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@@ -40,6 +40,8 @@ class CosyVoiceModel:
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# hift cache
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self.mel_cache_len = 20
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self.source_cache_len = int(self.mel_cache_len * 256)
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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.stream_scale_factor = 1
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assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
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@@ -50,7 +52,6 @@ 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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@@ -117,10 +118,9 @@ class CosyVoiceModel:
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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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- 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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+ 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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