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"""
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This module defines the Wav2Vec model, which is a pre-trained model for speech recognition and understanding.
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It inherits from the Wav2Vec2Model class in the transformers library and provides additional functionalities
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such as feature extraction and encoding.
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Classes:
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Wav2VecModel: Inherits from Wav2Vec2Model and adds additional methods for feature extraction and encoding.
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Functions:
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linear_interpolation: Interpolates the features based on the sequence length.
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"""
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import torch.nn.functional as F
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from transformers import Wav2Vec2Model
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from transformers.modeling_outputs import BaseModelOutput
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class Wav2VecModel(Wav2Vec2Model):
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"""
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Wav2VecModel is a custom model class that extends the Wav2Vec2Model class from the transformers library.
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It inherits all the functionality of the Wav2Vec2Model and adds additional methods for feature extraction and encoding.
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...
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Attributes:
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base_model (Wav2Vec2Model): The base Wav2Vec2Model object.
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Methods:
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forward(input_values, seq_len, attention_mask=None, mask_time_indices=None
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, output_attentions=None, output_hidden_states=None, return_dict=None):
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Forward pass of the Wav2VecModel.
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It takes input_values, seq_len, and other optional parameters as input and returns the output of the base model.
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feature_extract(input_values, seq_len):
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Extracts features from the input_values using the base model.
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encode(extract_features, attention_mask=None, mask_time_indices=None, output_attentions=None, output_hidden_states=None, return_dict=None):
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Encodes the extracted features using the base model and returns the encoded features.
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"""
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def forward(
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self,
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input_values,
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seq_len,
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attention_mask=None,
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mask_time_indices=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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"""
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Forward pass of the Wav2Vec model.
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Args:
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self: The instance of the model.
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input_values: The input values (waveform) to the model.
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seq_len: The sequence length of the input values.
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attention_mask: Attention mask to be used for the model.
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mask_time_indices: Mask indices to be used for the model.
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output_attentions: If set to True, returns attentions.
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output_hidden_states: If set to True, returns hidden states.
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return_dict: If set to True, returns a BaseModelOutput instead of a tuple.
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Returns:
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The output of the Wav2Vec model.
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"""
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self.config.output_attentions = True
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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extract_features = self.feature_extractor(input_values)
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extract_features = extract_features.transpose(1, 2)
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extract_features = linear_interpolation(extract_features, seq_len=seq_len)
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if attention_mask is not None:
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attention_mask = self._get_feature_vector_attention_mask(
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extract_features.shape[1], attention_mask, add_adapter=False
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)
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hidden_states, extract_features = self.feature_projection(extract_features)
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hidden_states = self._mask_hidden_states(
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hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
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)
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encoder_outputs = self.encoder(
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hidden_states,
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attention_mask=attention_mask,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = encoder_outputs[0]
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if self.adapter is not None:
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hidden_states = self.adapter(hidden_states)
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if not return_dict:
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return (hidden_states, ) + encoder_outputs[1:]
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=encoder_outputs.hidden_states,
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attentions=encoder_outputs.attentions,
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)
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def feature_extract(
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self,
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input_values,
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seq_len,
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):
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"""
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Extracts features from the input values and returns the extracted features.
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Parameters:
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input_values (torch.Tensor): The input values to be processed.
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seq_len (torch.Tensor): The sequence lengths of the input values.
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Returns:
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extracted_features (torch.Tensor): The extracted features from the input values.
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"""
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extract_features = self.feature_extractor(input_values)
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extract_features = extract_features.transpose(1, 2)
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extract_features = linear_interpolation(extract_features, seq_len=seq_len)
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return extract_features
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def encode(
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self,
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extract_features,
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attention_mask=None,
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mask_time_indices=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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"""
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Encodes the input features into the output space.
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Args:
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extract_features (torch.Tensor): The extracted features from the audio signal.
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attention_mask (torch.Tensor, optional): Attention mask to be used for padding.
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mask_time_indices (torch.Tensor, optional): Masked indices for the time dimension.
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output_attentions (bool, optional): If set to True, returns the attention weights.
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output_hidden_states (bool, optional): If set to True, returns all hidden states.
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return_dict (bool, optional): If set to True, returns a BaseModelOutput instead of the tuple.
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Returns:
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The encoded output features.
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"""
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self.config.output_attentions = True
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if attention_mask is not None:
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attention_mask = self._get_feature_vector_attention_mask(
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extract_features.shape[1], attention_mask, add_adapter=False
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)
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hidden_states, extract_features = self.feature_projection(extract_features)
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hidden_states = self._mask_hidden_states(
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hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
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)
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encoder_outputs = self.encoder(
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hidden_states,
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attention_mask=attention_mask,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = encoder_outputs[0]
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if self.adapter is not None:
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hidden_states = self.adapter(hidden_states)
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if not return_dict:
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return (hidden_states, ) + encoder_outputs[1:]
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=encoder_outputs.hidden_states,
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attentions=encoder_outputs.attentions,
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)
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def linear_interpolation(features, seq_len):
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"""
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Transpose the features to interpolate linearly.
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Args:
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features (torch.Tensor): The extracted features to be interpolated.
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seq_len (torch.Tensor): The sequence lengths of the features.
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Returns:
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torch.Tensor: The interpolated features.
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"""
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features = features.transpose(1, 2)
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output_features = F.interpolate(features, size=seq_len, align_corners=True, mode='linear')
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return output_features.transpose(1, 2)
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