biome_edge_bio

BioME: A Resource-Efficient Bioacoustic Foundational Model

BioME (Bioacoustic Modulation-aware Encoder) is a resource-efficient audio encoder designed for bioacoustic applications. BioME is trained via layer-to-layer distillation from a high-capacity teacher model (BEATs), enabling strong representational transfer while significantly reducing the parameter count. To further improve ecological generalization, the model is pretrained on multi-domain data spanning speech, environmental sounds, and animal vocalizations. A key contribution is the integration of modulation-aware acoustic features via FiLM conditioning, injecting a DSP-inspired inductive bias that enhances feature disentanglement in low-capacity regimes.

You can read the full preprint here

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Checkpoints

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🚀 How To Use

Installation

pip install -U transformers

Load Model and Extract Features

import torch
import torchaudio
from transformers import AutoModel

# Load pre-trained model
model = AutoModel.from_pretrained("Hguimaraes/biome_edge_bio", trust_remote_code=True).cuda().eval()

# Load audio and resample to 16kHz
wav, sr = torchaudio.load_audio("path/to/audio")  # (batch_size, wav_len)
wav = torchaudio.functional.resample(
    wav,
    sr,
    16000,
    lowpass_filter_width=64,
    rolloff=0.9475937167399596,
    resampling_method="sinc_interp_kaiser",
    beta=14.769656459379492,
)

# Extract features
with torch.no_grad():
    output = model(wav)

# output["last_hidden_states"]: final output (batch_size, seq_len, encoder_dim)
# output["hidden_states"]:      list of 12 elements with (batch_size, seq_len, encoder_dim) tensors (features for each layer)

For more details on the model architecture, please check the file modeling_biome.py

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📖 Citation

@article{guimaraes2026biome,
  title={BioME: A Resource-Efficient Bioacoustic Foundational Model for IoT Applications},
  author={Guimar{\~a}es, Heitor R and Tiwari, Abhishek and Abdollahi, Mahsa and Avila, Anderson R and Falk, Tiago H},
  journal={arXiv preprint arXiv:2602.09970},
  year={2026}
}

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Acknowledgement

Much of our code base (and even this README.md!) is based on the following repositories:

• USAD
• BEATs
• Llama 3
• OpenBEATs

Thank you so much to the authors!