| --- |
| license: other |
| license_name: sam-license |
| license_link: LICENSE |
| pipeline_tag: audio-to-audio |
| library_name: pytorch |
| tags: |
| - bioacoustics |
| - audio |
| - denoising |
| - animal-vocalizations |
| - knowledge-distillation |
| --- |
| |
| # CallCleaner |
|
|
| **Lightweight bioacoustic denoising from an audio foundation model.** |
|
|
| CallCleaner removes background noise (wind, rain, vehicles, other animals) from recordings of |
| animal vocalizations. It is a 19M-parameter waveform-to-waveform model that needs no detector, |
| text prompt or GPU, and runs about 90× faster than real time on a laptop CPU. |
|
|
| [Project page](https://callcleaner.github.io) · [Paper (arXiv)](https://arxiv.org/abs/2611.11491) · [Code (GitHub)](https://github.com/morrisalp/CallCleaner) |
|
|
| ## How it was made |
|
|
| The source-separation foundation model **SAM-Audio** (`facebook/sam-audio-small`), prompted with |
| "animal sound" and with time spans from a simple animal voice-activity detector, removes more noise |
| from animal calls than dedicated bioacoustic denoisers, but it is slow and inherits detector errors. |
| CallCleaner distills it into a small model: |
|
|
| - **Architecture:** the Demucs denoiser from [Biodenoising](https://github.com/earthspecies/biodenoising) |
| (`biodenoising16k_dns48`, 16 kHz, mono), initialized from Biodenoising's weights. |
| - **Teacher:** SAM-Audio with VAD-span prompts. Its target stream is the pseudo-label; its residual |
| stream is a noise estimate. Half of each batch is remixed (another segment's residual added to a |
| pseudo-label), as in bootstrapped remixing. |
| - **Data:** 4 s segments (1.8 h) from the public corpora Biodenoising was trained on (DCASE few-shot |
| bioacoustics, Orchive and five bird corpora), none shared with its benchmark. |
| - **Training:** 2,000 steps. |
|
|
| On Biodenoising's benchmark (47 clips × 4 SNRs), CallCleaner leaves **11 dB less noise around the call** |
| than Biodenoising, the leading neural bioacoustic denoiser with public weights, and among fast, |
| CPU-capable systems it pushes the Pareto frontier of noise removal vs. call fidelity. See the paper |
| for full results. |
|
|
| ## Usage |
|
|
| ```bash |
| pip install biodenoising safetensors huggingface_hub librosa soundfile |
| ``` |
|
|
| ```python |
| import librosa, soundfile as sf, torch |
| from biodenoising.denoiser.pretrained import biodenoising16k_dns48 |
| from huggingface_hub import hf_hub_download |
| from safetensors.torch import load_file |
| |
| model = biodenoising16k_dns48(pretrained=False) |
| model.load_state_dict(load_file(hf_hub_download("malper/CallCleaner", "model.safetensors"))) |
| model.eval() |
| |
| y, _ = librosa.load("noisy.wav", sr=16000, mono=True) # the model works at 16 kHz, mono |
| with torch.no_grad(): |
| clean = model(torch.tensor(y)[None, None])[0, 0, : len(y)].numpy() |
| sf.write("clean.wav", clean, 16000) |
| ``` |
|
|
| For long recordings, process overlapping chunks (e.g. 60 s with a 1 s cross-fade); the command-line |
| script in the [code repository](https://github.com/morrisalp/CallCleaner) does this, and handles folders and other sample rates. |
|
|
| ## Limitations |
|
|
| - Output is 16 kHz: content above 8 kHz (e.g. many bat and some bird and insect calls) is not restored. |
| - Trained to keep one caller's vocalizations; overlapping calls from other animals may be treated as noise. |
| - Distilled from a teacher that can miss quiet calls or keep loud non-target sounds; it inherits some |
| of these errors. Listen to outputs before using them for analysis. |
|
|
| ## License |
|
|
| The weights are a derivative of SAM-Audio and are released under the **SAM License** |
| (see [LICENSE](LICENSE)); by using or redistributing them you agree to its terms. The Biodenoising |
| architecture and initialization are MIT-licensed. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{xie2026callcleaner, |
| title = {CallCleaner: Lightweight Bioacoustic Denoising from an Audio Foundation Model}, |
| author = {Michael Xie and Ga{\v{s}}per Begu{\v{s}} and Morris Alper}, |
| year = {2026}, |
| eprint = {2611.11491}, |
| archivePrefix = {arXiv} |
| } |
| ``` |
|
|
