Skip to main content

A Python library for adding effects to audio.

Project description

Pedalboard Logo

License: GPL v3 Documentation PyPI - Python Version Supported Platforms Apple Silicon support for macOS and Linux (Docker) PyPI - Wheel Test Badge Coverage Badge PyPI - Downloads DOI GitHub Repo stars

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.

Documentation

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 usage
    • Live audio effects via AudioStream
  • Built-in support for a number of basic audio transformations, including:
    • Guitar-style effects: Chorus, Distortion, Phaser, Clipping
    • Loudness and dynamic range effects: Compressor, Gain, Limiter
    • Equalizers and filters: HighpassFilter, LadderFilter, LowpassFilter
    • Spatial effects: Convolution, Delay, Reverb
    • Pitch effects: PitchShift
    • Lossy compression: GSMFullRateCompressor, MP3Compressor
    • Quality reduction: Resample, Bitcrush
  • Supports VST3® instrument and effect plugins on macOS, Windows, and Linux (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.data pipelines!

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 3.8, 3.9, 3.10, 3.11, 3.12, and 3.13.

  • Linux
    • Tested heavily in production use cases at Spotify
    • Tested automatically on GitHub with VSTs
    • Platform manylinux and musllinux wheels built for x86_64 (Intel/AMD) and aarch64 (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)

Examples

Note: If you'd rather watch a video instead of reading examples or documentation, watch Working with Audio in Python (feat. Pedalboard) on YouTube.

Quick start

from pedalboard import Pedalboard, Chorus, Reverb
from pedalboard.io import AudioFile

# Make a Pedalboard object, containing multiple audio plugins:
board = Pedalboard([Chorus(), Reverb(room_size=0.25)])

# Open an audio file for reading, just like a regular file:
with AudioFile('some-file.wav') as f:
  
  # Open an audio file to write to:
  with AudioFile('output.wav', 'w', f.samplerate, f.num_channels) as o:
  
    # Read one second of audio at a time, until the file is empty:
    while f.tell() < f.frames:
      chunk = f.read(f.samplerate)
      
      # Run the audio through our pedalboard:
      effected = board(chunk, f.samplerate, reset=False)
      
      # Write the output to our output file:
      o.write(effected)

Note: For more information about how to process audio through Pedalboard plugins, including how the reset parameter works, see the documentation for pedalboard.Plugin.process.

Making a guitar-style pedalboard

# Don't do import *! (It just makes this example smaller)
from pedalboard import *
from pedalboard.io import AudioFile

# Read in a whole file, resampling to our desired sample rate:
samplerate = 44100.0
with AudioFile('guitar-input.wav').resampled_to(samplerate) as f:
  audio = f.read(f.frames)

# Make a pretty interesting sounding guitar pedalboard:
board = Pedalboard([
    Compressor(threshold_db=-50, ratio=25),
    Gain(gain_db=30),
    Chorus(),
    LadderFilter(mode=LadderFilter.Mode.HPF12, cutoff_hz=900),
    Phaser(),
    Convolution("./guitar_amp.wav", 1.0),
    Reverb(room_size=0.25),
])

# Pedalboard objects behave like lists, so you can add plugins:
board.append(Compressor(threshold_db=-25, ratio=10))
board.append(Gain(gain_db=10))
board.append(Limiter())

# ... or change parameters easily:
board[0].threshold_db = -40

# Run the audio through this pedalboard!
effected = board(audio, samplerate)

# Write the audio back as a wav file:
with AudioFile('processed-output.wav', 'w', samplerate, effected.shape[0]) as f:
  f.write(effected)

Using VST3® or Audio Unit instrument and effect plugins

from pedalboard import Pedalboard, Reverb, load_plugin
from pedalboard.io import AudioFile
from mido import Message # not part of Pedalboard, but convenient!

# Load a VST3 or Audio Unit plugin from a known path on disk:
instrument = load_plugin("./VSTs/Magical8BitPlug2.vst3")
effect = load_plugin("./VSTs/RoughRider3.vst3")

print(effect.parameters.keys())
# dict_keys([
#   'sc_hpf_hz', 'input_lvl_db', 'sensitivity_db',
#   'ratio', 'attack_ms', 'release_ms', 'makeup_db',
#   'mix', 'output_lvl_db', 'sc_active',
#   'full_bandwidth', 'bypass', 'program',
# ])

# Set the "ratio" parameter to 15
effect.ratio = 15

# Render some audio by passing MIDI to an instrument:
sample_rate = 44100
audio = instrument(
  [Message("note_on", note=60), Message("note_off", note=60, time=5)],
  duration=5, # seconds
  sample_rate=sample_rate,
)

# Apply effects to this audio:
effected = effect(audio, sample_rate)

# ...or put the effect into a chain with other plugins:
board = Pedalboard([effect, Reverb()])
# ...and run that pedalboard with the same VST instance!
effected = board(audio, sample_rate)

Creating parallel effects chains

This example creates a delayed pitch-shift effect by running multiple Pedalboards in parallel on the same audio. Pedalboard objects are themselves Plugin objects, so you can nest them as much as you like:

from pedalboard import Pedalboard, Compressor, Delay, Distortion, Gain, PitchShift, Reverb, Mix

passthrough = Gain(gain_db=0)

delay_and_pitch_shift = Pedalboard([
  Delay(delay_seconds=0.25, mix=1.0),
  PitchShift(semitones=7),
  Gain(gain_db=-3),
])

delay_longer_and_more_pitch_shift = Pedalboard([
  Delay(delay_seconds=0.5, mix=1.0),
  PitchShift(semitones=12),
  Gain(gain_db=-6),
])

board = Pedalboard([
  # Put a compressor at the front of the chain:
  Compressor(),
  # Run all of these pedalboards simultaneously with the Mix plugin:
  Mix([
    passthrough,
    delay_and_pitch_shift,
    delay_longer_and_more_pitch_shift,
  ]),
  # Add a reverb on the final mix:
  Reverb()
])

Running Pedalboard on Live Audio

pedalboard supports streaming live audio through an AudioStream object, allowing for real-time manipulation of audio by adding effects in Python.

from pedalboard import Pedalboard, Chorus, Compressor, Delay, Gain, Reverb, Phaser
from pedalboard.io import AudioStream

# Open up an audio stream:
with AudioStream(
  input_device_name="Apogee Jam+",  # Guitar interface
  output_device_name="MacBook Pro Speakers"
) as stream:
  # Audio is now streaming through this pedalboard and out of your speakers!
  stream.plugins = Pedalboard([
      Compressor(threshold_db=-50, ratio=25),
      Gain(gain_db=30),
      Chorus(),
      Phaser(),
      Convolution("./guitar_amp.wav", 1.0),
      Reverb(room_size=0.25),
  ])
  input("Press enter to stop streaming...")

# The live AudioStream is now closed, and audio has stopped.

Using Pedalboard in tf.data Pipelines

import tensorflow as tf 

sr = 48000 

# Put whatever plugins you like in here:
plugins = pedalboard.Pedalboard([pedalboard.Gain(), pedalboard.Reverb()]) 

# Make a dataset containing random noise:
# NOTE: for real training, here's where you'd want to load your audio somehow:
ds = tf.data.Dataset.from_tensor_slices([np.random.rand(sr)])

# Apply our Pedalboard instance to the tf.data Pipeline:
ds = ds.map(lambda audio: tf.numpy_function(plugins.process, [audio, sr], tf.float32)) 

# Create and train a (dummy) ML model on this audio:
model = tf.keras.models.Sequential([tf.keras.layers.InputLayer(input_shape=(sr,)), tf.keras.layers.Dense(1)])
model.compile(loss="mse") 
model.fit(ds.map(lambda effected: (effected, 1)).batch(1), epochs=10)

For more examples, see:

Contributing

Contributions to pedalboard are welcomed! See CONTRIBUTING.md for details.

Citing

To cite pedalboard in academic work, use its entry on Zenodo: DOI 7817838

To cite via BibTeX:

@software{sobot_peter_2023_7817838,
  author       = {Sobot, Peter},
  title        = {Pedalboard},
  month        = jul,
  year         = 2021,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.7817838},
  url          = {https://doi.org/10.5281/zenodo.7817838}
}

License

pedalboard is Copyright 2021-2024 Spotify AB.

pedalboard is licensed under the GNU General Public License v3. pedalboard includes a number of libraries that are statically compiled, and which carry the following licenses:

VST is a registered trademark of Steinberg Media Technologies GmbH.

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

pedalboard-0.9.16-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.9 MB view details)

Uploaded CPython 3.13t manylinux: glibc 2.17+ x86-64

pedalboard-0.9.16-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.13t manylinux: glibc 2.17+ ARM64

pedalboard-0.9.16-cp313-cp313t-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.13t macOS 11.0+ ARM64

pedalboard-0.9.16-cp313-cp313t-macosx_10_14_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.13t macOS 10.14+ x86-64

pedalboard-0.9.16-cp313-cp313-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.13 Windows x86-64

pedalboard-0.9.16-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.9 MB view details)

Uploaded CPython 3.13 manylinux: glibc 2.17+ x86-64

pedalboard-0.9.16-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.13 manylinux: glibc 2.17+ ARM64

pedalboard-0.9.16-cp313-cp313-macosx_11_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.13 macOS 11.0+ ARM64

pedalboard-0.9.16-cp313-cp313-macosx_10_14_x86_64.whl (2.6 MB view details)

Uploaded CPython 3.13 macOS 10.14+ x86-64

pedalboard-0.9.16-cp312-cp312-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.12 Windows x86-64

pedalboard-0.9.16-cp312-cp312-win32.whl (2.7 MB view details)

Uploaded CPython 3.12 Windows x86

pedalboard-0.9.16-cp312-cp312-musllinux_1_1_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.12 musllinux: musl 1.1+ x86-64

pedalboard-0.9.16-cp312-cp312-musllinux_1_1_aarch64.whl (7.0 MB view details)

Uploaded CPython 3.12 musllinux: musl 1.1+ ARM64

pedalboard-0.9.16-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.9 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

pedalboard-0.9.16-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ ARM64

pedalboard-0.9.16-cp312-cp312-macosx_11_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.12 macOS 11.0+ ARM64

pedalboard-0.9.16-cp312-cp312-macosx_10_14_x86_64.whl (2.6 MB view details)

Uploaded CPython 3.12 macOS 10.14+ x86-64

pedalboard-0.9.16-cp312-cp312-macosx_10_14_universal2.whl (5.0 MB view details)

Uploaded CPython 3.12 macOS 10.14+ universal2 (ARM64, x86-64)

pedalboard-0.9.16-cp311-cp311-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.11 Windows x86-64

pedalboard-0.9.16-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.9 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pedalboard-0.9.16-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

pedalboard-0.9.16-cp311-cp311-macosx_11_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pedalboard-0.9.16-cp311-cp311-macosx_10_14_x86_64.whl (2.6 MB view details)

Uploaded CPython 3.11 macOS 10.14+ x86-64

pedalboard-0.9.16-cp310-cp310-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10 Windows x86-64

pedalboard-0.9.16-cp310-cp310-musllinux_1_1_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

pedalboard-0.9.16-cp310-cp310-musllinux_1_1_aarch64.whl (7.0 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ ARM64

pedalboard-0.9.16-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.9 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pedalboard-0.9.16-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

pedalboard-0.9.16-cp310-cp310-macosx_11_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pedalboard-0.9.16-cp310-cp310-macosx_10_14_x86_64.whl (2.6 MB view details)

Uploaded CPython 3.10 macOS 10.14+ x86-64

pedalboard-0.9.16-cp39-cp39-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.9 Windows x86-64

pedalboard-0.9.16-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.9 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pedalboard-0.9.16-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (7.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

pedalboard-0.9.16-cp39-cp39-macosx_11_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pedalboard-0.9.16-cp39-cp39-macosx_10_14_x86_64.whl (2.6 MB view details)

Uploaded CPython 3.9 macOS 10.14+ x86-64

File details

Details for the file pedalboard-0.9.16-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 03e73da46d6678a8adbd1ebc70bf38e4316918e44514eb9b1d7989bfb43cb30c
MD5 7f3acc914f6b9ab3bbd867e23e9078dc
BLAKE2b-256 a8c737a857a8ea7578e467f26888024c3314867297c54f3a63d58f219ff14ee7

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8abf69fe280c0c834750072692d5ebb3b4ba7ef49d50b369946d4743a723bb49
MD5 6897760d0805c4143ba2cff24ad9dcf6
BLAKE2b-256 9c97e7dd59c5ebd3f60b959330c46a335ba9e4d024d44c48179ace5e88f4ca3c

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313t-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313t-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a260d2c5bc79f90340e5eef985919da92188dea9b493b4756d622f6e7e151028
MD5 82f6eb720df1cb33a36e5f92bc420a05
BLAKE2b-256 9d0485e55fc2ce426790c03aeb49cc91a6d518ac85bca953cd833386dd9a319c

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313t-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313t-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 8ffadf7de250d4ccc45929f88b49979d9243077b910fb352eb3cc7231f240d8e
MD5 42c6bd6e8eef86a3dbd175fa357b5b3d
BLAKE2b-256 b981ca0059e343f306b276b6dce136095cfae82723da6022989f656449620fcc

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 1eea9fb96672014a663fa7309133c6506ebdd28d84249a7708eae18c0cabb35c
MD5 9e43a005c57a1b15838e0593825ec009
BLAKE2b-256 fe110996856a87d8ff37f4b2783b1173f0d47b552910c9a3ae914a38d52a2229

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0e46117dc1b62af13bdd4a7ac53b6d92b3a001f2c68ce89bacced9bfcaafe295
MD5 655dc56d5ed7e87a8545ee90fac52457
BLAKE2b-256 792cf876c2ae94dffb283ffe9c5b788dcc32430580fd99a5e99cf5ae25ef669d

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 4be376b2727341cac2540e9f0f3b1d0bb2449ceed7f92399bcfabae052ae7a1a
MD5 858fd58b56c4ef16995e9a8ccb80dea4
BLAKE2b-256 9295373d6a2d7018c1fb7f1d8c3ff40ae5c788dc2b51a3a3d32dcd3ce96086a3

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 031e6833c5983c3fe0a96bc4ff04790663a9e95a01c402828bd5a4eff4b8b541
MD5 492473748faf567cc2a62490be2c619f
BLAKE2b-256 d99cf9c8f5bd63c17523176308028a7a624978a8f7dd9d3c3b33487ba2aa66dd

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp313-cp313-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp313-cp313-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 b84fe32ab13b50076e21d1e79bbef7c00f43bb3623c84d7ced1022b0d9ed55ca
MD5 fa659a599d5be6aff930ad8da9003a27
BLAKE2b-256 6f3efed6202a68c51c26196f5da09385d9eb74b34f0fb72cc3eab80df505a6ce

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 6baf122b22403e5bff2477f37e17e457ad7db1621948debf558f8b2cfe64f39e
MD5 4ce76161ac911ea3f16402f7b24c4af1
BLAKE2b-256 5a3fc66f6b5d3729805c32f8a764b5a19da258e3af8a1ba3c41508eeaa9abd00

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-win32.whl.

File metadata

  • Download URL: pedalboard-0.9.16-cp312-cp312-win32.whl
  • Upload date:
  • Size: 2.7 MB
  • Tags: CPython 3.12, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 3d2a815c0fd045a252fdfa57baa9bf35e7dff5e1ddcda8bf1a9ae59b53d3e798
MD5 61f9f2f32ba8253e209de900be0576e7
BLAKE2b-256 eac66b42fac86cdba73669a19a667e995322c6719b61ebdedd7fac32db475d95

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 dece4acbc8d43f6a0b44c4af48fdeb90c181890babe88c77cfcee726fbb73865
MD5 4c47828ce1a8a991606b3102fdff9687
BLAKE2b-256 2a8ba35cee6a33274c135440aec3f0ae0782b9a0019aca0ec13a062cf5cd3c3e

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 3a523fba1756d1bed0f5e3ae9816d70ffa971457f67adf4efd03cca0310a879a
MD5 f16ee9a8faa884695a5f766f6d33b5f7
BLAKE2b-256 30f83009508d7114136b47c47cc142a2463acd7daebe9504f6771ea895d947a9

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 eac5dd6627bbd8c239c916bffbb7c5d9588d99867591d2e43ab6efd79f75ccbc
MD5 b33cdecab7e8316e1aad416d32957328
BLAKE2b-256 3905fb3641ceb261146e2cca76d748216fda50578ffb106e51518ad8023a9cf7

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 7e32ad20a3ffc13324cbf17a1c3fae9c08a391edabcfa371d2ad0ed08bedfffb
MD5 54d08d79f3c61a66c6eee16c9655a002
BLAKE2b-256 6e3e9910129edc8cddcc5c2dea5e3ee39eabeb363f141aac1d938f8783181357

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2b6161453ac8f8f28f67b49eeaf58f41448f61b54a73e151c0a59626ab574d08
MD5 9c13b09c21605f11da4cb264750662cd
BLAKE2b-256 f592bf8b59bc50e1a233958456558ac8809053b19f129e7851db13ad64be7883

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 4b92ab1c940d27dcd520cd376a61aa99d442edd27b6787ee6193ad7f8bd799e5
MD5 8e7f38d47a39de9c5105171db71c30ee
BLAKE2b-256 7158e053eab9654c51a652bac846853ee3149c3b8aa083fecf6236d1fe675235

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp312-cp312-macosx_10_14_universal2.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp312-cp312-macosx_10_14_universal2.whl
Algorithm Hash digest
SHA256 0f352356fdb8e6246a0577a62ce003781c2a5fa9ba0f6b09cf344e5a51142288
MD5 0fbb728085fcac42c720d8e13fad33b2
BLAKE2b-256 83afb7de13536d1e7e1e5998f3b73e5cd342296e0f8be00acc31318a7e54e4e6

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 abdb338b9231c509e0ed900507f128a65a79a272c3c284fa3912c53751a4f3c3
MD5 152c5644adcb36fa44d19533e9a1d587
BLAKE2b-256 bc7f319758b75fca05483ab834ba19ff56a3170b91be3c1ab291bf6efcce270e

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e870cac507fdae73fa0bfbe6d1d5de380228862d4eba9164d1615e9bccaf6d04
MD5 c7e942df833bf532416664535915901c
BLAKE2b-256 a038b05541be56015600417a1c8e739aea6fff88434b0eb014ee450f4304c625

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8873a52e06b41336f06f93306bb113545485bbf3759f30d5f6bf887c5056902d
MD5 157bb139829b5c7647f57a8a3683c134
BLAKE2b-256 b7c160fa96cfacc84617e1a512866c0c5d442656af88108133eb72bd06325549

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c90e80f3b8a6397b4918aabf4635053b8c62d55414038defa9906c378443f2f0
MD5 846408a7aeb71d865f16ac63b3323c0b
BLAKE2b-256 7a9e62a4a4bc6ad63d27cbe88cf5c4ce66bfb6306de093fa2f8d834b0a457c31

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp311-cp311-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp311-cp311-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 57f1a78e9d253263360d8de1d6bc24d37f827511fae63035a4d5b0477a891401
MD5 93c646699157d771e344eb1e76d9693a
BLAKE2b-256 92385b6bab2de1adcbd71a52505ac23c49d57f1e970cf76d514802c353deb841

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 6f82a4941e9ad72a0c672994fa45453521eedf05081367b77c0a09cdabc25b9c
MD5 e6ada901e1c2be77620e0cbffbfc7ba0
BLAKE2b-256 74dc02f5c2b9307964304e9d6231ce7061381319ee8b3894fe08ea39e4e62081

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 29ba6f8d785060f4c2e9e659d3ab45559c1ce222b468dc1676f6345dcc17b6df
MD5 59742b06d315dd00987fd9e918a039cf
BLAKE2b-256 517ae11f0ce0a8aaa327b3d4bd308f8ac0116ddaed9fa0f1bc3a57271c97da62

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp310-cp310-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp310-cp310-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 b040271cc9eadaa14f0a0afdc2393e8b11fe1ed6967de0ed2957b3dc0b2d1431
MD5 6ed9aae75e23bbac88ee52670eca04e7
BLAKE2b-256 12c7c8a42233e149c96875e71e7a0aa16d6d0b9a68311965d652803a1814afee

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5712fe0f07eb0a0955681b867c2aff7f57e3d4d8dbf05a3d6f1eb0404cb0e679
MD5 e49c31be21157b14296a20a13b2d333b
BLAKE2b-256 f9410e4dd72cd938a12e46d9b4b48df20c15a139390c2a99f31bb11c9dcd0652

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 89270613982174fb0ce6d10e6f251d78ef15aa21438c22753db87d974d5e2b27
MD5 f801177d3bdf1e84fe970d4d2bcbf5c2
BLAKE2b-256 0e5b5e5c633e197b79acf308aa8690bdc99342495f3ed2c5033111442601fe91

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2ac0f9c57a89c2e437a41288cd5da42d65d737457848fd95adfba7c97bb99914
MD5 1b5b69cac1ade037dce3363a3ff6d325
BLAKE2b-256 c9eae3549f4ff6816a7b16e1e8e782a6bd6356a41ebd969fd2d93f776256c879

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp310-cp310-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 9d68f2a0816f4b611ef9e6fd2d216c0f89840c7aa855efdb499b63b5e8014009
MD5 8f92db3b759715ab7cfa8c7b1d6a2875
BLAKE2b-256 ebd2faec9c347334358227a54540abf75fc0dea0f07d2ec704cbc94e0d2f22ed

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d46f917322065fe780778c416e016ab79e6a3d8ba75df9a71ae368142aba3443
MD5 e2c089aabc53c97c5042e648c8809069
BLAKE2b-256 88ac5f61d1baa599b1f316a21eb4907b5d7ce9890329a1c6faaeff88c60576ec

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8635d57f72be0f2eb8ca523dacec5bd13736ccffe5f70109c586a6b750fdeb33
MD5 511d3aa71f05db6989b58265bd208ee7
BLAKE2b-256 bf210df8a3192699424f7803daabaff71699030ef85e5c248db7d0df4d9d1943

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 822f3184dd09f07c29b7b0d4281fa834d9a94ed8aa86037c0c739e8907132f9f
MD5 9e4b9aaf8cb5c085e4aa10603f9a7a56
BLAKE2b-256 4137b5369ce3ff91a2f71b0d4170fcf10d7b5171473716678e06a9fdbb051ac5

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c47265698416d6e1042c21eaf027eb643f2e4f83421383f7ab783f7fb9320bd4
MD5 256cea43143f4d2c8889a39c25486e48
BLAKE2b-256 504a104b53d52a8f9a36e847d2908059e6c6475b43ac0491f2078cc22e0b9782

See more details on using hashes here.

Provenance

File details

Details for the file pedalboard-0.9.16-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.9.16-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 16b2610ad1923e0f49d1e6923c2b740c8da9a2db2c5315f56e857b7a862f1fdd
MD5 9e0270cef9695576074348e24a4345b7
BLAKE2b-256 3e96f48c7b6504b4c53181ba878fe92c144e618138991691f6e10991d7c59dce

See more details on using hashes here.

Provenance

Supported by

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