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from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.nn as nn
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2PreTrainedModel,
Wav2Vec2Model
)
@dataclass
class AudioClassifierOutput:
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class Wav2Vec2ForAudioClassification(Wav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wav2vec2 = Wav2Vec2Model(config)
self.classifier = nn.Sequential(
nn.Linear(config.hidden_size, config.classifier_proj_size),
nn.GELU(),
nn.Dropout(config.final_dropout),
nn.Linear(config.classifier_proj_size, config.num_labels)
)
self.init_weights()
def freeze_feature_encoder(self):
self.wav2vec2.feature_extractor._freeze_parameters()
def forward(
self,
input_values,
attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
outputs = self.wav2vec2(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
pooled_output = torch.mean(hidden_states, dim=1)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
return AudioClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) |