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meld-audio-baseline
PetraMicanovic/meld-audio-baseline
meld-audio-baseline is a machine learning model from PetraMicanovic. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This repository contains a pretrained audio-only emotion recognition model evaluated on the MELD dataset.
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At a glance
- License
- mit
- Model type
- bert-embedding-mlp
- Access
- Public
- Created
- Jan 31, 2026
- Updated
- Feb 3, 2026
- SHA
- d7813bc6
Model card
From the Hugging Face model README
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.
Dataset
MELD (declare-lab/MELD)
Seven emotion classes:
neutral, joy, surprise, anger, sadness, fear, disgust
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.
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
Evaluation Metrics
- Accuracy
- Weighted F1-score
- Confusion matrix
Files
pytorch_model.bin– Audio model weightsconfig.json– Model configuration
Reproducibility
The full training and evaluation pipeline is available in the corresponding GitHub repository notebooks.
- Type
- bert-embedding-mlp
- License
- mit
- Languages
- en
- Created
- Jan 31, 2026
- Updated
- Feb 3, 2026