Skip to main content

A Python package providing a common interface for running machine learning models for audio classification tasks.

Project description

audioclass

A Python library that simplifies the process of using machine learning models for audio classification.

Description

Audioclass provides a unified interface for working with various audio classification models, making it easier to load, preprocess, batch process, and analyze audio data. It offers:

  • Standardized Model Interface: Easily swap between different model implementations (TensorFlow, TensorFlow Lite, etc.) without changing your code.
  • Flexible Data Loading: Load audio data from files, directories, or pandas DataFrames with a few simple commands.
  • Efficient Batch Processing: Effortlessly process large datasets in batches for faster inference.
  • Unified Postprocessing: Convert model outputs into easy-to-use formats like xarray datasets or soundevent objects.
  • Pre-trained Models: Get started quickly with built-in support for popular models like BirdNET and Perch.

Installation

Get started with audioclass in a snap:

pip install audioclass

Optional Dependencies:

  • For BirdNET: Install additional dependencies using pip install "audioclass[birdnet]".
  • For Perch: Install additional dependencies using pip install "audioclass[perch]".

How to Use It

Here's a quick example of how to load the BirdNET model, preprocess an audio file, and get predictions:

from audioclass.models.birdnet import BirdNET

# Load the model
model = BirdNET.load()

# Get predictions
predictions = model.process_file("path/to/audio/file.wav")

print(predictions)

For more detailed examples, tutorials, and complete API documentation, visit our documentation website.

Contributing

We welcome contributions to audioclass! If you'd like to get involved, please check out our Contributing Guidelines.

Attribution

  • The BirdNET model was developed by the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology, in collaboration with Chemnitz University of Technology. This package is not affiliated with the BirdNET project. If you use the BirdNET model, please cite the relevant paper (see the audioclass.models.birdnet module docstring for details).

  • The Perch model is a research product by Google Research. This package is not affiliated with Google Research.

Audioclass is an independent project and is not affiliated with either the BirdNET or Perch projects.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

audioclass-0.2.3.tar.gz (48.3 MB view details)

Uploaded Source

Built Distribution

audioclass-0.2.3-py3-none-any.whl (48.3 MB view details)

Uploaded Python 3

File details

Details for the file audioclass-0.2.3.tar.gz.

File metadata

  • Download URL: audioclass-0.2.3.tar.gz
  • Upload date:
  • Size: 48.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.22

File hashes

Hashes for audioclass-0.2.3.tar.gz
Algorithm Hash digest
SHA256 f35a3b2bd273f691d56cd9c123b2a175a60f7db99694279740afef2ab671fee2
MD5 1fef5526086b0ee115a9e1a5c107af1a
BLAKE2b-256 52f06e98fc483c98c1f68392b3afdda693cae12631ac4fc456b970bdd10a8b01

See more details on using hashes here.

File details

Details for the file audioclass-0.2.3-py3-none-any.whl.

File metadata

  • Download URL: audioclass-0.2.3-py3-none-any.whl
  • Upload date:
  • Size: 48.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.22

File hashes

Hashes for audioclass-0.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 9c6f32156e6766cdcae0341692d893e88fe1875fed149623c24666a79e697c2a
MD5 ad153809398ff9b921336c34bdca1e33
BLAKE2b-256 46c883d5db6906bcad8bbbaa6195fc0c20ee0d3b3debc09bd787e25584c75678

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page