---
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](https://huggingface.co/deepvoice1)** 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](https://www.ravensoundsoftware.com/).
     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.