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from dataclasses import dataclass |
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from typing import Optional, Tuple |
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import torch |
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from transformers.file_utils import ModelOutput |
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from .wav2vec2fsr_config import W2V2FSRConfig |
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@dataclass |
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class SpeechClassifierOutput(ModelOutput): |
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loss: Optional[torch.FloatTensor] = None |
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logits: torch.FloatTensor = None |
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None |
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attentions: Optional[Tuple[torch.FloatTensor]] = None |
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import torch |
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import torch.nn as nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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from transformers.models.wav2vec2.modeling_wav2vec2 import ( |
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Wav2Vec2PreTrainedModel, |
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Wav2Vec2Model |
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) |
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class Wav2Vec2ClassificationHead(nn.Module): |
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"""Head for wav2vec classification task.""" |
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config_class = W2V2FSRConfig |
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def __init__(self, config): |
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super().__init__() |
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self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
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self.dropout = nn.Dropout(config.final_dropout) |
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self.out_proj = nn.Linear(config.hidden_size, config.num_labels) |
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def forward(self, features, **kwargs): |
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x = features |
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x = self.dropout(x) |
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x = self.dense(x) |
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x = torch.tanh(x) |
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x = self.dropout(x) |
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x = self.out_proj(x) |
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return x |
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class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel): |
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config_class = W2V2FSRConfig |
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def __init__(self, config): |
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super().__init__(config) |
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self.num_labels = config.num_labels |
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self.pooling_mode = config.pooling_mode |
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self.config = config |
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self.wav2vec2 = Wav2Vec2Model(config) |
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self.classifier = Wav2Vec2ClassificationHead(config) |
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self.init_weights() |
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def freeze_feature_extractor(self): |
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self.wav2vec2.feature_extractor._freeze_parameters() |
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def merged_strategy( |
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self, |
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hidden_states, |
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mode="mean" |
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): |
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if mode == "mean": |
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outputs = torch.mean(hidden_states, dim=1) |
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elif mode == "sum": |
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outputs = torch.sum(hidden_states, dim=1) |
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elif mode == "max": |
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outputs = torch.max(hidden_states, dim=1)[0] |
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else: |
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raise Exception( |
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"The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']") |
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return outputs |
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def forward( |
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self, |
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input_values, |
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attention_mask=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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labels=None, |
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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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outputs = self.wav2vec2( |
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input_values, |
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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 = outputs[0] |
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hidden_states = self.merged_strategy(hidden_states, mode=self.pooling_mode) |
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logits = self.classifier(hidden_states) |
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loss = None |
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if labels is not None: |
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if self.config.problem_type is None: |
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if self.num_labels == 1: |
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self.config.problem_type = "regression" |
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
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self.config.problem_type = "single_label_classification" |
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else: |
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self.config.problem_type = "multi_label_classification" |
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if self.config.problem_type == "regression": |
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loss_fct = MSELoss() |
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loss = loss_fct(logits.view(-1, self.num_labels), labels) |
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elif self.config.problem_type == "single_label_classification": |
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loss_fct = CrossEntropyLoss() |
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) |
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elif self.config.problem_type == "multi_label_classification": |
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loss_fct = BCEWithLogitsLoss() |
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loss = loss_fct(logits, labels) |
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if not return_dict: |
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output = (logits,) + outputs[2:] |
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return ((loss,) + output) if loss is not None else output |
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return SpeechClassifierOutput( |
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loss=loss, |
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logits=logits, |
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hidden_states=outputs.hidden_states, |
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attentions=outputs.attentions, |
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) |
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