pedalboard

A Python library for adding effects to audio.

These details have not been verified by PyPI

Project description

Pedalboard Logo

PyPI - Python Version Supported Platforms Apple Silicon support for macOS and Linux (Docker) PyPI - Wheel 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:

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
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 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).

VST is a registered trademark of Steinberg Media Technologies GmbH.

Project details

These details have not been verified by PyPI

Release history Release notifications | RSS feed

0.9.16

Sep 18, 2024

0.9.14

Aug 23, 2024

0.9.13

Aug 21, 2024

0.9.12

Jul 30, 2024

0.9.10

Jul 11, 2024

0.9.9

Jul 2, 2024

0.9.8

Jun 20, 2024

0.9.7

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

0.8.3

Oct 7, 2023

0.8.2

Oct 4, 2023

0.8.1

Sep 21, 2023

0.8.0

Sep 12, 2023

0.7.9

Sep 5, 2023

0.7.8

Aug 11, 2023

0.7.7

Aug 1, 2023

0.7.6

Jul 29, 2023

0.7.5

Jul 17, 2023

0.7.4

Jun 15, 2023

0.7.3

Apr 10, 2023

0.7.2

Mar 24, 2023

0.7.1

Mar 9, 2023

0.7.0

Feb 15, 2023

0.6.9

Feb 6, 2023

0.6.8

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

0.5.10

Aug 22, 2022

0.5.9

Aug 12, 2022

0.5.8

Jul 28, 2022

0.5.7

Jul 27, 2022

0.5.5

Jul 13, 2022

0.5.4

Apr 25, 2022

0.5.3

Mar 28, 2022

0.5.2

Mar 24, 2022

0.5.1

Mar 16, 2022

0.4.3

Mar 1, 2022

0.4.2

Feb 22, 2022

0.4.1

Feb 10, 2022

0.4.0

Feb 9, 2022

This version

0.3.14

Jan 31, 2022

0.3.12

Jan 25, 2022

0.3.11

Jan 19, 2022

0.3.10

Jan 3, 2022

0.3.9

Jan 1, 2022

0.3.8

Oct 5, 2021

0.3.7

Oct 1, 2021

0.3.6

Sep 13, 2021

0.3.5

Sep 7, 2021

0.3.4

Aug 2, 2021

0.3.3

Jul 8, 2021

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-0.3.14-pp38-pypy38_pp73-win_amd64.whl (2.6 MB view details)

Uploaded Jan 31, 2022 PyPy Windows x86-64

pedalboard-0.3.14-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded Jan 31, 2022 PyPy manylinux: glibc 2.17+ x86-64

pedalboard-0.3.14-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.5 MB view details)

Uploaded Jan 31, 2022 PyPy manylinux: glibc 2.17+ ARM64

pedalboard-0.3.14-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (3.5 MB view details)

Uploaded Jan 31, 2022 PyPy macOS 10.9+ x86-64

pedalboard-0.3.14-pp37-pypy37_pp73-win_amd64.whl (2.6 MB view details)

Uploaded Jan 31, 2022 PyPy Windows x86-64

pedalboard-0.3.14-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded Jan 31, 2022 PyPy manylinux: glibc 2.17+ x86-64

pedalboard-0.3.14-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.5 MB view details)

Uploaded Jan 31, 2022 PyPy manylinux: glibc 2.17+ ARM64

pedalboard-0.3.14-pp37-pypy37_pp73-macosx_10_9_x86_64.whl (3.5 MB view details)

Uploaded Jan 31, 2022 PyPy macOS 10.9+ x86-64

pedalboard-0.3.14-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.10 Windows x86-64

pedalboard-0.3.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded Jan 31, 2022 CPython 3.10 manylinux: glibc 2.17+ x86-64

pedalboard-0.3.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.5 MB view details)

Uploaded Jan 31, 2022 CPython 3.10 manylinux: glibc 2.17+ ARM64

pedalboard-0.3.14-cp310-cp310-macosx_10_9_universal2.whl (6.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

pedalboard-0.3.14-cp39-cp39-win_amd64.whl (2.7 MB view details)

Uploaded Jan 31, 2022 CPython 3.9 Windows x86-64

pedalboard-0.3.14-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded Jan 31, 2022 CPython 3.9 manylinux: glibc 2.17+ x86-64

pedalboard-0.3.14-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.5 MB view details)

Uploaded Jan 31, 2022 CPython 3.9 manylinux: glibc 2.17+ ARM64

pedalboard-0.3.14-cp39-cp39-macosx_10_9_universal2.whl (6.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

pedalboard-0.3.14-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.8 Windows x86-64

pedalboard-0.3.14-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded Jan 31, 2022 CPython 3.8 manylinux: glibc 2.17+ x86-64

pedalboard-0.3.14-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.5 MB view details)

Uploaded Jan 31, 2022 CPython 3.8 manylinux: glibc 2.17+ ARM64

pedalboard-0.3.14-cp38-cp38-macosx_10_9_universal2.whl (6.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

pedalboard-0.3.14-cp37-cp37m-win_amd64.whl (2.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.7m Windows x86-64

pedalboard-0.3.14-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded Jan 31, 2022 CPython 3.7m manylinux: glibc 2.17+ x86-64

pedalboard-0.3.14-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.7m manylinux: glibc 2.17+ ARM64

pedalboard-0.3.14-cp37-cp37m-macosx_10_9_x86_64.whl (3.5 MB view details)

Uploaded Jan 31, 2022 CPython 3.7m macOS 10.9+ x86-64

pedalboard-0.3.14-cp36-cp36m-win_amd64.whl (2.9 MB view details)

Uploaded Jan 31, 2022 CPython 3.6m Windows x86-64

pedalboard-0.3.14-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded Jan 31, 2022 CPython 3.6m manylinux: glibc 2.17+ x86-64

pedalboard-0.3.14-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.6 MB view details)

Uploaded Jan 31, 2022 CPython 3.6m manylinux: glibc 2.17+ ARM64

pedalboard-0.3.14-cp36-cp36m-macosx_10_9_x86_64.whl (3.5 MB view details)

Uploaded Jan 31, 2022 CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file pedalboard-0.3.14-pp38-pypy38_pp73-win_amd64.whl.

File metadata

Download URL: pedalboard-0.3.14-pp38-pypy38_pp73-win_amd64.whl
Upload date: Jan 31, 2022
Size: 2.6 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-0.3.14-pp38-pypy38_pp73-win_amd64.whl
Algorithm	Hash digest
SHA256	`b82c97bf9336568ca20541f68848230f1a6e7f97b94339c4688c70ef36ceb2ab`
MD5	`9ee87e8d67d373d0f81c85ea812543b3`
BLAKE2b-256	`685ce0b8110e19db5f71edaa03791486430071fc3d47f4bab189f48a6fa55c2e`

Algorithm	Hash digest
SHA256	`68f24ec42d209587adb1c8352767c421f9cd79d735bca206a1b57659542e1f14`
MD5	`b08e8855699811daed992f4658fa1685`
BLAKE2b-256	`a3cf54001a03e3dee11e52e6747925b66020ca94a8c6b37a568e743e0742bed9`

Algorithm	Hash digest
SHA256	`93d01655841ef8af62d8cf70bb5cbfed293ca70c8afc08594dcf7e4163fe8834`
MD5	`7fc7b4f4ff800c77ebab622c1585ffcf`
BLAKE2b-256	`e0f2ea86ae4e94b85b77763e23abfbb93de8d4c2de55834c017221c7ab1d0838`

Algorithm	Hash digest
SHA256	`a7a1fbadf3223a2ff5358d7e25d586252f86da700f243bf6dd1b622a5ad74f86`
MD5	`c2cf1031afed003c90084d89f09333fb`
BLAKE2b-256	`6fbe0c0210c9254a0458f235fedd6107cc1f97f775cc23e091775363995d97be`

Algorithm	Hash digest
SHA256	`67219914807ef3b517859483fb2486b1500decfb8faf34d585d372f8bdb68ee3`
MD5	`93df23367e85b3bd9b51904fe192bbf0`
BLAKE2b-256	`7e0da58156e86d04c0ac8a55cbcf5dd2fea057ac86683f44f27dda7659561e75`

Algorithm	Hash digest
SHA256	`9ee368a3cacb0e785dce73e97fbc3caf82da2399cbd3775b67ac819434c85eba`
MD5	`5d3d113326841777554fd07d1b3b9c3c`
BLAKE2b-256	`0dae3c353db28841c17d73604d41433f770550c7a896f88525c20629d88df822`

Algorithm	Hash digest
SHA256	`30459ab34035fdbfe0529c7ff3690f0f298474330e6dc7ab20c2306a807a02e5`
MD5	`b6723dbda150ba9560f9f09978eaef12`
BLAKE2b-256	`5719cd5789e99ce9392ed056612c40c776183d98fcea04af86ef48af654e4e37`

Algorithm	Hash digest
SHA256	`af6c4028e2c7c00ae5346633534028a564f9a5a0f8867847b0b3a2c681639d98`
MD5	`313cf8535c419f4a607c29ed5e8f845c`
BLAKE2b-256	`8e5cb81648775b77cce7b88d10d5d63c6f0d722f5d667e8d74f37151a22d0e85`

Algorithm	Hash digest
SHA256	`476dc3872d48818e81d87116523231b8ef4d767bf0493a86d37e4f7cd39e886f`
MD5	`1e4c5e59e33244f51a2c3c2d51c1c284`
BLAKE2b-256	`6a1c041742f1f0eec8b7b76e437533054ef8c57db06efac364325d17b59801b1`

Algorithm	Hash digest
SHA256	`d11b59aaf66809cc794df169d70ccce1b052a0d11079fbc06a08925886e85b46`
MD5	`a98464e45aaded87e43291e593b27dc2`
BLAKE2b-256	`42fb9e932984052d9d9bcebd97256e96861e3a26c252eb057c2de821630b0b13`

Algorithm	Hash digest
SHA256	`fc5c8176031615f85d593a2335c4cab4de71ea8429420d1055a1b400bebadf86`
MD5	`819f4729d4a4b564fbbaba482e6d018e`
BLAKE2b-256	`716683f84538992f84683c6e60c118b761bab0a3580c2849f8d9bc3fa7d95ec9`

Algorithm	Hash digest
SHA256	`0ba831168d969df44cd8339adcd84db9bf73c98ff1aade80bf824c9200707f6c`
MD5	`8be47768944755821ac3b52ff097d782`
BLAKE2b-256	`b991584df2b471f56a0a4996648b860a27949b3eb5e2f175aab9ae684da1d456`

Algorithm	Hash digest
SHA256	`067864d6ce1dcd49c67c29f14e54e38a9a09b3f85dcd7fa446973545847d20b3`
MD5	`ff1a3f01b5fd9d708b03f51050e29409`
BLAKE2b-256	`e2dc529514cb9201de4bf9063cc80c615e3c531dc38da9b530699a3f6cddeb69`

Algorithm	Hash digest
SHA256	`b205f556d5b12921b5b8f094e2eeaf455c696371bab8ccf7370772ead9158d41`
MD5	`a2cdc4c5115a6444088bb9a4b772956e`
BLAKE2b-256	`b5207375af894227720c3c95537003e2cbeca0f3ba1a6154e7b88364c1882f9d`

Algorithm	Hash digest
SHA256	`e16456a9280cca8780a5cac9b3237c1240f9f90ef0ef6339533f902e77332b7b`
MD5	`74f6a8c94f4777bba6a6de5986d75b75`
BLAKE2b-256	`cba6cf33944491323b4ed8719508fdd1d77c1d8a4f8dccc855cbf62c4f6fb0fe`

Algorithm	Hash digest
SHA256	`cf3800f7c47a47f5ffc6dea4c175b40539d20dc21d791dca46d2159e304a9153`
MD5	`501d09718b90f1c628e2c846a4050eb9`
BLAKE2b-256	`04ae1237f7d0c6aa1aaae7e8db67ffc02e2f80f6d45cb8d6416d7e825fd4506e`

Algorithm	Hash digest
SHA256	`d41125b4a80acb6f8ac57bc7c586598a48c3c5ff2218f6481405646da01be40d`
MD5	`8b43ba6f6aab90eb29be3a8302b79799`
BLAKE2b-256	`a226f6d6eee3b04ae4a72a21eb951eaa9cc2a336b14976ae6882694ce774bdba`

Algorithm	Hash digest
SHA256	`7dd611072ace64ef186b8363aa88964285adfee25a013b884bcd62125552ae40`
MD5	`4d8f973a23e3f395c37665cee28206b5`
BLAKE2b-256	`13b562c1d691eac6377bba3752427f8c6f095cf6e17cf9148057c9b8937295e7`

Algorithm	Hash digest
SHA256	`2d2f2e18f3835f8a4f19283efb2da100137594d4f78658a922009890b3e20941`
MD5	`9f590af1161852a543bfb40c4c8b2ae1`
BLAKE2b-256	`ca00a4c7e8f1c5f980d0bded7a83b8d6f17d9241476be16b3c47444f735bf4f9`

Algorithm	Hash digest
SHA256	`1d5e52a121ae1d12caeb011fcc8d80cfd44381ba6efd8817e0e9a730f2cb2308`
MD5	`a0cfbefd581dc4cc3128ee63248ab5d1`
BLAKE2b-256	`295a0a8a5446c37a40be512de27c02c8bf52caf6050d13330dad68ac07f0c0c9`

Algorithm	Hash digest
SHA256	`932f2a71dcb27c9ad9b414b0e5b27b26954bce6e7e9549c95a53f563c23325e4`
MD5	`8acf0a92509c9a2678401a24edf20c0e`
BLAKE2b-256	`263465cc16b7392191d09c6b1a0ce9ef72d7054448012c6ec719e8ab598f5854`

pedalboard 0.3.14

Navigation

Verified details

Maintainers

Unverified details

Meta

Classifiers

Project description

Features

Installation

Compatibility

Plugin Compatibility

Examples

Loading a VST3® plugin and manipulating its parameters

Contributing

Frequently Asked Questions

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

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

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

Can Pedalboard plugins accept MIDI?

License

Project details

Verified details

Maintainers

Unverified details

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

Algorithm	Hash digest
SHA256	`f606818b53e8cb17c0e3048997c6cdb34eac8dfacbd0e6ad00f8672c512d3056`
MD5	`af54f63415a5b8b4f7bd73ae3434f48b`
BLAKE2b-256	`beed93f3bc4e10493eaacae7803078109fdc18c6e7eef98ccf639ef3db86cd2f`

Algorithm	Hash digest
SHA256	`0c3f647405bdafc9efcb8d7af30aab34e37cac577f4c3f3c2b03c2cb9d86d7c3`
MD5	`589a2ad674acd3449e62065457b7a4fe`
BLAKE2b-256	`0865449f3c83e29488b25fbc6dcb6dd374dcd6dbcd61dce1f5f8e81b38506a45`

Algorithm	Hash digest
SHA256	`3badb03403bbdc23d05e0103eb94e33b60e18f7806d7e28c8669591d7588726a`
MD5	`87bde16f35362a376258f57c4dc068b7`
BLAKE2b-256	`37ba87513909f8ba8ceb081a69d9b359fa5488e951a4700f0ae950bfad096480`

Algorithm	Hash digest
SHA256	`4c85d1f4adfd186f06e6d40f38d8e228e5721b29c16d71d08aeaaac71d9a0df3`
MD5	`7ea8cb0d1e945567a898d48ed9dfbc59`
BLAKE2b-256	`3ca5366b5a4f00160d7ceef29bb36a6c1bf82a508194ac8f422fb58bd916f70b`

Algorithm	Hash digest
SHA256	`4dcc6badf8887a3a1139a7b3776d20945c39e5fd948720ba22a449ac6c18aad5`
MD5	`e922914360531e04c4a398a689b62350`
BLAKE2b-256	`aadc1880132567c77f71b782ce6c79c5d94508f74f790afa5c6079a89307e589`

Algorithm	Hash digest
SHA256	`fec898368bb82552e0d8ae096673765b0e96149ef49c1ed48fbfeb056d2ddea0`
MD5	`dfe5c546dddcc448db385ca5f28a3cd7`
BLAKE2b-256	`c63f89de490fe413971bc62139faf4129dc0539d41f2b6bb992d3aa5d3b6e9ca`

Algorithm	Hash digest
SHA256	`2e96b9a9218c83a16213f01ab809b409a7fc454ccc430c1418d7e997f0ca94dd`
MD5	`c67054f7857f28d6721abd8f882fac97`
BLAKE2b-256	`b56c7b2f9a389fc2d9131e0b0ba416ec16c234272f8de43a83b8e24c53f40cd5`