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Merge pull request #360 from FunAudioLLM/dev/lyuxiang.lx

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set onnx to false as last chunk rtf unstable
This commit is contained in:
Xiang Lyu
2024-09-06 17:12:23 +08:00
committed by GitHub
parent bcda6d807c 122df8c420
commit c901a12789
3 changed files with 45 additions and 51 deletions
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1
.github/workflows/lint.yml vendored
View File

@@ -2,6 +2,7 @@ name: Lint
on: on:
pull_request: pull_request:
push:
jobs: jobs:
quick-checks: quick-checks:

2
cosyvoice/cli/cosyvoice.py
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@@ -23,7 +23,7 @@ from cosyvoice.utils.file_utils import logging
class CosyVoice: 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 instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir self.model_dir = model_dir
if not os.path.exists(model_dir): if not os.path.exists(model_dir):

93
cosyvoice/cli/model.py
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@@ -43,7 +43,6 @@ class CosyVoiceModel:
self.stream_scale_factor = 1 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' 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.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() self.lock = threading.Lock()
# dict used to store session related variable # dict used to store session related variable
self.tts_speech_token_dict = {} self.tts_speech_token_dict = {}
@@ -93,32 +92,31 @@ class CosyVoiceModel:
self.llm_end_dict[uuid] = True self.llm_end_dict[uuid] = True
def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False): 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),
tts_mel = self.flow.inference(token=token.to(self.device), token_len=torch.tensor([token.shape[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=prompt_token.to(self.device), prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).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=prompt_feat.to(self.device), prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), embedding=embedding.to(self.device))
embedding=embedding.to(self.device)) # mel overlap fade in out
# mel overlap fade in out if self.mel_overlap_dict[uuid] is not None:
if self.mel_overlap_dict[uuid] is not None: tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window) # append hift cache
# append hift cache if self.hift_cache_dict[uuid] is not None:
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']
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)
tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) else:
else: hift_cache_source = torch.zeros(1, 1, 0)
hift_cache_source = torch.zeros(1, 1, 0) # keep overlap mel and hift cache
# keep overlap mel and hift cache if finalize is False:
if finalize is False: self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:] tts_mel = 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)
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:]}
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]
tts_speech = tts_speech[:, :-self.source_cache_len] else:
else: tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
return tts_speech return tts_speech
def inference(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192), def inference(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
@@ -139,13 +137,12 @@ class CosyVoiceModel:
time.sleep(0.1) time.sleep(0.1)
if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len: 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) 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,
this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token,
prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat,
prompt_feat=prompt_speech_feat, embedding=flow_embedding,
embedding=flow_embedding, uuid=this_uuid,
uuid=this_uuid, finalize=False)
finalize=False)
yield {'tts_speech': this_tts_speech.cpu()} yield {'tts_speech': this_tts_speech.cpu()}
with self.lock: with self.lock:
self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:] self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
@@ -156,30 +153,26 @@ class CosyVoiceModel:
p.join() p.join()
# deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None # 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) 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,
this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token,
prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat,
prompt_feat=prompt_speech_feat, embedding=flow_embedding,
embedding=flow_embedding, uuid=this_uuid,
uuid=this_uuid, finalize=True)
finalize=True)
yield {'tts_speech': this_tts_speech.cpu()} yield {'tts_speech': this_tts_speech.cpu()}
else: else:
# deal with all tokens # deal with all tokens
p.join() p.join()
this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1) 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,
this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token,
prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat,
prompt_feat=prompt_speech_feat, embedding=flow_embedding,
embedding=flow_embedding, uuid=this_uuid,
uuid=this_uuid, finalize=True)
finalize=True)
yield {'tts_speech': this_tts_speech.cpu()} yield {'tts_speech': this_tts_speech.cpu()}
with self.lock: with self.lock:
self.tts_speech_token_dict.pop(this_uuid) self.tts_speech_token_dict.pop(this_uuid)
self.llm_end_dict.pop(this_uuid) self.llm_end_dict.pop(this_uuid)
self.mel_overlap_dict.pop(this_uuid) self.mel_overlap_dict.pop(this_uuid)
self.hift_cache_dict.pop(this_uuid) self.hift_cache_dict.pop(this_uuid)
if torch.cuda.is_available():
torch.cuda.synchronize()