mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
121 lines
8.1 KiB
Python
121 lines
8.1 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import numpy as np
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class CosyVoiceModel:
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def __init__(self,
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llm: torch.nn.Module,
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flow: torch.nn.Module,
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hift: torch.nn.Module):
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.llm = llm
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self.flow = flow
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self.hift = hift
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self.stream_win_len = 60
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self.stream_hop_len = 50
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self.overlap = 4395 # 10 token equals 4395 sample point
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self.window = np.hamming(2 * self.overlap)
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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.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 inference(self, text, text_len, flow_embedding, llm_embedding=torch.zeros(0, 192),
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prompt_text=torch.zeros(1, 0, dtype=torch.int32), prompt_text_len=torch.zeros(1, dtype=torch.int32),
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llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
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flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
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prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32), stream=False):
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if stream is True:
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tts_speech_token, cache_speech = [], None
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for i in self.llm.inference(text=text.to(self.device),
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text_len=text_len.to(self.device),
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prompt_text=prompt_text.to(self.device),
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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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beam_size=1,
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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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stream=stream):
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tts_speech_token.append(i)
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if len(tts_speech_token) == self.stream_win_len:
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this_tts_speech_token = torch.concat(tts_speech_token, dim=1)
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this_tts_mel = self.flow.inference(token=this_tts_speech_token,
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token_len=torch.tensor([this_tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
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prompt_token=flow_prompt_speech_token.to(self.device),
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prompt_token_len=flow_prompt_speech_token_len.to(self.device),
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prompt_feat=prompt_speech_feat.to(self.device),
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prompt_feat_len=prompt_speech_feat_len.to(self.device),
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embedding=flow_embedding.to(self.device))
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this_tts_speech = self.hift.inference(mel=this_tts_mel).cpu()
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# fade in/out if necessary
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if cache_speech is not None:
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this_tts_speech[:, :self.overlap] = this_tts_speech[:, :self.overlap] * self.window[:self.overlap] + cache_speech * self.window[-self.overlap:]
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yield {'tts_speech': this_tts_speech[:, :-self.overlap]}
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cache_speech = this_tts_speech[:, -self.overlap:]
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tts_speech_token = tts_speech_token[-(self.stream_win_len - self.stream_hop_len):]
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# deal with remain tokens
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if cache_speech is None or len(tts_speech_token) > self.stream_win_len - self.stream_hop_len:
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this_tts_speech_token = torch.concat(tts_speech_token, dim=1)
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this_tts_mel = self.flow.inference(token=this_tts_speech_token,
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token_len=torch.tensor([this_tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
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prompt_token=flow_prompt_speech_token.to(self.device),
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prompt_token_len=flow_prompt_speech_token_len.to(self.device),
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prompt_feat=prompt_speech_feat.to(self.device),
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prompt_feat_len=prompt_speech_feat_len.to(self.device),
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embedding=flow_embedding.to(self.device))
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this_tts_speech = self.hift.inference(mel=this_tts_mel).cpu()
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if cache_speech is not None:
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this_tts_speech[:, :self.overlap] = this_tts_speech[:, :self.overlap] * self.window[:self.overlap] + cache_speech * self.window[-self.overlap:]
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yield {'tts_speech': this_tts_speech}
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else:
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assert len(tts_speech_token) == self.stream_win_len - self.stream_hop_len, 'tts_speech_token not equal to {}'.format(self.stream_win_len - self.stream_hop_len)
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yield {'tts_speech': cache_speech}
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else:
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tts_speech_token = []
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for i in self.llm.inference(text=text.to(self.device),
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text_len=text_len.to(self.device),
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prompt_text=prompt_text.to(self.device),
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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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beam_size=1,
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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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stream=stream):
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tts_speech_token.append(i)
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assert len(tts_speech_token) == 1, 'tts_speech_token len should be 1 when stream is {}'.format(stream)
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tts_speech_token = torch.concat(tts_speech_token, dim=1)
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tts_mel = self.flow.inference(token=tts_speech_token,
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token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
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prompt_token=flow_prompt_speech_token.to(self.device),
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prompt_token_len=flow_prompt_speech_token_len.to(self.device),
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prompt_feat=prompt_speech_feat.to(self.device),
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prompt_feat_len=prompt_speech_feat_len.to(self.device),
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embedding=flow_embedding.to(self.device))
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tts_speech = self.hift.inference(mel=tts_mel).cpu()
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torch.cuda.empty_cache()
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yield {'tts_speech': tts_speech}
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