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add text_frontend arg

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lyuxiang.lx
2024-12-17 14:03:35 +08:00
parent 60b0416229
commit 26c774098d
3 changed files with 16 additions and 17 deletions
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24
cosyvoice/cli/cosyvoice.py
View File

@@ -59,8 +59,8 @@ class CosyVoice:
spks = list(self.frontend.spk2info.keys())
return spks
def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_sft(i, spk_id)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
@@ -70,9 +70,9 @@ class CosyVoice:
yield model_output
start_time = time.time()
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0):
prompt_text = self.frontend.text_normalize(prompt_text, split=False)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
if len(i) < 0.5 * len(prompt_text):
logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate)
@@ -84,10 +84,10 @@ class CosyVoice:
yield model_output
start_time = time.time()
def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0):
def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
if self.frontend.instruct is True and isinstance(self.model, CosyVoiceModel):
raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir))
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
@@ -97,12 +97,12 @@ class CosyVoice:
yield model_output
start_time = time.time()
def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0):
def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
assert isinstance(self.model, CosyVoiceModel)
if self.frontend.instruct is False:
raise ValueError('{} do not support instruct inference'.format(self.model_dir))
instruct_text = self.frontend.text_normalize(instruct_text, split=False)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
@@ -112,9 +112,9 @@ class CosyVoice:
yield model_output
start_time = time.time()
def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0):
def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
assert isinstance(self.model, CosyVoice2Model)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_speech_16k, self.sample_rate)
start_time = time.time()
logging.info('synthesis text {}'.format(i))

8
cosyvoice/cli/frontend.py
View File

@@ -107,12 +107,10 @@ class CosyVoiceFrontEnd:
speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device)
return speech_feat, speech_feat_len
def text_normalize(self, text, split=True):
def text_normalize(self, text, split=True, text_frontend=True):
if text_frontend is False:
return [text] if split is True else text
text = text.strip()
# NOTE(lyuxiang.lx) move this judgement into ttsfrd in the future
for token in self.tokenizer.special_tokens['additional_special_tokens']:
if token in text:
return text if split is False else [text]
if contains_chinese(text):
if self.use_ttsfrd:
texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]]