deepgeek/CosyVoice
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[feature] fix badcase, add fade on speech output

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liubaiji
2024-09-11 10:41:50 +08:00
parent df653f1e98
commit 9e0b99e48e
1 changed files with 9 additions and 1 deletions
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10
cosyvoice/cli/model.py
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@@ -49,6 +49,7 @@ class CosyVoiceModel:
self.llm_end_dict = {} self.llm_end_dict = {}
self.mel_overlap_dict = {} self.mel_overlap_dict = {}
self.hift_cache_dict = {} self.hift_cache_dict = {}
self.speech_window = np.hamming(2 * self.source_cache_len)
def load(self, llm_model, flow_model, hift_model): def load(self, llm_model, flow_model, hift_model):
self.llm.load_state_dict(torch.load(llm_model, map_location=self.device)) self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
@@ -113,10 +114,17 @@ class CosyVoiceModel:
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:]} if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
self.hift_cache_dict[uuid] = {
'mel': tts_mel[:, :, -self.mel_cache_len:],
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_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)
if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
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),