📣🐋 Whale Speech

A pipeline to map whale encounters to hydrophone audio.

_{Derived from PacificSoundDetectHumpbackSong, though not directly affiliated with MBARI, NOAA, or HappyWhale.}

Installation

M1:

CONDA_SUBDIR=osx-arm64 conda create -n whale-speech python=3.11
conda activate whale-speech
pip install -r requirements.txt

Other:

conda create -n whale-speech python=3.11
conda activate whale-speech
pip install -r requirements.txt

Stages:

Input: When (and where*) to look for whale encounters on HappyWhale.
Geometry Search: Query open-oceans/happywhale to find potential whale encounters.

→ Expected outputs: encounter ids, start and end times, and longitude and latitude.
Retrive Audio: Download audio from MBARI's Pacific Ocean Sound Recordings around the time of the encounter.

→ Expected outputs: audio array, start and end times, and encounter ids.
Filter Frequency: Break audio into non-overlaping segments with flagged frequency detections.

→ Expected outputs: cut audio array, detection intervals, and encounter ids.
Classify Audio: Use a NOAA and Google's humpback_whale model to classify the flagged segments.

→ Expected outputs: resampled audio, classification score array, and encounter ids.
Postprocess Labels: Build clip-intervals for each encounter for playback snippets.

→ Expected outputs: encounter ids, cut/resampled audio array, and aggregated classification score.
Output: Map the whale encounter ids to the playback snippets.

_{*Currently only support encounters around the Monterey Bay Hydrophone (MARS).}