HowFar-Caarma

HuBERT-based model for distance estimation from speech — predicting the physical distance between a speaker and a microphone from the audio signal.

Backbone: facebook/hubert-large-ls960-ft with a classification head trained on GAN-augmented data using the CAARMA framework.

Files

• epoch18_val_acc7997.ckpt — PyTorch Lightning checkpoint (epoch 18, val acc 79.97%)
• inference.py — minimal loader + embedding extraction script

Usage

pip install torch torchaudio transformers pytorch-lightning huggingface_hub

# Download the checkpoint
huggingface-cli download MassaBaali/HowFar-Caarma epoch18_val_acc7997.ckpt --local-dir .

# Run inference
python inference.py --ckpt epoch18_val_acc7997.ckpt --audio sample.wav

Or load it directly in Python:

from inference import load_model, extract_embedding

model = load_model("epoch18_val_acc7997.ckpt", device="cuda")
embedding = extract_embedding(model, "sample.wav", device="cuda")
print(embedding.shape)

Notes

• Expects 16 kHz mono audio.
• The checkpoint was trained with PyTorch Lightning; strict=False is used on load to tolerate minor state-dict key differences.
• This is the raw Lightning checkpoint rather than a transformers-native format, so standard AutoModel.from_pretrained will not work.

Citation

If you use this model, please cite:

@article{baali2025caarma,
  title={CAARMA: Class augmentation with adversarial mixup regularization},
  author={Baali, Massa and Li, Xiang and Chen, Hao and Hannan, Syed Abdul and Singh, Rita and Raj, Bhiksha},
  journal={Findings of the Association for Computational Linguistics: EMNLP},
  volume={2025},
  pages={9732--9742},
  year={2025}
}