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--- |
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license: bsd-3-clause |
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tags: |
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- meg |
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- brain-signals |
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- speech-detection |
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- conformer |
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- libribrain |
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datasets: |
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- pnpl/LibriBrain |
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metrics: |
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- f1 |
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library_name: pytorch |
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model-index: |
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- name: megconformer-speech-detection |
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results: |
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- task: |
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type: audio-classification |
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name: Speech classification |
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dataset: |
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name: LibriBrain 2025 PNPL (Standard track, speech task) |
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type: pnpl/LibriBrain |
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split: holdout |
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metrics: |
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- name: F1-macro |
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type: f1 |
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value: 0.8890 |
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args: |
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average: macro |
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--- |
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# MEGConformer for Speech Detection |
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Conformer-based MEG decoder for binary speech detection, trained with 10 different random seeds for reproducibility. |
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## Model Performance |
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| Seed | Val F1-Macro | Checkpoint | |
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|------|--------------|------------| |
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| 0 (best) | **87.06%** | `seed-0/pytorch_model.ckpt` | |
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| 6 | 86.80% | `seed-6/pytorch_model.ckpt` | |
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| 4 | 86.62% | `seed-4/pytorch_model.ckpt` | |
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| 1 | 86.54% | `seed-1/pytorch_model.ckpt` | |
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| 2 | 86.37% | `seed-2/pytorch_model.ckpt` | |
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| 5 | 86.29% | `seed-5/pytorch_model.ckpt` | |
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| 7 | 86.18% | `seed-7/pytorch_model.ckpt` | |
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| 3 | 86.13% | `seed-3/pytorch_model.ckpt` | |
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| 8 | 85.92% | `seed-8/pytorch_model.ckpt` | |
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| 9 | 85.18% | `seed-9/pytorch_model.ckpt` | |
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- **Holdout score of seed 0:** 88.90% |
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## Quick Start |
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### Load Best Model |
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```python |
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import torch |
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from huggingface_hub import hf_hub_download |
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from libribrain_experiments.models.configurable_modules.classification_module import ( |
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ClassificationModule, |
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) |
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# Download a checkpoint (seed-0) |
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checkpoint_path = hf_hub_download( |
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repo_id="zuazo/megconformer-speech-detection", filename="seed-0/pytorch_model.ckpt" |
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) |
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# Choose device |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Load model and move to device |
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model = ClassificationModule.load_from_checkpoint(checkpoint_path, map_location=device) |
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model.eval() |
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# Inference |
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meg_signal = torch.randn(1, 306, 125, device=device) # Create directly on device |
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with torch.no_grad(): |
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logits = model(meg_signal) |
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prediction = torch.argmax(logits, dim=1) # 0=silence, 1=speech |
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print(f"Prediction: {'Speech' if prediction.item() == 1 else 'Silence'}") |
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``` |
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## Model Details |
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- **Architecture**: Conformer Small |
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- Hidden size: 144 |
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- FFN dim: 576 |
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- Layers: 16 |
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- Attention heads: 4 |
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- Depthwise conv kernel: 31 |
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- **Input**: 306-channel MEG signals |
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- **Window size**: 2.5 seconds (625 samples at 250 Hz) |
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- **Output**: Binary classification (silence/speech) |
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- **Training**: [LibriBrain](https://huggingface.co/datasets/pnpl/LibriBrain) 2025 Standard track |
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## Reproducibility |
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All 10 random seeds are provided to ensure reproducibility. |
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## Citation |
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```bibtex |
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@misc{dezuazo2025megconformerconformerbasedmegdecoder, |
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title={MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification}, |
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author={Xabier de Zuazo and Ibon Saratxaga and Eva Navas}, |
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year={2025}, |
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eprint={2512.01443}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2512.01443}, |
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} |
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``` |
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## License |
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The 3-Clause BSD License |
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## Links |
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- **Paper**: [arXiv:2512.01443](https://arxiv.org/abs/2512.01443) |
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- **Code**: [GitHub](https://github.com/neural2speech/libribrain-experiments) |
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- **Competition**: [LibriBrain 2025](https://neural-processing-lab.github.io/2025-libribrain-competition/) |
