Skip to main content

A Python library for adding effects to audio.

Project description

Pedalboard Logo

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

pedalboard is a Python library for adding effects to audio. It supports a number of common audio effects out of the box, and also allows the use of VST3® and Audio Unit plugin formats for third-party effects. It 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. 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 support for a number of basic audio transformations:
    • Convolution
    • Compressor
    • Chorus
    • Distortion
    • Gain
    • HighpassFilter
    • LadderFilter
    • Limiter
    • LowpassFilter
    • Phaser
    • PitchShift (provided by Chris Cannam's Rubber Band Library)
    • Reverb
  • Supports VST3® plugins on macOS, Windows, and Linux (pedalboard.load_plugin)
  • Supports 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 faster1 than SoxBindings
  • Tested compatibility with TensorFlow - can be used in tf.data pipelines!

Installation

pedalboard is available via PyPI (via Platform Wheels):

pip install pedalboard

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, and 3.10 as well as experimental support for PyPy 7.3.

  • Linux
    • Tested heavily in production use cases at Spotify
    • Tested automatically on GitHub with VSTs
    • Platform manylinux 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)

Plugin Compatibility

pedalboard allows loading VST3® and Audio Unit plugins, which could contain any code. Most plugins that have been tested work just fine with pedalboard, but some plugins may not work with pedalboard; at worst, some may even crash the Python interpreter without warning and with no ability to catch the error. For an ever-growing compatibility list, see COMPATIBILITY.md.

Most audio plugins are "well-behaved" and conform to a set of conventions for how audio plugins are supposed to work, but many do not conform to the VST3® or Audio Unit specifications. pedalboard attempts to detect some common programming errors in plugins and can work around many issues, including automatically detecting plugins that don't clear their internal state when asked. Even so, plugins can misbehave without pedalboard noticing.

If audio is being rendered incorrectly or if audio is "leaking" from one process() call to the next in an undesired fashion, try:

  1. Passing silence to the plugin in between calls to process(), to ensure that any reverb tails or other internal state has time to fade to silence
  2. Reloading the plugin every time audio is processed (with pedalboard.load_plugin)

Examples

A very basic example of how to use pedalboard's built-in plugins:

import soundfile as sf
from pedalboard import (
    Pedalboard,
    Convolution,
    Compressor,
    Chorus,
    Gain,
    Reverb,
    Limiter,
    LadderFilter,
    Phaser,
)

audio, sample_rate = sf.read('some-file.wav')

# Make a Pedalboard object, containing multiple plugins:
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),
], sample_rate=sample_rate)

# 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())

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

# Write the audio back as a wav file:
with sf.SoundFile('./processed-output-stereo.wav', 'w', samplerate=sample_rate, channels=len(effected.shape)) as f:
    f.write(effected)

Loading a VST3® plugin and manipulating its parameters

import soundfile as sf
from pedalboard import Pedalboard, Reverb, load_plugin

# Load a VST3 package from a known path on disk:
vst = load_plugin("./VSTs/RoughRider3.vst3")

print(vst.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
vst.ratio = 15

# Use this VST to process some audio:
audio, sample_rate = sf.read('some-file.wav')
effected = vst(audio, sample_rate=sample_rate)

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

For more examples, see:

Contributing

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

Frequently Asked Questions

Can Pedalboard be used with live (real-time) audio?

Technically, yes, Pedalboard could be used with live audio input/output. See @stefanobazzi's guitarboard project for an example that uses the python-sounddevice library to wire Pedalboard up to live audio.

However, there are a couple big caveats when talking about using Pedalboard in a live context. Python, as a language, is garbage-collected, meaning that your code randomly pauses on a regular interval to clean up unused objects. In most programs, this is not an issue at all. However, for live audio, garbage collection can result in random pops, clicks, or audio drop-outs that are very difficult to prevent.

Note that if your application processes audio in a streaming fashion, but allows for large buffer sizes (multiple seconds of audio) or soft real-time requirements, Pedalboard can be used there without issue. Examples of this use case include streaming audio processing over the network, or processing data offline but chunk-by-chunk.

Does Pedalboard support changing a plugin's parameters over time?

Yes! While there's no built-in function for this, it is possible to vary the parameters of a plugin over time manually:

import numpy
from pedalboard import Pedalboard, Compressor, Reverb

input_audio = ...
output_audio = np.zeros_like(input_audio)
board = Pedalboard([Compressor(), Reverb()])
reverb = board[-1]

# smaller step sizes would give a smoother transition,
# at the expense of processing speed
step_size_in_samples = 100

# Manually step through the audio 100 samples at a time
for i in range(0, input_audio.shape[0], step_size_in_samples):
    # Set the reverb's "wet" parameter to be equal to the percentage through the track
    # (i.e.: make a ramp from 0% to 100%)
    percentage_through_track = i / input_audio.shape[0]
    reverb.wet_level = percentage_through_track
    
    # Process this chunk of audio, setting `reset` to `False`
    # to ensure that reverb tails aren't cut off
    chunk = board.process(input_audio[i : i + step_size_in_samples], reset=False)
    output_audio[i : i + step_size_in_samples] = chunk

With this technique, it's possible to automate any parameter. Usually, using a step size of somewhere between 100 and 1,000 (2ms to 22ms at a 44.1kHz sample rate) is small enough to avoid hearing any audio artifacts, but big enough to avoid slowing down the code dramatically.

Can Pedalboard be used with VST instruments, instead of effects?

Not yet! The underlying framework (JUCE) supports VST and AU instruments just fine, but Pedalboard itself would have to be modified to support instruments.

Can Pedalboard plugins accept MIDI?

Not yet, either - although the underlying framework (JUCE) supports passing MIDI to plugins, so this would also be possible to add.

License

pedalboard is Copyright 2021 Spotify AB.

pedalboard is licensed under the GNU General Public License v3, because:

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-0.3.12-pp38-pypy38_pp73-win_amd64.whl (2.5 MB view details)

Uploaded PyPy Windows x86-64

pedalboard-0.3.12-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pedalboard-0.3.12-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

pedalboard-0.3.12-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (3.3 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

pedalboard-0.3.12-pp37-pypy37_pp73-win_amd64.whl (2.5 MB view details)

Uploaded PyPy Windows x86-64

pedalboard-0.3.12-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pedalboard-0.3.12-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

pedalboard-0.3.12-pp37-pypy37_pp73-macosx_10_9_x86_64.whl (3.3 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

pedalboard-0.3.12-cp310-cp310-win_amd64.whl (2.5 MB view details)

Uploaded CPython 3.10 Windows x86-64

pedalboard-0.3.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pedalboard-0.3.12-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

pedalboard-0.3.12-cp310-cp310-macosx_10_9_universal2.whl (6.3 MB view details)

Uploaded CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

pedalboard-0.3.12-cp39-cp39-win_amd64.whl (2.5 MB view details)

Uploaded CPython 3.9 Windows x86-64

pedalboard-0.3.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pedalboard-0.3.12-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

pedalboard-0.3.12-cp39-cp39-macosx_10_9_universal2.whl (6.3 MB view details)

Uploaded CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

pedalboard-0.3.12-cp38-cp38-win_amd64.whl (2.5 MB view details)

Uploaded CPython 3.8 Windows x86-64

pedalboard-0.3.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pedalboard-0.3.12-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.3 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

pedalboard-0.3.12-cp38-cp38-macosx_10_9_universal2.whl (6.3 MB view details)

Uploaded CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

pedalboard-0.3.12-cp37-cp37m-win_amd64.whl (2.5 MB view details)

Uploaded CPython 3.7m Windows x86-64

pedalboard-0.3.12-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

pedalboard-0.3.12-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

pedalboard-0.3.12-cp37-cp37m-macosx_10_9_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

pedalboard-0.3.12-cp36-cp36m-win_amd64.whl (2.7 MB view details)

Uploaded CPython 3.6m Windows x86-64

pedalboard-0.3.12-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ x86-64

pedalboard-0.3.12-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.4 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

pedalboard-0.3.12-cp36-cp36m-macosx_10_9_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file pedalboard-0.3.12-pp38-pypy38_pp73-win_amd64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-pp38-pypy38_pp73-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: PyPy, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 25cc082f960c5297b8edc97e0a85e2e029d2dc6bdd47bc7b309d4c213cc4165d
MD5 0c8c2a25696468c85580bbf0ec18f709
BLAKE2b-256 24727e72b81de0bbe979f631f1fbecb631d80ba73a01d77e6d732ccb88b814bf

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4c11aea4289bab6a9ae11786ea8f0c7f398383ea2998ab4e2d264903697f0370
MD5 e04a881e0d0bb7f000d21083020a6990
BLAKE2b-256 a576ab2b1c39575e6e132c09d354403592d376b2e535ccc5a34e66285fabda8f

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 2df7d780bcd90a0664d4dc67f89fa70943cd63a40a6e37664f0b5f5918c0a019
MD5 e2a41dd1c740766b53896ad1076c0be3
BLAKE2b-256 4a0abe63f1b2d97a31b26ced0d29877f87f71ec05c8a0228ddbf270565ef2bfc

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: PyPy, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6e93debb1f50468d1c5e27656433fe0c56dc5f8045b1cdaebb5339b0b7c2a459
MD5 b2c3739a1a69523861cb76580a5bef98
BLAKE2b-256 343edd1dd2a98296d14f828896b9c6e11d1f962203e166714adffad1ad7310b0

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-pp37-pypy37_pp73-win_amd64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-pp37-pypy37_pp73-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: PyPy, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-pp37-pypy37_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 4ee6bc8f3bec4802647116068aad3a31ef6c6bdbc6174691a95d119011a56aaf
MD5 ef92f6b2007f9378c0aa3a54967e6187
BLAKE2b-256 08b95434bb38d51402a769807ba3353ed146559ca49782b68b8323673e715f17

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9141cf114fc9cbd0c0ec40a280120bc668ea8936795f153cee3a03474e471d42
MD5 df86627cf1517712bd6bab96b851b4b0
BLAKE2b-256 58ec1d23164ed83941e485a1228b564ff3eb3d39a67438f66e8bb7fcfefbf31d

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f536f55aa9cbcf86874f99b4c70ad989eefd37a0bbe00a47f42e5efee7ee2825
MD5 1aa2c46461413add64ff971078d1ea50
BLAKE2b-256 d945c7d7483fcec34320146b8730e127c2e06d5c8679aa87c3330f93221a6d94

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-pp37-pypy37_pp73-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-pp37-pypy37_pp73-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: PyPy, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-pp37-pypy37_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3658164a8c75450f10033aa98daa00ca19d31d5706ce7a5f81e71b277f9fa43b
MD5 50b7614c97d798d5af1df458c8cdedc5
BLAKE2b-256 f3bb51146624b94e0268fd8a3b72961dae7910361d9e0861c0c7e74ccefcb4d2

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 84803f14a29626a69027cece71fcebcf091fbd070a124ef9ef87744736f15d0c
MD5 10ea5b28247f4359e7758212d1df06d6
BLAKE2b-256 5a78f81a40a61851c533f27607d5761e8ab5e63d7c20316c5a6dbb1fdec3db72

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f61f8016feafdf18cd00188022b21076df5c54b53464e6456a6850c14aa18321
MD5 cbe60d623ad5a5099f1ba8e7e2ecff97
BLAKE2b-256 59dc227de5abcf4f2d224fa875754dbad3c0f4adda07dde4973d7c5796844dae

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 1d52ba2da178ac08c768b4cecfff0bf0965fd27a521864de32353b8e9e6776a2
MD5 54eec38e71ed141133594e0e0997cc5c
BLAKE2b-256 a7287f2287c7c3cb7fd2e459b6293d80db6deefc674516551f8c0c69d01c40a8

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp310-cp310-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 6.3 MB
  • Tags: CPython 3.10, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 590ce0b0699357b48d51108c9865fd8871324d40c4c9c19fdc7a98883295c44a
MD5 34dff54074184a436d7db7f2461f9482
BLAKE2b-256 efe901ae74c2c8471d74d8c52f837ff75d1ed86b5d864ba30c2c96adc09e218e

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 90a8dd030345fdebee7ba1746489735b054f5e8808f1fdc49e6049c17174b2e4
MD5 9fa7c002a6646dce150b3f9fb8d51e15
BLAKE2b-256 2e43f62d0b7f05467b29720109310b2873012f8b0500cfbd423bd6f423d63015

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1db70ada4df5b952c64e8810571ae059cde9e22bbb42106318ed8cef3564c894
MD5 50a1ae770d10d18f9d7541d89b278221
BLAKE2b-256 0a6fc2d71f8e6bd3da3621549d1d796d5ca3e5705a245b597d6c9888c3adc614

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a697e1ed360009ba3b9a31f0057d4980d442d0ba23d70c070b3862582dbeaa55
MD5 005aba16b423dbad3f38a017d9554dd1
BLAKE2b-256 ba7739b413aef7952b8826ce02afff0439d981e550c58c0abd930318185f4249

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp39-cp39-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 6.3 MB
  • Tags: CPython 3.9, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 dd6ec08d62833f57e97142f6c56024c0078d1012cae293b1f1096e3dbd981e1c
MD5 bbdfb9fb22107763a2baaa61edb76469
BLAKE2b-256 d181de38ce868307df8e4238e8cd10f7bf0f68f80b31e909988f8f300a8db07e

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 16d243f3ad4441c4364bee642d84e0fcbd63af39dd3c5f36cf14dae0722b6ada
MD5 bcca3184ca05d82419089ea77b7b5eae
BLAKE2b-256 1324809b7955c332d2c350922a022b47cb4bf7c081b98c90eb9e0d41608d4075

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f9ab05d67f7377f476e64b3c976ec9542948ec0f978f2bd392370ff260e895d9
MD5 66d9dd44ea7cc8d5aaeb69085c144b82
BLAKE2b-256 046c5d148d0287fdd2f95ff96f87f3bf08d6479127458666dcfb3ffff7ffc080

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 925258e3aff9534c58bc79b80403b219c90a560b8cfa7360bc357b53875af8e3
MD5 aef6a03a485795ec8d80df88d6704198
BLAKE2b-256 1ccb9a4f184df830998fb61a55c88102ad679b3effcf503a04cfc844d8d847a1

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp38-cp38-macosx_10_9_universal2.whl
  • Upload date:
  • Size: 6.3 MB
  • Tags: CPython 3.8, macOS 10.9+ universal2 (ARM64, x86-64)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 baaadd9be55dca9e21615371c5819e62f0cec10029f579a9f304324b9de370a9
MD5 f1129b7692013907f9d165d7881931f0
BLAKE2b-256 9264ef7008d00fcf4c4401635c81b9b2704215ba4a0dd6413795f281b60158d8

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 f5e9a4153f1e99ddd19697f9bf09df8f6fb6e28ccbf9d9988d24fd5b2ff03d69
MD5 fea1fba0c483d868fdec9ef4499283de
BLAKE2b-256 a23beae4d5a98bd33c122c95616562b2e8ac6616f2ac19b3f67610710ebcee14

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0c3f41e9b844cad4ee1926f35e9f8d73290710d0e3216363ff3c705c4a2ec4d5
MD5 43f814ce6d8dd304616b8ad20e84358d
BLAKE2b-256 11084ebc8d798142ae5f2da0cbee338fe7e1e15b92f37110ff5741b11ef12b84

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 3db81db1df61803523ea87172decf1327bc9d3c4f9c72891f742fb6c8014a215
MD5 43d79dd0a3ccdfc51e0622c39f4457bd
BLAKE2b-256 6653ceb4c6142e7778c96e5622da97b9bd51a324bcda12ebcb44a6703b7217ca

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 81e6a268b821619be7d68cbd358b5ba02790225964cca20cd34e4cfab91ef30f
MD5 c9e4036c71a9e399409b5ca3f93fa5bc
BLAKE2b-256 f89925a06f6b46acf8a6e38ec1940c0084342519ee9cb7312a9b02577422ab73

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 2.7 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 f59e5fd6e880bffd538a1a14b8344870462723b4162a63ce1dd203dc354ad76e
MD5 9cdd3e688985196f8f324ec52447cb43
BLAKE2b-256 e4a2c47b08b20c9fa7d5ea6c7f6d111d865b02dbb61e2b6d42f47846051edc26

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b00fe7eb0568594bdfd8f449a0bdf4955cf62789d82e3d426926dd703ef76a08
MD5 706e6714e65d27cd9285ec9926a71a67
BLAKE2b-256 5da8862a4a5eda020a7c1cb7153d2d310357f9f1bc5dc1b3e034c677f3b183ec

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pedalboard-0.3.12-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5ed53a618dd8b0c736f7d686989db8c8969cc95fbcdf6182b7810dec49f54c0f
MD5 4895ad9f9a6cce2b09cc2651fab3e908
BLAKE2b-256 d71369608d05e75576d20f4b340cdb437036397a8cc473a8cf86139b6fb4f42d

See more details on using hashes here.

File details

Details for the file pedalboard-0.3.12-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pedalboard-0.3.12-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pedalboard-0.3.12-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 cae961cef132eb2320be5377630ea5f2b4bff765eab1817b85c596a497832d96
MD5 1a042cbfeb57d589ae28ed2c6de9abc5
BLAKE2b-256 e41da5f426f3b4b50e7ecbc17c06e0be238587276e85310ef6c2c578964e699a

See more details on using hashes here.

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