| Name | pedalboard JSON |
| Version |
0.9.16
JSON |
| download |
| home_page | None |
| Summary | A Python library for adding effects to audio. |
| upload_time | 2024-09-18 17:13:36 |
| maintainer | None |
| docs_url | None |
| author | Peter Sobot |
| requires_python | None |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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No coveralls.
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[](https://github.com/spotify/pedalboard/blob/master/LICENSE)
[](https://spotify.github.io/pedalboard)
[](https://pypi.org/project/pedalboard)
[](https://pypi.org/project/pedalboard)
[](https://pypi.org/project/pedalboard)
[](https://pypi.org/project/pedalboard)
[](https://github.com/spotify/pedalboard/actions/workflows/all.yml)
[](https://gist.githubusercontent.com/psobot/8736467e9952991ef44a67915ee7c762/raw/coverage.json)
[](https://pypistats.org/packages/pedalboard)
[](https://doi.org/10.5281/zenodo.7817838)
[](https://github.com/spotify/pedalboard/stargazers)
`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®](https://www.steinberg.net/en/company/technologies/vst3.html) and [Audio Unit](https://en.wikipedia.org/wiki/Audio_Units) formats for loading third-party software instruments and effects.
`pedalboard` was built by [Spotify's Audio Intelligence Lab](https://research.atspotify.com/audio-intelligence/) to enable using studio-quality audio effects from within Python and TensorFlow. Internally at Spotify, `pedalboard` is used for [data augmentation](https://en.wikipedia.org/wiki/Data_augmentation) to improve machine learning models and to help power features like [Spotify's AI DJ](https://newsroom.spotify.com/2023-02-22/spotify-debuts-a-new-ai-dj-right-in-your-pocket/) and [AI Voice Translation](https://newsroom.spotify.com/2023-09-25/ai-voice-translation-pilot-lex-fridman-dax-shepard-steven-bartlett/). `pedalboard` also helps in the process of content creation, making it possible to add effects to audio without using a Digital Audio Workstation.
[](https://spotify.github.io/pedalboard)
## Features
- Built-in audio I/O utilities ([pedalboard.io](https://spotify.github.io/pedalboard/reference/pedalboard.io.html))
- 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 <a href="https://spotify.github.io/pedalboard/reference/pedalboard.io.html#pedalboard.io.AudioStream"><code class="docutils literal"><span class="pre">AudioStream</span></code></a>
- 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 (<a href="https://spotify.github.io/pedalboard/reference/pedalboard.html#pedalboard.load_plugin"><code class="docutils literal"><span class="pre">pedalboard.load_plugin</span></code></a>)
- 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](https://github.com/rabitt/pysox) for single transforms, and 2-5x faster than [SoxBindings](https://github.com/pseeth/soxbindings) (via [iCorv](https://github.com/iCorv/pedalboard_with_tfdata))
- Reads audio files up to **4x** faster than [librosa.load](https://librosa.org/doc/main/generated/librosa.load.html) (in many cases)
- Tested compatibility with TensorFlow - can be used in `tf.data` pipelines!
## Installation
`pedalboard` is available via PyPI (via [Platform Wheels](https://packaging.python.org/guides/distributing-packages-using-setuptools/#platform-wheels)):
```
pip install pedalboard # That's it! No other dependencies required.
```
If you are new to Python, follow [INSTALLATION.md](https://github.com/spotify/pedalboard/blob/master/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, <strong>watch <a href="https://www.youtube.com/watch?v=NYhkqXpFAlg" target="_blank"><em>Working with Audio in Python (feat. Pedalboard)</em> on YouTube</a></strong>.
### Quick start
```python
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 <a href="https://spotify.github.io/pedalboard/reference/pedalboard.html#pedalboard.Plugin.process">
> the documentation for <code class="docutils literal"><span class="pre">pedalboard.Plugin.process</span></code></a>.
### Making a guitar-style pedalboard
```python
# 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
```python
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:
```python
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
<a href="https://spotify.github.io/pedalboard/reference/pedalboard.io.html#pedalboard.io.AudioStream">
an <code class="docutils literal"><span class="pre">AudioStream</span></code> object</a>,
allowing for real-time manipulation of audio by adding effects in Python.
```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
```python
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:
- [the "examples" folder of this repository](https://github.com/spotify/pedalboard/tree/master/examples)
- [the "Pedalboard Demo" Colab notebook](https://colab.research.google.com/drive/1bHjhJj1aCoOlXKl_lOfG99Xs3qWVrhch)
- [_Working with Audio in Python (feat. Pedalboard)_ by Peter Sobot at EuroPython 2022](https://www.youtube.com/watch?v=NYhkqXpFAlg)
- [an interactive web demo on Hugging Face Spaces and Gradio](https://huggingface.co/spaces/akhaliq/pedalboard) (via [@AK391](https://github.com/AK391))
## Contributing
Contributions to `pedalboard` are welcomed! See [CONTRIBUTING.md](https://github.com/spotify/pedalboard/blob/master/CONTRIBUTING.md) for details.
## Citing
To cite `pedalboard` in academic work, use [its entry on Zenodo](https://doi.org/10.5281/zenodo.7817838): [](https://doi.org/10.5281/zenodo.7817838)
To cite via BibTeX:
```tex
@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](https://www.gnu.org/licenses/gpl-3.0.en.html). `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](https://juce.com/), which is [dual-licensed under a commercial license and the GPLv3](https://juce.com/juce-6-licence).
- The [VST3 SDK](https://github.com/steinbergmedia/vst3sdk), bundled with JUCE, is owned by [Steinberg® Media Technologies GmbH](https://www.steinberg.net/en/home.html) and licensed under the GPLv3.
- The `PitchShift` plugin and `time_stretch` functions use [the Rubber Band Library](https://github.com/breakfastquay/rubberband), which is [dual-licensed under a commercial license](https://breakfastquay.com/technology/license.html) and the GPLv2 (or newer). [FFTW](https://www.fftw.org/) is also included to speed up Rubber Band, and [is licensed under the GPLv2 (or newer)](https://www.fftw.org/doc/License-and-Copyright.html).
- The `MP3Compressor` plugin uses [libmp3lame from the LAME project](https://lame.sourceforge.io/), which is [licensed under the LGPLv2](https://github.com/lameproject/lame/blob/master/README) and [upgraded to the GPLv3 for inclusion in this project (as permitted by the LGPLv2)](https://www.gnu.org/licenses/gpl-faq.html#AllCompatibility).
- The `GSMFullRateCompressor` plugin uses [libgsm](http://quut.com/gsm/), which is [licensed under the ISC license](https://github.com/timothytylee/libgsm/blob/master/COPYRIGHT) and [compatible with the GPLv3](https://www.gnu.org/licenses/license-list.en.html#ISC).
_VST is a registered trademark of Steinberg Media Technologies GmbH._
Raw data
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"name": "pedalboard",
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"author_email": "psobot@spotify.com",
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"description": "\n\n\n[](https://github.com/spotify/pedalboard/blob/master/LICENSE)\n[](https://spotify.github.io/pedalboard)\n[](https://pypi.org/project/pedalboard)\n[](https://pypi.org/project/pedalboard)\n[](https://pypi.org/project/pedalboard)\n[](https://pypi.org/project/pedalboard)\n[](https://github.com/spotify/pedalboard/actions/workflows/all.yml)\n[](https://gist.githubusercontent.com/psobot/8736467e9952991ef44a67915ee7c762/raw/coverage.json)\n[](https://pypistats.org/packages/pedalboard)\n[](https://doi.org/10.5281/zenodo.7817838)\n[](https://github.com/spotify/pedalboard/stargazers)\n\n\n`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\u00ae](https://www.steinberg.net/en/company/technologies/vst3.html) and [Audio Unit](https://en.wikipedia.org/wiki/Audio_Units) formats for loading third-party software instruments and effects.\n\n`pedalboard` was built by [Spotify's Audio Intelligence Lab](https://research.atspotify.com/audio-intelligence/) to enable using studio-quality audio effects from within Python and TensorFlow. Internally at Spotify, `pedalboard` is used for [data augmentation](https://en.wikipedia.org/wiki/Data_augmentation) to improve machine learning models and to help power features like [Spotify's AI DJ](https://newsroom.spotify.com/2023-02-22/spotify-debuts-a-new-ai-dj-right-in-your-pocket/) and [AI Voice Translation](https://newsroom.spotify.com/2023-09-25/ai-voice-translation-pilot-lex-fridman-dax-shepard-steven-bartlett/). `pedalboard` also helps in the process of content creation, making it possible to add effects to audio without using a Digital Audio Workstation.\n\n[](https://spotify.github.io/pedalboard)\n\n## Features \n\n - Built-in audio I/O utilities ([pedalboard.io](https://spotify.github.io/pedalboard/reference/pedalboard.io.html))\n - Support for reading and writing AIFF, FLAC, MP3, OGG, and WAV files on all platforms with no dependencies\n - Additional support for reading AAC, AC3, WMA, and other formats depending on platform\n - Support for on-the-fly resampling of audio files and streams with `O(1)` memory usage\n - Live audio effects via <a href=\"https://spotify.github.io/pedalboard/reference/pedalboard.io.html#pedalboard.io.AudioStream\"><code class=\"docutils literal\"><span class=\"pre\">AudioStream</span></code></a>\n - Built-in support for a number of basic audio transformations, including:\n - Guitar-style effects: `Chorus`, `Distortion`, `Phaser`, `Clipping`\n - Loudness and dynamic range effects: `Compressor`, `Gain`, `Limiter`\n - Equalizers and filters: `HighpassFilter`, `LadderFilter`, `LowpassFilter`\n - Spatial effects: `Convolution`, `Delay`, `Reverb`\n - Pitch effects: `PitchShift`\n - Lossy compression: `GSMFullRateCompressor`, `MP3Compressor`\n - Quality reduction: `Resample`, `Bitcrush`\n - Supports VST3\u00ae instrument and effect plugins on macOS, Windows, and Linux (<a href=\"https://spotify.github.io/pedalboard/reference/pedalboard.html#pedalboard.load_plugin\"><code class=\"docutils literal\"><span class=\"pre\">pedalboard.load_plugin</span></code></a>)\n - Supports instrument and effect Audio Units on macOS\n - Strong thread-safety, memory usage, and speed guarantees\n - Releases Python's Global Interpreter Lock (GIL) to allow use of multiple CPU cores\n - No need to use `multiprocessing`!\n - Even when only using one thread:\n - Processes audio up to **300x** faster than [pySoX](https://github.com/rabitt/pysox) for single transforms, and 2-5x faster than [SoxBindings](https://github.com/pseeth/soxbindings) (via [iCorv](https://github.com/iCorv/pedalboard_with_tfdata))\n - Reads audio files up to **4x** faster than [librosa.load](https://librosa.org/doc/main/generated/librosa.load.html) (in many cases)\n - Tested compatibility with TensorFlow - can be used in `tf.data` pipelines!\n\n## Installation\n\n`pedalboard` is available via PyPI (via [Platform Wheels](https://packaging.python.org/guides/distributing-packages-using-setuptools/#platform-wheels)):\n```\npip install pedalboard # That's it! No other dependencies required.\n```\n\nIf you are new to Python, follow [INSTALLATION.md](https://github.com/spotify/pedalboard/blob/master/INSTALLATION.md) for a robust guide.\n\n### Compatibility\n\n`pedalboard` is thoroughly tested with Python 3.8, 3.9, 3.10, 3.11, 3.12, and 3.13.\n\n- Linux\n - Tested heavily in production use cases at Spotify\n - Tested automatically on GitHub with VSTs\n - Platform `manylinux` and `musllinux` wheels built for `x86_64` (Intel/AMD) and `aarch64` (ARM/Apple Silicon)\n - Most Linux VSTs require a relatively modern Linux installation (with glibc > 2.27)\n- macOS\n - Tested manually with VSTs and Audio Units\n - Tested automatically on GitHub with VSTs\n - Platform wheels available for both Intel and Apple Silicon\n - Compatible with a wide range of VSTs and Audio Units\n- Windows\n - Tested automatically on GitHub with VSTs\n - Platform wheels available for `amd64` (x86-64, Intel/AMD)\n\n## Examples\n\n> **Note**: If you'd rather watch a video instead of reading examples or documentation, <strong>watch <a href=\"https://www.youtube.com/watch?v=NYhkqXpFAlg\" target=\"_blank\"><em>Working with Audio in Python (feat. Pedalboard)</em> on YouTube</a></strong>.\n\n### Quick start\n\n```python\nfrom pedalboard import Pedalboard, Chorus, Reverb\nfrom pedalboard.io import AudioFile\n\n# Make a Pedalboard object, containing multiple audio plugins:\nboard = Pedalboard([Chorus(), Reverb(room_size=0.25)])\n\n# Open an audio file for reading, just like a regular file:\nwith AudioFile('some-file.wav') as f:\n \n # Open an audio file to write to:\n with AudioFile('output.wav', 'w', f.samplerate, f.num_channels) as o:\n \n # Read one second of audio at a time, until the file is empty:\n while f.tell() < f.frames:\n chunk = f.read(f.samplerate)\n \n # Run the audio through our pedalboard:\n effected = board(chunk, f.samplerate, reset=False)\n \n # Write the output to our output file:\n o.write(effected)\n```\n\n> **Note**: For more information about how to process audio through\n> Pedalboard plugins, including how the `reset` parameter works,\n> see <a href=\"https://spotify.github.io/pedalboard/reference/pedalboard.html#pedalboard.Plugin.process\">\n> the documentation for <code class=\"docutils literal\"><span class=\"pre\">pedalboard.Plugin.process</span></code></a>.\n\n### Making a guitar-style pedalboard\n\n```python\n# Don't do import *! (It just makes this example smaller)\nfrom pedalboard import *\nfrom pedalboard.io import AudioFile\n\n# Read in a whole file, resampling to our desired sample rate:\nsamplerate = 44100.0\nwith AudioFile('guitar-input.wav').resampled_to(samplerate) as f:\n audio = f.read(f.frames)\n\n# Make a pretty interesting sounding guitar pedalboard:\nboard = Pedalboard([\n Compressor(threshold_db=-50, ratio=25),\n Gain(gain_db=30),\n Chorus(),\n LadderFilter(mode=LadderFilter.Mode.HPF12, cutoff_hz=900),\n Phaser(),\n Convolution(\"./guitar_amp.wav\", 1.0),\n Reverb(room_size=0.25),\n])\n\n# Pedalboard objects behave like lists, so you can add plugins:\nboard.append(Compressor(threshold_db=-25, ratio=10))\nboard.append(Gain(gain_db=10))\nboard.append(Limiter())\n\n# ... or change parameters easily:\nboard[0].threshold_db = -40\n\n# Run the audio through this pedalboard!\neffected = board(audio, samplerate)\n\n# Write the audio back as a wav file:\nwith AudioFile('processed-output.wav', 'w', samplerate, effected.shape[0]) as f:\n f.write(effected)\n```\n\n### Using VST3\u00ae or Audio Unit instrument and effect plugins\n\n```python\nfrom pedalboard import Pedalboard, Reverb, load_plugin\nfrom pedalboard.io import AudioFile\nfrom mido import Message # not part of Pedalboard, but convenient!\n\n# Load a VST3 or Audio Unit plugin from a known path on disk:\ninstrument = load_plugin(\"./VSTs/Magical8BitPlug2.vst3\")\neffect = load_plugin(\"./VSTs/RoughRider3.vst3\")\n\nprint(effect.parameters.keys())\n# dict_keys([\n# 'sc_hpf_hz', 'input_lvl_db', 'sensitivity_db',\n# 'ratio', 'attack_ms', 'release_ms', 'makeup_db',\n# 'mix', 'output_lvl_db', 'sc_active',\n# 'full_bandwidth', 'bypass', 'program',\n# ])\n\n# Set the \"ratio\" parameter to 15\neffect.ratio = 15\n\n# Render some audio by passing MIDI to an instrument:\nsample_rate = 44100\naudio = instrument(\n [Message(\"note_on\", note=60), Message(\"note_off\", note=60, time=5)],\n duration=5, # seconds\n sample_rate=sample_rate,\n)\n\n# Apply effects to this audio:\neffected = effect(audio, sample_rate)\n\n# ...or put the effect into a chain with other plugins:\nboard = Pedalboard([effect, Reverb()])\n# ...and run that pedalboard with the same VST instance!\neffected = board(audio, sample_rate)\n```\n\n### Creating parallel effects chains\n\nThis example creates a delayed pitch-shift effect by running\nmultiple Pedalboards in parallel on the same audio. `Pedalboard`\nobjects are themselves `Plugin` objects, so you can nest them\nas much as you like:\n\n```python\nfrom pedalboard import Pedalboard, Compressor, Delay, Distortion, Gain, PitchShift, Reverb, Mix\n\npassthrough = Gain(gain_db=0)\n\ndelay_and_pitch_shift = Pedalboard([\n Delay(delay_seconds=0.25, mix=1.0),\n PitchShift(semitones=7),\n Gain(gain_db=-3),\n])\n\ndelay_longer_and_more_pitch_shift = Pedalboard([\n Delay(delay_seconds=0.5, mix=1.0),\n PitchShift(semitones=12),\n Gain(gain_db=-6),\n])\n\nboard = Pedalboard([\n # Put a compressor at the front of the chain:\n Compressor(),\n # Run all of these pedalboards simultaneously with the Mix plugin:\n Mix([\n passthrough,\n delay_and_pitch_shift,\n delay_longer_and_more_pitch_shift,\n ]),\n # Add a reverb on the final mix:\n Reverb()\n])\n```\n\n### Running Pedalboard on Live Audio\n\n`pedalboard` supports streaming live audio through\n<a href=\"https://spotify.github.io/pedalboard/reference/pedalboard.io.html#pedalboard.io.AudioStream\">\nan <code class=\"docutils literal\"><span class=\"pre\">AudioStream</span></code> object</a>,\nallowing for real-time manipulation of audio by adding effects in Python.\n\n```python\nfrom pedalboard import Pedalboard, Chorus, Compressor, Delay, Gain, Reverb, Phaser\nfrom pedalboard.io import AudioStream\n\n# Open up an audio stream:\nwith AudioStream(\n input_device_name=\"Apogee Jam+\", # Guitar interface\n output_device_name=\"MacBook Pro Speakers\"\n) as stream:\n # Audio is now streaming through this pedalboard and out of your speakers!\n stream.plugins = Pedalboard([\n Compressor(threshold_db=-50, ratio=25),\n Gain(gain_db=30),\n Chorus(),\n Phaser(),\n Convolution(\"./guitar_amp.wav\", 1.0),\n Reverb(room_size=0.25),\n ])\n input(\"Press enter to stop streaming...\")\n\n# The live AudioStream is now closed, and audio has stopped.\n```\n\n### Using Pedalboard in `tf.data` Pipelines\n\n```python\nimport tensorflow as tf \n\nsr = 48000 \n\n# Put whatever plugins you like in here:\nplugins = pedalboard.Pedalboard([pedalboard.Gain(), pedalboard.Reverb()]) \n\n# Make a dataset containing random noise:\n# NOTE: for real training, here's where you'd want to load your audio somehow:\nds = tf.data.Dataset.from_tensor_slices([np.random.rand(sr)])\n\n# Apply our Pedalboard instance to the tf.data Pipeline:\nds = ds.map(lambda audio: tf.numpy_function(plugins.process, [audio, sr], tf.float32)) \n\n# Create and train a (dummy) ML model on this audio:\nmodel = tf.keras.models.Sequential([tf.keras.layers.InputLayer(input_shape=(sr,)), tf.keras.layers.Dense(1)])\nmodel.compile(loss=\"mse\") \nmodel.fit(ds.map(lambda effected: (effected, 1)).batch(1), epochs=10)\n```\n\nFor more examples, see:\n - [the \"examples\" folder of this repository](https://github.com/spotify/pedalboard/tree/master/examples)\n - [the \"Pedalboard Demo\" Colab notebook](https://colab.research.google.com/drive/1bHjhJj1aCoOlXKl_lOfG99Xs3qWVrhch)\n - [_Working with Audio in Python (feat. Pedalboard)_ by Peter Sobot at EuroPython 2022](https://www.youtube.com/watch?v=NYhkqXpFAlg)\n - [an interactive web demo on Hugging Face Spaces and Gradio](https://huggingface.co/spaces/akhaliq/pedalboard) (via [@AK391](https://github.com/AK391)) \n\n## Contributing\n\nContributions to `pedalboard` are welcomed! See [CONTRIBUTING.md](https://github.com/spotify/pedalboard/blob/master/CONTRIBUTING.md) for details.\n\n## Citing\n\nTo cite `pedalboard` in academic work, use [its entry on Zenodo](https://doi.org/10.5281/zenodo.7817838): [](https://doi.org/10.5281/zenodo.7817838)\n\nTo cite via BibTeX:\n\n```tex\n@software{sobot_peter_2023_7817838,\n author = {Sobot, Peter},\n title = {Pedalboard},\n month = jul,\n year = 2021,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.7817838},\n url = {https://doi.org/10.5281/zenodo.7817838}\n}\n```\n\n## License\n`pedalboard` is Copyright 2021-2024 Spotify AB.\n\n`pedalboard` is licensed under the [GNU General Public License v3](https://www.gnu.org/licenses/gpl-3.0.en.html). `pedalboard` includes a number of libraries that are statically compiled, and which carry the following licenses:\n\n - The core audio processing code is pulled from [JUCE 6](https://juce.com/), which is [dual-licensed under a commercial license and the GPLv3](https://juce.com/juce-6-licence).\n - The [VST3 SDK](https://github.com/steinbergmedia/vst3sdk), bundled with JUCE, is owned by [Steinberg\u00ae Media Technologies GmbH](https://www.steinberg.net/en/home.html) and licensed under the GPLv3.\n - The `PitchShift` plugin and `time_stretch` functions use [the Rubber Band Library](https://github.com/breakfastquay/rubberband), which is [dual-licensed under a commercial license](https://breakfastquay.com/technology/license.html) and the GPLv2 (or newer). [FFTW](https://www.fftw.org/) is also included to speed up Rubber Band, and [is licensed under the GPLv2 (or newer)](https://www.fftw.org/doc/License-and-Copyright.html).\n - The `MP3Compressor` plugin uses [libmp3lame from the LAME project](https://lame.sourceforge.io/), which is [licensed under the LGPLv2](https://github.com/lameproject/lame/blob/master/README) and [upgraded to the GPLv3 for inclusion in this project (as permitted by the LGPLv2)](https://www.gnu.org/licenses/gpl-faq.html#AllCompatibility).\n - The `GSMFullRateCompressor` plugin uses [libgsm](http://quut.com/gsm/), which is [licensed under the ISC license](https://github.com/timothytylee/libgsm/blob/master/COPYRIGHT) and [compatible with the GPLv3](https://www.gnu.org/licenses/license-list.en.html#ISC).\n\n_VST is a registered trademark of Steinberg Media Technologies GmbH._\n",
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