llm_embedding=torch.zeros(0, 192),
|
||||
prompt_text=torch.zeros(1, 0, dtype=torch.int32), prompt_text_len=torch.zeros(1, dtype=torch.int32),
|
||||
llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
|
||||
flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
|
||||
prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32), stream=False):
|
||||
def inference(self, text, flow_embedding, 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, **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[this_uuid], self.llm_end[this_uuid] = [], False
|
||||
p = threading.Thread(target=self.llm_job, args=(text.to(self.device), text_len.to(self.device), prompt_text.to(self.device), prompt_text_len.to(self.device),
|
||||
llm_prompt_speech_token.to(self.device), llm_prompt_speech_token_len.to(self.device), llm_embedding.to(self.device), this_uuid))
|
||||
self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid], self.mel_overlap_dict[this_uuid], self.hift_cache_dict[this_uuid] = [], False, None, None
|
||||
p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
|
||||
p.start()
|
||||
p.join()
|
||||
if stream is True:
|
||||
cache_speech, cache_token, token_hop_len = None, None, self.token_min_hop_len
|
||||
token_hop_len = self.token_min_hop_len
|
||||
while True:
|
||||
time.sleep(0.1)
|
||||
if len(self.tts_speech_token[this_uuid]) >= token_hop_len + self.token_overlap_len:
|
||||
this_tts_speech_token = torch.concat(self.tts_speech_token[this_uuid][:token_hop_len + self.token_overlap_len], dim=1)
|
||||
if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
|
||||
this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len], dim=1)
|
||||
with self.flow_hift_context:
|
||||
this_tts_speech = self.token2wav(token=this_tts_speech_token,
|
||||
prompt_token=flow_prompt_speech_token.to(self.device),
|
||||
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
|
||||
prompt_feat=prompt_speech_feat.to(self.device),
|
||||
prompt_feat_len=prompt_speech_feat_len.to(self.device),
|
||||
embedding=flow_embedding.to(self.device))
|
||||
# fade in/out if necessary
|
||||
if cache_speech is not None:
|
||||
this_tts_speech = fade_in_out(this_tts_speech, cache_speech, self.window)
|
||||
yield {'tts_speech': this_tts_speech[:, :-self.speech_overlap_len]}
|
||||
cache_speech = this_tts_speech[:, -self.speech_overlap_len:]
|
||||
cache_token = self.tts_speech_token[this_uuid][:token_hop_len]
|
||||
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[this_uuid] = self.tts_speech_token[this_uuid][token_hop_len:]
|
||||
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[this_uuid] is True and len(self.tts_speech_token[this_uuid]) < token_hop_len + self.token_overlap_len:
|
||||
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
|
||||
p.join()
|
||||
# p.join()
|
||||
# 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.concat(self.tts_speech_token[this_uuid], dim=1)
|
||||
if this_tts_speech_token.shape[1] < self.token_min_hop_len + self.token_overlap_len and cache_token is not None:
|
||||
cache_token_len = self.token_min_hop_len + self.token_overlap_len - this_tts_speech_token.shape[1]
|
||||
this_tts_speech_token = torch.concat([torch.concat(cache_token[-cache_token_len:], dim=1), this_tts_speech_token], dim=1)
|
||||
else:
|
||||
cache_token_len = 0
|
||||
this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
|
||||
with self.flow_hift_context:
|
||||
this_tts_speech = self.token2wav(token=this_tts_speech_token,
|
||||
prompt_token=flow_prompt_speech_token.to(self.device),
|
||||
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
|
||||
prompt_feat=prompt_speech_feat.to(self.device),
|
||||
prompt_feat_len=prompt_speech_feat_len.to(self.device),
|
||||
embedding=flow_embedding.to(self.device))
|
||||
this_tts_speech = this_tts_speech[:, int(cache_token_len / this_tts_speech_token.shape[1] * this_tts_speech.shape[1]):]
|
||||
if cache_speech is not None:
|
||||
this_tts_speech = fade_in_out(this_tts_speech, cache_speech, self.window)
|
||||
yield {'tts_speech': this_tts_speech}
|
||||
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
|
||||
p.join()
|
||||
this_tts_speech_token = torch.concat(self.tts_speech_token[this_uuid], dim=1)
|
||||
# p.join()
|
||||
this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
|
||||
with self.flow_hift_context:
|
||||
this_tts_speech = self.token2wav(token=this_tts_speech_token,
|
||||
prompt_token=flow_prompt_speech_token.to(self.device),
|
||||
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
|
||||
prompt_feat=prompt_speech_feat.to(self.device),
|
||||
prompt_feat_len=prompt_speech_feat_len.to(self.device),
|
||||
embedding=flow_embedding.to(self.device))
|
||||
yield {'tts_speech': this_tts_speech}
|
||||
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()}
|
||||
with self.lock:
|
||||
self.tts_speech_token.pop(this_uuid)
|
||||
self.llm_end.pop(this_uuid)
|
||||
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)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
Reference in New Issue
Block a user