PetraMicanovic/meld-audio-baseline

Audio Emotion Recognition on MELD

Baseline – Mean Temporal Pooling

This repository contains a pretrained audio-only emotion recognition model evaluated on the MELD dataset.

The model uses a pretrained Wav2Vec2 encoder and simple mean pooling over temporal frames to obtain utterance-level representations.

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Dataset

MELD (declare-lab/MELD)
Seven emotion classes:

neutral, joy, surprise, anger, sadness, fear, disgust

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Model Architecture

• Audio encoder: facebook/wav2vec2-base
• Pooling: Mean pooling over time frames
• Classifier: Fully connected layer
• Output: 7 emotion classes

This model serves as an audio-only baseline before introducing more advanced temporal or attention-based pooling mechanisms.

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Training Setup (Summary)

• Sampling rate: 16 kHz
• Batch size: 32
• Learning rate: 1e-4
• Optimizer: Adam
• Scheduler: ReduceLROnPlateau
• Epochs: 30
• Early stopping on validation weighted F1-score

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Evaluation Metrics

• Accuracy
• Weighted F1-score
• Confusion matrix

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Files

• pytorch_model.bin – Audio model weights
• config.json – Model configuration

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Reproducibility

The full training and evaluation pipeline is available in the corresponding GitHub repository notebooks.