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---
tags:
- audio-classification
- speech-emotion-recognition
- automatic-speech-recognition
- emotion-recognition
- wav2vec2
- toronto-emotional-speech-dataset
datasets:
- toronto-emotional-speech-dataset
metrics:
- accuracy: 0.85
base_model: facebook/wav2vec2-base
model-index:
- name: dynann/emotion-speech-recognition
results: []
---
# Wav2Vec2 for Emotion Recognition
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the Toronto Emotional Speech Dataset (TESS).
It achieves the following results on the evaluation set:
- **Accuracy**: 85%
- **Loss**: ~3.76
## Model Description
The model classifies audio input into 7 discrete emotions:
- Angry
- Disgust
- Fear
- Happy
- Neutral
- Pleasant Surprise (`ps`)
- Sad
It uses a custom classification head on top of the frozen Wav2Vec2 base model.
## Usage
**Note**: You must define the custom `Wav2Vec2ForEmotionClassification` class to load this model.
```python
import torch
import torch.nn as nn
from transformers import Wav2Vec2Processor, Wav2Vec2Model, Wav2Vec2Config
# Define the Custom Model Class
class Wav2Vec2ForEmotionClassification(nn.Module):
def __init__(self, config):
super().__init__()
self.wav2vec2 = Wav2Vec2Model(config)
self.classifier = nn.Sequential(
nn.Linear(config.hidden_size, 256),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(256, 128),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(128, config.num_labels),
)
def forward(self, input_values, attention_mask=None, labels=None, **kwargs):
outputs = self.wav2vec2(input_values, attention_mask=attention_mask)
hidden_states = outputs.last_hidden_state
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, labels.view(-1))
return {
"loss": loss,
"logits": logits
}
# Load Model
model_id = "dynann/emotion-speech-recognition"
config = Wav2Vec2Config.from_pretrained(model_id)
model = Wav2Vec2ForEmotionClassification(config)
model.load_state_dict(torch.hub.load_state_dict_from_url(f"https://huggingface.co/{model_id}/resolve/main/pytorch_model.bin"))
processor = Wav2Vec2Processor.from_pretrained(model_id)
```
## Training Procedure
- **Epochs**: 10
- **Batch Size**: 32 (optimized for P100) / 8 (local)
- **Learning Rate**: 3e-4
- **Feature Encoder**: Frozen
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