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@@ -49,6 +49,7 @@ class CosyVoiceModel:
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self.llm_end_dict = {}
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self.mel_overlap_dict = {}
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self.hift_cache_dict = {}
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+ self.speech_window = np.hamming(2 * self.source_cache_len)
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def load(self, llm_model, flow_model, hift_model):
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self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
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@@ -113,10 +114,17 @@ class CosyVoiceModel:
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self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
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tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
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tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
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- self.hift_cache_dict[uuid] = {'source': tts_source[:, :, -self.source_cache_len:], 'mel': tts_mel[:, :, -self.mel_cache_len:]}
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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] = {
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+ '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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tts_speech, tts_source = self.hift.inference(mel=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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return tts_speech
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def inference(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
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