| from transformers import Wav2Vec2Model,Wav2Vec2Config,Wav2Vec2FeatureExtractor |
| from transformers.modeling_outputs import BaseModelOutput |
| from torchaudio.functional import resample |
| import torch |
| from torch import nn |
| from torch.nn import functional as F |
| import numpy as np |
| W2V_MODEL_PATH = "/mnt/pfs-mc0p4k/tts/team/digital_avatar_group/sunwenzhang/qiyuan/code/AMD_linear/ckpt/wav2vec" |
| def linear_interpolation(features, seq_len): |
| features = features.transpose(1, 2) |
| output_features = F.interpolate(features, size=seq_len, align_corners=True, mode='linear') |
| return output_features.transpose(1, 2) |
| class Wav2Vec2ModelLerp(Wav2Vec2Model): |
| def __init__(self, config: Wav2Vec2Config): |
| super().__init__(config) |
|
|
| def forward( |
| self, |
| input_values, |
| seq_len, |
| attention_mask=None, |
| mask_time_indices=None, |
| output_attentions=None, |
| output_hidden_states=None, |
| return_dict=None, |
| ): |
| self.config.output_attentions = True |
|
|
| output_hidden_states = ( |
| output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
| ) |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
| extract_features = self.feature_extractor(input_values) |
| extract_features = extract_features.transpose(1, 2) |
| extract_features = linear_interpolation(extract_features, seq_len=seq_len) |
|
|
| if attention_mask is not None: |
| |
| attention_mask = self._get_feature_vector_attention_mask( |
| extract_features.shape[1], attention_mask, add_adapter=False |
| ) |
|
|
| hidden_states, extract_features = self.feature_projection(extract_features) |
| hidden_states = self._mask_hidden_states( |
| hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask |
| ) |
|
|
| encoder_outputs = self.encoder( |
| hidden_states, |
| attention_mask=attention_mask, |
| output_attentions=output_attentions, |
| output_hidden_states=output_hidden_states, |
| return_dict=return_dict, |
| ) |
|
|
| hidden_states = encoder_outputs[0] |
|
|
| if self.adapter is not None: |
| hidden_states = self.adapter(hidden_states) |
|
|
| if not return_dict: |
| return (hidden_states, ) + encoder_outputs[1:] |
| return BaseModelOutput( |
| last_hidden_state=hidden_states, |
| hidden_states=encoder_outputs.hidden_states, |
| attentions=encoder_outputs.attentions, |
| ) |
|
|
|
|
| def feature_extract( |
| self, |
| input_values, |
| seq_len, |
| ): |
| extract_features = self.feature_extractor(input_values) |
| extract_features = extract_features.transpose(1, 2) |
| extract_features = linear_interpolation(extract_features, seq_len=seq_len) |
|
|
| return extract_features |
|
|
| def encode( |
| self, |
| extract_features, |
| attention_mask=None, |
| mask_time_indices=None, |
| output_attentions=None, |
| output_hidden_states=None, |
| return_dict=None, |
| ): |
| self.config.output_attentions = True |
|
|
| output_hidden_states = ( |
| output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
| ) |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
| if attention_mask is not None: |
| |
| attention_mask = self._get_feature_vector_attention_mask( |
| extract_features.shape[1], attention_mask, add_adapter=False |
| ) |
| |
|
|
| hidden_states, extract_features = self.feature_projection(extract_features) |
| hidden_states = self._mask_hidden_states( |
| hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask |
| ) |
|
|
| encoder_outputs = self.encoder( |
| hidden_states, |
| attention_mask=attention_mask, |
| output_attentions=output_attentions, |
| output_hidden_states=output_hidden_states, |
| return_dict=return_dict, |
| ) |
|
|
| hidden_states = encoder_outputs[0] |
|
|
| if self.adapter is not None: |
| hidden_states = self.adapter(hidden_states) |
|
|
| if not return_dict: |
| return (hidden_states, ) + encoder_outputs[1:] |
| return BaseModelOutput( |
| last_hidden_state=hidden_states, |
| hidden_states=encoder_outputs.hidden_states, |
| attentions=encoder_outputs.attentions, |
| ) |
| class AudioProcessor: |
| def __init__( |
| self, |
| device, |
| sampling_rate:int=16000, |
| only_last_features:bool=False |
| ): |
| self._only_last_features = only_last_features |
| self.audio_encoder = Wav2Vec2ModelLerp.from_pretrained(W2V_MODEL_PATH, local_files_only=True).to(device) |
| self.audio_encoder.requires_grad_(False) |
| self._processor = Wav2Vec2FeatureExtractor.from_pretrained(W2V_MODEL_PATH, local_files_only=True) |
| self.sr = sampling_rate |
| self.device = device |
|
|
| @torch.no_grad() |
| def process(self, audio:torch.Tensor,sampling_rate:int,seq_len:int): |
| if len(audio.shape) == 2: |
| audio = torch.mean(audio,dim=0) |
| audio = resample(audio,sampling_rate,self.sr) |
| audio_feature = np.squeeze(self._processor(audio,sampling_rate=self.sr).input_values) |
| audio_feature = torch.from_numpy(audio_feature) |
| audio_feature = audio_feature.unsqueeze(0).to(self.device) |
| embeddings = self.audio_encoder(audio_feature, seq_len, output_hidden_states=True) |
| if self._only_last_features: |
| return embeddings.last_hidden_state |
| else: |
| return torch.concat(embeddings.hidden_states[1:],dim=0).permute(1,0,2) |
| @torch.no_grad() |
| def __call__(self, audio:torch.Tensor,sampling_rate:int,seq_len:int): |
| if len(audio.shape) == 2: |
| audio = torch.mean(audio,dim=0) |
| audio = resample(audio,sampling_rate,self.sr) |
| audio_feature = np.squeeze(self._processor(audio,sampling_rate=self.sr).input_values) |
| audio_feature = torch.from_numpy(audio_feature) |
| audio_feature = audio_feature.unsqueeze(0).to(self.device) |
| embeddings = self.audio_encoder(audio_feature, seq_len, output_hidden_states=True) |
| if self._only_last_features: |
| return embeddings.last_hidden_state |
| else: |
| return torch.concat(embeddings.hidden_states[1:],dim=0).permute(1,0,2) |
| if __name__ == "__main__": |
| processor = AudioProcessor(torch.device("cuda:7"),only_last_features=False) |
| x = torch.load("/mnt/pfs-mc0p4k/tts/team/digital_avatar_group/liuhuaize/dataset/hdtf_dataset//audio_emb/RD_Radio11_000.pt") |
| |
| |
