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| # 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 os | |
| import uuid | |
| import time | |
| from tqdm import tqdm | |
| import torch | |
| import torchaudio | |
| from hyperpyyaml import load_hyperpyyaml | |
| from cosyvoice.cli.frontend import CosyVoiceFrontEnd | |
| from cosyvoice.cli.model import CosyVoiceModel | |
| class CosyVoice: | |
| def __init__( | |
| self, | |
| model_dir, | |
| ): | |
| self.model_dir = model_dir | |
| with open("{}/cosyvoice.yaml".format(model_dir), "r") as f: | |
| configs = load_hyperpyyaml(f) | |
| self.frontend = CosyVoiceFrontEnd( | |
| configs["feat_extractor"], | |
| "{}/campplus.onnx".format(model_dir), | |
| "{}/speech_tokenizer_v1.onnx".format(model_dir), | |
| ) | |
| self.model = CosyVoiceModel(configs["flow"], configs["hift"]) | |
| self.model.load( | |
| "{}/flow.pt".format(model_dir), | |
| "{}/hift.pt".format(model_dir), | |
| ) | |
| self.model.flow = self.model.flow.to(torch.bfloat16) | |
| del configs | |
| def token_to_wav_offline( | |
| self, | |
| speech_token, | |
| speech_feat, | |
| speech_feat_len, | |
| prompt_token, | |
| prompt_token_len, | |
| embedding, | |
| ): | |
| tts_mel = self.model.flow.inference( | |
| token=speech_token.to(self.model.device), | |
| token_len=torch.tensor([speech_token.size(1)], dtype=torch.int32).to( | |
| self.model.device | |
| ), | |
| prompt_token=prompt_token.to(self.model.device), | |
| prompt_token_len=prompt_token_len.to(self.model.device), | |
| prompt_feat=speech_feat.to(self.model.device), | |
| prompt_feat_len=speech_feat_len.to(self.model.device), | |
| embedding=embedding.to(self.model.device), | |
| ) | |
| tts_speech = self.model.hift.inference(mel=tts_mel.float())[0].cpu() | |
| return tts_speech | |