alp-data
What is alp-data?
alp-data (Animal Language Processing data) is a Python package that provides access to 35+ bioacoustic datasets behind one unified interface. Designed to support machine learning across species, the current datasets cover birds, marine mammals, primates, insects, anurans, and multi-taxon benchmarks.
This is the first piece of shared infrastructure for Animal Language Processing (ALP), an AI-powered, data-driven, and species-agnostic approach to studying animal communication introduced in April 2026. ALP's promise of working across taxa depends on being able to access ML-friendly data across taxa through a single interface. That access layer is what alp-data provides.
Why alp-data?
Today, bioacoustic datasets are distributed across many repositories — Zenodo, OSF, GBIF, and institutional archives — and each arrives with its own conventions for format, structure, sampling rate, and licensing. As a result, researchers working across multiple datasets must typically invest considerable effort in preprocessing: writing a custom loader for each dataset, then a separate step to combine them, before any of the data can be used together.
alp-data handles this overhead, letting you combine datasets and work through a single consistent interface from the start:
- Unified dataset interface: access datasets stored locally, on cloud storage, or in various formats (CSV, JSON, Parquet) through a consistent API, with iteration and random-access indexing.
- Streaming support: work with large datasets that don't fit into memory by streaming samples as you go.
- Transformations: apply transforms such as row/column filtering and label creation per sample.
- Combinations: concatenate or chain multiple datasets into a single unified dataset for training or evaluation.