pedalboard is a Python library for working with audio: reading, writing, rendering, adding effects, and more. It supports most popular audio file formats and a number of common audio effects out of the box, and also allows the use of VST3® and Audio Unit formats for loading third-party software instruments and effects.
pedalboard was built by Spotify’s Audio Intelligence Lab to enable using studio-quality audio effects from within Python and TensorFlow. Internally at Spotify, pedalboard is used for data augmentation to improve machine learning models and to help power features like Spotify’s AI DJ and AI Voice Translation. pedalboard also helps in the process of content creation, making it possible to add effects to audio without using a Digital Audio Workstation.
Features#
Built-in audio I/O utilities (pedalboard.io)
Support for reading and writing AIFF, FLAC, MP3, OGG, and WAV files on all platforms with no dependencies
Additional support for reading AAC, AC3, WMA, and other formats depending on platform
Support for on-the-fly resampling of audio files and streams with
O(1)memory usageLive audio effects via ``AudioStream` <https://spotify.github.io/pedalboard/reference/pedalboard.io.html#pedalboard.io.AudioStream>`_
Built-in support for a number of basic audio transformations, including:
Guitar-style effects:
Chorus,Distortion,Phaser,ClippingLoudness and dynamic range effects:
Compressor,Gain,LimiterEqualizers and filters:
HighpassFilter,LadderFilter,LowpassFilterSpatial effects:
Convolution,Delay,ReverbPitch effects:
PitchShiftLossy compression:
GSMFullRateCompressor,MP3CompressorQuality reduction:
Resample,Bitcrush
Supports VST3® instrument and effect plugins on macOS, Windows, and Linux (``pedalboard.load_plugin` <https://spotify.github.io/pedalboard/reference/pedalboard.html#pedalboard.load_plugin>`_)
Supports instrument and effect Audio Units on macOS
Strong thread-safety, memory usage, and speed guarantees
Releases Python’s Global Interpreter Lock (GIL) to allow use of multiple CPU cores
No need to use
multiprocessing!
Even when only using one thread:
Processes audio up to 300x faster than pySoX for single transforms, and 2-5x faster than SoxBindings (via iCorv)
Reads audio files up to 4x faster than librosa.load (in many cases)
Tested compatibility with TensorFlow - can be used in
tf.datapipelines!
Installation#
pedalboard is available via PyPI (via Platform Wheels):
pip install pedalboard # That's it! No other dependencies required.
If you are new to Python, follow INSTALLATION.md for a robust guide.
Compatibility#
pedalboard is thoroughly tested with Python 3.6, 3.7, 3.8, 3.9, 3.10, 3.11, and 3.12 as well as experimental support for PyPy 3.7, 3.8, and 3.9.
Linux
Tested heavily in production use cases at Spotify
Tested automatically on GitHub with VSTs
Platform
manylinuxandmusllinuxwheels built forx86_64(Intel/AMD) andaarch64(ARM/Apple Silicon)Most Linux VSTs require a relatively modern Linux installation (with glibc > 2.27)
macOS
Tested manually with VSTs and Audio Units
Tested automatically on GitHub with VSTs
Platform wheels available for both Intel and Apple Silicon
Compatible with a wide range of VSTs and Audio Units
Windows
Tested automatically on GitHub with VSTs
Platform wheels available for
amd64(x86-64, Intel/AMD)