pedalboard

A Python library for adding effects to audio.

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Project description

Pedalboard Logo

pedalboard is a Python library for working with audio: reading, writing, 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 third-party plugins.

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. 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 usage
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® 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 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 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11 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 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)

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(int(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 plugins

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

# Load a VST3 or Audio Unit plugin 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:
with AudioFile('some-file.wav', 'r') as f:
  audio = f.read(f.frames)
  samplerate = f.samplerate
effected = vst(audio, samplerate)

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

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()
])

For more examples, see:

Contributing

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

License

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:

The core audio processing code is pulled from JUCE 6, which is dual-licensed under a commercial license and the GPLv3.
The VST3 SDK, bundled with JUCE, is owned by Steinberg® Media Technologies GmbH and licensed under the GPLv3.
The PitchShift plugin uses the Rubber Band Library, which is dual-licensed under a commercial license and the GPLv2 (or newer).
The MP3Compressor plugin uses libmp3lame from the LAME project, which is licensed under the LGPLv2 and upgraded to the GPLv3 for inclusion in this project (as permitted by the LGPLv2).
The GSMFullRateCompressor plugin uses libgsm, which is licensed under the ISC license and compatible with the GPLv3.

VST is a registered trademark of Steinberg Media Technologies GmbH.

Project details

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0.9.16

Sep 18, 2024

0.9.14

Aug 23, 2024

0.9.13

Aug 21, 2024

0.9.12

Jul 30, 2024

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Jul 11, 2024

0.9.9

Jul 2, 2024

0.9.8

Jun 20, 2024

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Jun 18, 2024

0.9.6

May 19, 2024

0.9.5

May 7, 2024

0.9.4

Apr 22, 2024

0.9.3

Mar 12, 2024

0.9.2

Mar 7, 2024

0.9.1

Feb 29, 2024

0.9.0

Feb 28, 2024

0.8.9

Feb 22, 2024

0.8.7

Dec 14, 2023

0.8.6

Nov 15, 2023

0.8.5

Oct 17, 2023

0.8.4

Oct 16, 2023

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Oct 7, 2023

0.8.2

Oct 4, 2023

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Sep 21, 2023

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Sep 12, 2023

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Sep 5, 2023

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Aug 11, 2023

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Jul 29, 2023

0.7.5

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0.7.4

Jun 15, 2023

0.7.3

Apr 10, 2023

0.7.2

Mar 24, 2023

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Mar 9, 2023

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Feb 15, 2023

This version

0.6.9

Feb 6, 2023

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Jan 18, 2023

0.6.7

Dec 4, 2022

0.6.6

Nov 30, 2022

0.6.5

Nov 28, 2022

0.6.4

Nov 18, 2022

0.6.3

Oct 25, 2022

0.6.2

Sep 23, 2022

0.6.1

Sep 9, 2022

0.6.0

Sep 8, 2022

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Aug 22, 2022

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Algorithm	Hash digest
SHA256	`de0804202009f2c42a359adbf48fcc1f2df00ca888333a0a7e3e36b11c1b177f`
MD5	`8541472f28bfb05216be3744c30b1c4e`
BLAKE2b-256	`36e59767fc3c67a26d495eb0966e3e935ce51bfb4ebd40f9d90bd06f606084b0`

Algorithm	Hash digest
SHA256	`f2270c56e2223d3df35dce474aff5d28cd2085f805a2c0cd49bc3abb93728da5`
MD5	`ee1b84a81aa55aef423eacafa872b0df`
BLAKE2b-256	`7f834a0ad55e8f292d55977aa40124ba4638519d7a5d708c1d2e7bfe343a0cfe`

Algorithm	Hash digest
SHA256	`58559445d9bc598fad76241bb24867a7d50db702b37d48da8dd0a745e3d1d31e`
MD5	`42992c383ec37235724a9dd9eeed7a31`
BLAKE2b-256	`dc0064eacc121a046b7e538eaa0e5a391119eae3f6297e8ddc4b531e4d2f4784`

Algorithm	Hash digest
SHA256	`b4f14bf7e3a33ae360aa895c1a87d0ffc8df17ec93f4660bae15aa4e097fa272`
MD5	`c904007e93ac8a943e65b947b25ac57c`
BLAKE2b-256	`60df4bc34c6b935721e517f77a9284aeb93d5cd9b06162e619ebaec0215c3f52`

Algorithm	Hash digest
SHA256	`e365fd1057008a01d4473d568f543f4e4f7d1d82d498351b240575465966bb2d`
MD5	`1b1a4760f4e8767a4e6a33605ad165e2`
BLAKE2b-256	`bc8f5496e57bf3071096af171f3d497e0561ab1b7e1094415612fa73a8d7cfff`

Algorithm	Hash digest
SHA256	`40b8f05ff52a8f1afd7e0f8ed5d2add9414958055b122e67493101119ff5d772`
MD5	`25f78a7a0f7660a907b14a5ad3ab1c22`
BLAKE2b-256	`62e1718b4acfa0db5e45c951fbd0fc0b40bfc6b961d8b1225613a0ffc920cd89`

Algorithm	Hash digest
SHA256	`9185f2f1e8d5952d413bdc9af0e41d71e978d7f989cf3c51a4552ba1dc6bf975`
MD5	`1d1594ee170f325ed8a9a50479415ba2`
BLAKE2b-256	`99ca6149c973de2223b1ef2284c32b74a6a48f19ac216a129a5decb0157d2ad9`

Algorithm	Hash digest
SHA256	`b84864660134d7f6c03278405a8699d90e82461bdba9a140fe887190685ab2b9`
MD5	`303c4aae5cc7c50d281e6823fe1a37f7`
BLAKE2b-256	`29dbd5151070aa8ebd1480f215a4f375d8afeccf4718b4fb3365b0e04aa44e37`

Algorithm	Hash digest
SHA256	`d14e6812847368cea4374ea12bacbbf1400580dbe92b6fcf2c887574634eab13`
MD5	`0afcbaed88a7eef82eceecca02e5faf2`
BLAKE2b-256	`3e2da4fdd576752ada8ec5b8bcbb29c9751920ca1d61483a547b15fc81486f35`

Algorithm	Hash digest
SHA256	`b616349ce8258a6e88b6bc6ce53b9a90b0c3770aaa64be1d8db0f63b95437335`
MD5	`94eae9096e89249cae19f3bb8cd6314d`
BLAKE2b-256	`e9ff080d150a2736be9f1d14d4e550a37b4fbbcfb08904f0a5c4892338f8feae`

pedalboard 0.6.9

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Making a guitar-style pedalboard

Using VST3® or Audio Unit plugins

Creating parallel effects chains

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Algorithm	Hash digest
SHA256	`c724e13fb74e7997333610876502479ee43915cd6c663095d4855941620af72a`
MD5	`942b70be2e66921b3585f3fca64323ca`
BLAKE2b-256	`c1fa3b49c918e9c7258f5fcdfa3afe1d5dba271af8a65f4a51bca9809f628f5e`

Algorithm	Hash digest
SHA256	`bca130da91e33ba674fa6e7e3bc93660d5e4a72e173bbd3210c67564a776b73c`
MD5	`974e1bb9b21c490ad9f1f348e8e8e385`
BLAKE2b-256	`829f5730da044cae126d7daa1a8e19954e881aad08d6561b00fe510591eec74d`

Algorithm	Hash digest
SHA256	`1e1bf950edac2a7279b0154635cf63551eaf8e32bfecaae293f28c0de03f5189`
MD5	`122b8be7360524c97732d5d9d2f9cf13`
BLAKE2b-256	`c0c05949062b286d87224e8f21ec4151acfb039851e0b5f1ae81e384876c3c72`

Algorithm	Hash digest
SHA256	`2352699d3096bf3993c829e11c409a775397cf89f025d1e945619beea1dd26be`
MD5	`6b6be96fa34bf3647ca245e8f056246b`
BLAKE2b-256	`d89665711166455bba1fbc0dc7e75c9bb247822f8a28c30cc70f3e350509b399`

Algorithm	Hash digest
SHA256	`bff9c7f4d87602ffe5c830b70264315e8fa91369fb13e148fa13f5591562f6b8`
MD5	`1e710dc2e70ab017c2e5ed7a5f10b63d`
BLAKE2b-256	`818e8a57ebb02cf2e50547cc9de0826227f430f383c856cf10f9e26fb5b38b88`

Algorithm	Hash digest
SHA256	`3e41c84d3abbd31fc8bb0ad654be96b67abb40c19329b4bf3c74db7cf8649cba`
MD5	`b8b045877aaebbd1b4e569667d00f24a`
BLAKE2b-256	`df9041a4351eea307d51fa6ba0a8f99b88137ec47c279959c073a1d63ca1f50b`

Algorithm	Hash digest
SHA256	`c1ad0d6517fff4f8357b1388dd5a65a96fe76f1906b373d42e56f55ba5d53d27`
MD5	`f92768481dc9e2faea29318685ffabb4`
BLAKE2b-256	`0d9a35b98a1c6dd6e55d712ddde704dc8dd6be1b042092e9f5d905557721341e`

Algorithm	Hash digest
SHA256	`d2af8e61937f3e737b6be62fb0881c8cb6a4c9759029256cc4a1cca90257caed`
MD5	`c6e4934015d039963f9d5ff868243742`
BLAKE2b-256	`206bebb3c4ffe9e7edd0ca4afd5e0f8d02c1902a2658874363f2a53617aa92f4`

Algorithm	Hash digest
SHA256	`a861d6acd7bee1bdb69538cf3360889852018645c6c929c8c2dac83e87a6e89b`
MD5	`9fb1a339ff25388a8d56afbab4d3d435`
BLAKE2b-256	`57a00c08c82329b1e15b33896e7b83eea512739ee2e0a263696b6bb285930e9d`

Algorithm	Hash digest
SHA256	`abf27ddcdf889ed4621a2ea1a509c977c5dcb88039f34ac2c73219f3a334cf02`
MD5	`26b8cfce8a94f590b255c77f1bb25c62`
BLAKE2b-256	`1bafc23efc277b0d51c7ce29df8cfd2ab985ad9371619911bb34489ff29a8d27`