README.md

metadata

title: Deep Voice — Bioacoustic Detection
emoji: 🐳
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 5.34.1
python_version: '3.11'
app_file: app.py
pinned: false
license: apache-2.0
short_description: Multi-species call detection in underwater recordings.

Deep Voice — Bioacoustic Call Detection

A public demo for running Deep Voice call-detection models on your own audio. Seven models trained in collaboration with marine biologists are available out of the box:

Species / call type	Window	Min input SR	Threshold
Arctic cod fish (Boreogadus saida)	1 s	2 kHz	0.5
Greater Caribbean manatee	0.2 s	96 kHz	0.5
Burrunan dolphin — barks	3 s	10 kHz	0.5
Burrunan dolphin — echo	3 s	96 kHz	0.9
Burrunan dolphin — buzz	0.5 s	96 kHz	0.8
Burrunan dolphin — whistle	1 s	96 kHz	0.5
Killer whale (orca) — 5-class	1.5 s	24 kHz	0.5
Humpback whale — Mozambique C1 group	1 s	16 kHz	0.5

Uploads at a higher sample rate are resampled automatically; uploads below the minimum are rejected (the missing high-frequency content can't be reconstructed).

How it works

1. Pick a model from the dropdown.
2. Upload .wav files (up to 500 MB total; runs that would exceed 15 min of compute on the free CPU tier are rejected with a clear message).
3. Click Run inference.
4. Download:
   • CSV — per-window class probabilities (one CSV per file; zipped if multiple).
   • Raven .txt — selection table compatible with Raven Pro / Lite. When you upload more than one file, the Raven outputs are merged into a single selection table with a Begin File column and per-file time offsets.

Running on the free HuggingFace CPU tier. Throughput varies a lot per model — low-sample-rate models (e.g. Arctic cod, 2 kHz internal) process ~100× faster than the 96 kHz dolphin/manatee models. The pre-flight line in the UI shows a per-model runtime estimate after you upload.

Models and code

• Soundbay training/inference framework: https://github.com/deep-voice/soundbay
• Checkpoints (gated): deepvoice1/bioacoustic-checkpoints on the HF Hub.
• This Space vendors the soundbay package and pulls checkpoints at runtime via HF_TOKEN.

If you'd like to use these models in your own pipeline, see the soundbay repo for the full inference CLI and config templates.

License

Apache 2.0 — please credit Deep Voice when publishing detections produced with these models.