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Merge branch 'FunAudioLLM:main' into main
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@liucr
liucr authored Sep 7, 2024
2 parents 16804f0 + c901a12 commit 9c72f90
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Showing 3 changed files with 45 additions and 51 deletions.
1 change: 1 addition & 0 deletions .github/workflows/lint.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@ name: Lint

on:
pull_request:
push:

jobs:
quick-checks:
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2 changes: 1 addition & 1 deletion cosyvoice/cli/cosyvoice.py
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Expand Up @@ -23,7 +23,7 @@

class CosyVoice:

def __init__(self, model_dir, load_jit=True, load_onnx=True):
def __init__(self, model_dir, load_jit=True, load_onnx=False):
instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir
if not os.path.exists(model_dir):
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93 changes: 43 additions & 50 deletions cosyvoice/cli/model.py
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Expand Up @@ -43,7 +43,6 @@ def __init__(self,
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_dict = {}
Expand Down Expand Up @@ -94,32 +93,31 @@ def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uui
self.llm_end_dict[uuid] = True

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.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.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=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device))
# 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)
tts_mel = self.flow.inference(token=token.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=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device))
# 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, flow_embedding, llm_embedding=torch.zeros(0, 192),
Expand All @@ -140,13 +138,12 @@ def inference(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
time.sleep(0.1)
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,
prompt_feat=prompt_speech_feat,
embedding=flow_embedding,
uuid=this_uuid,
finalize=False)
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:]
Expand All @@ -157,30 +154,26 @@ def inference(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
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_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,
prompt_feat=prompt_speech_feat,
embedding=flow_embedding,
uuid=this_uuid,
finalize=True)
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
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,
prompt_feat=prompt_speech_feat,
embedding=flow_embedding,
uuid=this_uuid,
finalize=True)
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()}
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)
if torch.cuda.is_available():
torch.cuda.synchronize()

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