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@@ -401,10 +401,6 @@ class CosyVoice2Model(CosyVoiceModel):
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prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs):
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# this_uuid is used to track variables related to this inference thread
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this_uuid = str(uuid.uuid1())
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- # NOTE in cache mode, trim flow_prompt to same size as flow_decoder_required_cache_size
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- if self.use_flow_cache is True:
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- flow_prompt_speech_token = flow_prompt_speech_token[:, -self.flow_decoder_required_cache_size:]
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- prompt_speech_feat = prompt_speech_feat[:, -self.flow_decoder_required_cache_size * 2:]
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with self.lock:
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self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
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self.hift_cache_dict[this_uuid] = None
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@@ -412,6 +408,10 @@ class CosyVoice2Model(CosyVoiceModel):
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p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
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p.start()
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if stream is True:
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+ assert self.use_flow_cache is True, "set use_flow_cache=True if you want to use stream inference to avoid OOM"
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+ # NOTE in cache mode, trim flow_prompt to same size as flow_decoder_required_cache_size
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+ flow_prompt_speech_token = flow_prompt_speech_token[:, -self.flow_decoder_required_cache_size:]
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+ prompt_speech_feat = prompt_speech_feat[:, -self.flow_decoder_required_cache_size * 2:]
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while True:
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time.sleep(0.1)
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if len(self.tts_speech_token_dict[this_uuid]) >= self.token_hop_len + self.flow.pre_lookahead_len:
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@@ -442,6 +442,7 @@ class CosyVoice2Model(CosyVoiceModel):
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yield {'tts_speech': this_tts_speech.cpu()}
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else:
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# deal with all tokens
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+ assert self.use_flow_cache is False, "set use_flow_cache=False for nonstream inference"
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p.join()
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this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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