| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960 |
- # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import torch
- class CosyVoiceModel:
- def __init__(self,
- llm: torch.nn.Module,
- flow: torch.nn.Module,
- hift: torch.nn.Module):
- self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- self.llm = llm
- self.flow = flow
- self.hift = hift
- def load(self, llm_model, flow_model, hift_model):
- self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
- self.llm.to(self.device).eval()
- self.flow.load_state_dict(torch.load(flow_model, map_location=self.device))
- self.flow.to(self.device).eval()
- self.hift.load_state_dict(torch.load(hift_model, map_location=self.device))
- self.hift.to(self.device).eval()
- def inference(self, text, text_len, flow_embedding, llm_embedding=torch.zeros(0, 192),
- prompt_text=torch.zeros(1, 0, dtype=torch.int32), prompt_text_len=torch.zeros(1, dtype=torch.int32),
- llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
- flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
- prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32)):
- tts_speech_token = self.llm.inference(text=text.to(self.device),
- text_len=text_len.to(self.device),
- prompt_text=prompt_text.to(self.device),
- prompt_text_len=prompt_text_len.to(self.device),
- prompt_speech_token=llm_prompt_speech_token.to(self.device),
- prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
- embedding=llm_embedding.to(self.device),
- beam_size=1,
- sampling=25,
- max_token_text_ratio=30,
- min_token_text_ratio=3)
- tts_mel = self.flow.inference(token=tts_speech_token,
- token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
- prompt_token=flow_prompt_speech_token.to(self.device),
- prompt_token_len=flow_prompt_speech_token_len.to(self.device),
- prompt_feat=prompt_speech_feat.to(self.device),
- prompt_feat_len=prompt_speech_feat_len.to(self.device),
- embedding=flow_embedding.to(self.device))
- tts_speech = self.hift.inference(mel=tts_mel).cpu()
- torch.cuda.empty_cache()
- return {'tts_speech': tts_speech}
|
