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add_zero_shot_spk

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lyuxiang.lx
2025-04-15 11:45:00 +08:00
parent 9dc559fc2a
commit 9fea0f0836
3 changed files with 38 additions and 23 deletions
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12
cosyvoice/cli/cosyvoice.py
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@@ -66,6 +66,14 @@ class CosyVoice:
spks = list(self.frontend.spk2info.keys())
return spks
def add_zero_shot_spk(self, prompt_text, prompt_speech_16k, zero_shot_spk_id):
assert zero_shot_spk_id != '', 'do not use empty zero_shot_spk_id'
model_input = self.frontend.frontend_zero_shot('', prompt_text, prompt_speech_16k, self.sample_rate, '')
del model_input['text']
del model_input['text_len']
self.frontend.spk2info[zero_shot_spk_id] = model_input
return 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)
@@ -77,12 +85,12 @@ 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, text_frontend=True):
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, zero_shot_spk_id='', 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 (not isinstance(i, Generator)) and 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)
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate, zero_shot_spk_id)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):

40
cosyvoice/cli/frontend.py
View File

@@ -122,7 +122,7 @@ class CosyVoiceFrontEnd:
if isinstance(text, Generator):
logging.info('get tts_text generator, will skip text_normalize!')
return [text]
if text_frontend is False:
if text_frontend is False or text == '':
return [text] if split is True else text
text = text.strip()
if self.use_ttsfrd:
@@ -154,24 +154,28 @@ class CosyVoiceFrontEnd:
model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding}
return model_input
def frontend_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, resample_rate):
def frontend_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, resample_rate, zero_shot_spk_id):
tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text)
prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k)
speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_resample)
speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k)
if resample_rate == 24000:
# cosyvoice2, force speech_feat % speech_token = 2
token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1])
speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2 * token_len
speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len
embedding = self._extract_spk_embedding(prompt_speech_16k)
model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len,
'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len,
'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len,
'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len,
'llm_embedding': embedding, 'flow_embedding': embedding}
if zero_shot_spk_id == '':
prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text)
prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k)
speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_resample)
speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k)
if resample_rate == 24000:
# cosyvoice2, force speech_feat % speech_token = 2
token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1])
speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2 * token_len
speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len
embedding = self._extract_spk_embedding(prompt_speech_16k)
model_input = {'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len,
'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len,
'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len,
'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len,
'llm_embedding': embedding, 'flow_embedding': embedding}
else:
model_input = self.spk2info[zero_shot_spk_id]
model_input['text'] = tts_text_token
model_input['text_len'] = tts_text_token_len
return model_input
def frontend_cross_lingual(self, tts_text, prompt_speech_16k, resample_rate):