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https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-05 01:49:25 +08:00
add llm export script
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@@ -47,11 +47,18 @@ class CosyVoiceModel:
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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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self.llm.to(self.device).eval()
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self.llm.half()
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self.flow.load_state_dict(torch.load(flow_model, map_location=self.device))
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self.flow.to(self.device).eval()
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self.hift.load_state_dict(torch.load(hift_model, map_location=self.device))
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self.hift.to(self.device).eval()
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def load_script(self, llm_text_encoder_model, llm_llm_model):
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llm_text_encoder = torch.jit.load(llm_text_encoder_model)
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self.llm.text_encoder = llm_text_encoder
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llm_llm = torch.jit.load(llm_llm_model)
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self.llm.llm = llm_llm
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def llm_job(self, text, text_len, prompt_text, prompt_text_len, llm_prompt_speech_token, llm_prompt_speech_token_len, llm_embedding, this_uuid):
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with self.llm_context:
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for i in self.llm.inference(text=text.to(self.device),
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@@ -60,7 +67,7 @@ class CosyVoiceModel:
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prompt_text_len=prompt_text_len.to(self.device),
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prompt_speech_token=llm_prompt_speech_token.to(self.device),
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prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
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embedding=llm_embedding.to(self.device),
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embedding=llm_embedding.to(self.device).half(),
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sampling=25,
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max_token_text_ratio=30,
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min_token_text_ratio=3):
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