# birdnet-team/BirdNET-Analyzer

BirdNET analyzer for scientific audio data processing.

Repository: https://github.com/birdnet-team/BirdNET-Analyzer
Canonical: https://ross.abutalabs.com/products/birdnet-analyzer
Homepage: https://birdnet-team.github.io/BirdNET-Analyzer/
Language: Python
License: MIT
License Family: permissive
Topics: bioacoustics, birds, birdsong, acoustic-monitoring, deep-learning
Last push: 2026-08-25T17:26:07+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 44, longevity 100
- inputs: {"age_days": 1806, "days_push": 8, "days_rel": 299, "gap_med": 32, "n_releases_24m": 10}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1681, forks 285 (observed 2026-08-28T04:05:21.603252+00:00)

## What it is
BirdNET-Analyzer is an open-source Python tool from the Cornell Lab of Ornithology that uses deep learning to identify bird species from audio recordings, supporting over 6,500 species. It can process large batches of audio data or single files and offers CLI, GUI, and library interfaces for scientific bioacoustic analysis.

## Use cases
- identify bird species from audio recordings
- process large batches of acoustic monitoring data
- classify birdsong in scientific research
- detect which birds are calling in field recordings
- run bioacoustic analysis without a CS background
- analyze passive acoustic monitoring recordings for avian diversity

## When to choose
- you need to identify bird species from audio files or large recording datasets
- you are doing ecological or bioacoustic research and want a well-cited, actively maintained tool
- you want a ready-to-use analyzer with CLI, GUI, and Python API options

## When to avoid
- you need to classify non-bird animal sounds or general audio events
- you want to train custom models from scratch (see the BirdNET training tools instead)
- you need real-time embedded deployment on microcontrollers (use BirdNET-Tiny or related repos)

## Facets
- artifact type: cli-tool
- maturity: active
- function: machine-learning, deep-learning, audio-processing, cli
- domain: machine-learning
- platform: windows, python, cli
- tags: bioacoustics, birdsong-classification, acoustic-monitoring, species-identification, cornell-lab-of-ornithology, audio, natural-language-processing, linux, macos, docker

## Member repositories
- birdnet-team/BirdNET-Analyzer (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:21.603252+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:41:24.992575+00:00, confidence not recorded.
  - readme: https://github.com/birdnet-team/BirdNET-Analyzer (fetched 2026-08-28T04:05:21.603252+00:00, sha edc3d5c8b48c)
  - homepage: https://birdnet-team.github.io/BirdNET-Analyzer/ (fetched 2026-08-29T11:14:28.487980+00:00, sha 0a3d787c9335)
  - registry_pypi: https://pypi.org/pypi/birdnet-analyzer/json (fetched 2026-08-29T11:14:28.497082+00:00, sha b0b6cf98bcce)
- Data as of 2026-08-30T08:39:29.467469+00:00.