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@@ -403,11 +403,6 @@ class CosyVoice3Model(CosyVoice2Model):
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self.flow.half()
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# NOTE must matching training static_chunk_size
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self.token_hop_len = 25
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- # hift cache
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- self.mel_cache_len = 8
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- self.source_cache_len = int(self.mel_cache_len * 480)
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- # speech fade in out
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- self.speech_window = np.hamming(2 * self.source_cache_len)
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# rtf and decoding related
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self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
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self.lock = threading.Lock()
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@@ -428,26 +423,17 @@ class CosyVoice3Model(CosyVoice2Model):
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streaming=stream,
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finalize=finalize)
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tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
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- # append hift cache
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+ # append mel cache
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if self.hift_cache_dict[uuid] is not None:
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- hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
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+ hift_cache_mel = self.hift_cache_dict[uuid]['mel']
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tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
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+ self.hift_cache_dict[uuid]['mel'] = tts_mel
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else:
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- hift_cache_source = torch.zeros(1, 1, 0)
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- # keep overlap mel and hift cache
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- if finalize is False:
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- tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
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- if self.hift_cache_dict[uuid] is not None:
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- tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
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- self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
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- 'source': tts_source[:, :, -self.source_cache_len:],
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- 'speech': tts_speech[:, -self.source_cache_len:]}
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- tts_speech = tts_speech[:, :-self.source_cache_len]
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- else:
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- if speed != 1.0:
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- assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
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- tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
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- tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel)
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- if self.hift_cache_dict[uuid] is not None:
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- tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
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+ self.hift_cache_dict[uuid] = {'mel': tts_mel, 'speech_offset': 0}
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+ if speed != 1.0:
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+ assert token_offset == 0 and finalize is True, 'speed change only support non-stream inference mode'
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+ tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
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+ tts_speech, _ = self.hift.inference(speech_feat=tts_mel, finalize=finalize)
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+ tts_speech = tts_speech[:, self.hift_cache_dict[uuid]['speech_offset']:]
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+ self.hift_cache_dict[uuid]['speech_offset'] += tts_speech.shape[1]
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return tts_speech
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