forwarder1121/ast-finetuned-model

AST Fine-Tuned Model for Emotion Classification

AST Fine-Tuned Model for Emotion Classification

This is a fine-tuned Audio Spectrogram Transformer (AST) model, specifically designed for classifying emotions in speech audio. The model was fine-tuned on the CREMA-D dataset, focusing on six emotional categories. The base model was sourced from MIT's pre-trained AST model.

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Model Details

• Base Model: MIT/ast-finetuned-audioset-10-10-0.4593
• Fine-Tuned Dataset: CREMA-D
• Architecture: Audio Spectrogram Transformer (AST)
• Model Type: Single-label classification
• Input Features: Log-Mel Spectrograms (128 mel bins)
• Output Classes:
  • ANG: Anger
  • DIS: Disgust
  • FEA: Fear
  • HAP: Happiness
  • NEU: Neutral
  • SAD: Sadness

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Model Configuration

• Hidden Size: 768
• Number of Attention Heads: 12
• Number of Hidden Layers: 12
• Patch Size: 16
• Maximum Length: 1024
• Dropout Probability: 0.0
• Activation Function: GELU (Gaussian Error Linear Unit)
• Optimizer: Adam
• Learning Rate: 1e-4

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Training Details

• Dataset: CREMA-D (Emotion-Labeled Speech Data)
• Data Augmentation:
  • Noise injection
  • Time shifting
  • Speed perturbation
• Fine-Tuning Epochs: 5
• Batch Size: 16
• Learning Rate Scheduler: Linear decay
• Best Validation Accuracy: 60.71%
• Best Checkpoint: ./results/checkpoint-1119

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How to Use

Load the Model

from transformers import AutoModelForAudioClassification, AutoProcessor

# Load the model and processor
model = AutoModelForAudioClassification.from_pretrained("forwarder1121/ast-finetuned-model")
processor = AutoProcessor.from_pretrained("forwarder1121/ast-finetuned-model")

# Prepare input audio (e.g., waveform) as log-mel spectrogram
inputs = processor("path_to_audio.wav", sampling_rate=16000, return_tensors="pt")

# Make predictions
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()

print(f"Predicted emotion: {model.config.id2label[str(predicted_class)]}")

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Metrics

Validation Results

• Best Validation Accuracy: 60.71%
• Validation Loss: 1.1126

Evaluation Details

• Eval Dataset: CREMA-D test split
• Batch Size: 16
• Number of Steps: 94

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Limitations

• The model was trained on CREMA-D, which has a specific set of speech data. It may not generalize well to datasets with different accents, speech styles, or languages.
• Validation accuracy is 60.71%, indicating room for improvement for real-world deployment.

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Acknowledgments

This work is based on the Audio Spectrogram Transformer (AST) model by MIT, fine-tuned for emotion classification. Special thanks to the developers of Hugging Face and the CREMA-D dataset contributors.

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License

The model is shared under the MIT License. Refer to the licensing details in the repository.

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Citation

If you use this model in your work, please cite:

@misc{ast-finetuned-model,
  author = {forwarder1121},
  title = {Fine-Tuned Audio Spectrogram Transformer for Emotion Classification},
  year = {2024},
  url = {https://huggingface.co/forwarder1121/ast-finetuned-model},
}

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Contact

For questions, reach out to [email protected].