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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. | |
| from functools import partial | |
| import onnxruntime | |
| import torch | |
| import numpy as np | |
| import whisper | |
| import torchaudio.compliance.kaldi as kaldi | |
| class CosyVoiceFrontEnd: | |
| def __init__(self, speech_tokenizer_model: str, device: str = 'cuda', device_id: int = 0): | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| option = onnxruntime.SessionOptions() | |
| option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL | |
| option.intra_op_num_threads = 1 | |
| self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, providers=["CUDAExecutionProvider" if device == "cuda" and torch.cuda.is_available() else "CPUExecutionProvider"]) | |
| def extract_speech_token(self, speech): | |
| feat = whisper.log_mel_spectrogram(speech, n_mels=128) | |
| speech_token = self.speech_tokenizer_session.run(None, {self.speech_tokenizer_session.get_inputs()[0].name: feat.detach().cpu().numpy(), | |
| self.speech_tokenizer_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist() | |
| speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device) | |
| speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device) | |
| return speech_token, speech_token_len | |
| def _extract_spk_embedding(self, speech): | |
| feat = kaldi.fbank(speech, | |
| num_mel_bins=80, | |
| dither=0, | |
| sample_frequency=16000) | |
| feat = feat - feat.mean(dim=0, keepdim=True) | |
| embedding = self.campplus_session.run(None, {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist() | |
| embedding = torch.tensor([embedding]).to(self.device) | |
| return embedding | |
| def _extract_speech_feat(self, speech): | |
| speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device) | |
| speech_feat = speech_feat.unsqueeze(dim=0) | |
| speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device) | |
| return speech_feat, speech_feat_len |