踏板
v0.9.16
pedalboard是一个用于处理音频的 Python 库：读取、写入、渲染、添加效果等。它支持最流行的音频文件格式和许多开箱即用的常见音频效果，还允许使用 VST3® 和 Audio Unit 格式加载第三方软件乐器和效果。
pedalboard由 Spotify 的音频智能实验室构建，可在 Python 和 TensorFlow 中使用工作室品质的音频效果。在 Spotify 内部， pedalboard用于数据增强，以改进机器学习模型并帮助支持 Spotify 的 AI DJ 和 AI 语音翻译等功能。 pedalboard还有助于内容创建过程，无需使用数字音频工作站即可为音频添加效果。
O(1)内存使用量对音频文件和流进行动态重采样AudioStream实现实时音频效果Chorus 、 Distortion 、 Phaser 、 ClippingCompressor 、 Gain 、 LimiterHighpassFilter 、 LadderFilter 、 LowpassFilterConvolution 、 Delay 、 ReverbPitchShiftGSMFullRateCompressor 、 MP3CompressorResample 、 Bitcrushpedalboard.load_plugin )multiprocessing ！tf.data管道中使用！ pedalboard可通过 PyPI 使用（通过 Platform Wheels）：
pip install pedalboard # That's it! No other dependencies required.
如果您是 Python 新手，请按照 INSTALLATION.md 获取可靠指南。
pedalboard使用 Python 3.8、3.9、3.10、3.11、3.12 和 3.13 进行了全面测试。
x86_64 (Intel/AMD) 和aarch64 (ARM/Apple Silicon) 构建的平台manylinux和musllinux轮子amd64 （x86-64、Intel/AMD）的平台轮注意：如果您更愿意观看视频而不是阅读示例或文档，请观看 YouTube 上的使用 Python 中的音频 (feat. Pedalboard) 。
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 )注意：有关如何通过 Pedalboard 插件处理音频的更多信息，包括
reset参数的工作原理，请参阅pedalboard.Plugin.process的文档。
# 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 ) 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 )此示例通过在同一音频上并行运行多个踏板来创建延迟变调效果。 Pedalboard对象本身就是Plugin对象，因此您可以根据需要嵌套它们：
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 ()
])pedalboard支持通过AudioStream对象流式传输实时音频，允许通过在 Python 中添加效果来实时操作音频。
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.tf.data管道中使用 Pedalboard 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 )更多示例请参见：
欢迎对pedalboard做出贡献！有关详细信息，请参阅 CONTRIBUTING.md。
要在学术著作中引用pedalboard ，请使用 Zenodo 上的条目：
通过 BibTeX 引用：
@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}
}pedalboard版权所有 2021-2024 Spotify AB。
pedalboard根据 GNU 通用公共许可证 v3 获得许可。 pedalboard包含许多静态编译的库，并带有以下许可证：
PitchShift插件和time_stretch函数使用 Rubber Band Library，该库在商业许可证和 GPLv2（或更新版本）下获得双重许可。 FFTW 还用于加速 Rubber Band，并根据 GPLv2（或更新版本）获得许可。MP3Compressor插件使用 LAME 项目中的 libmp3lame，该项目在 LGPLv2 下获得许可，并升级到 GPLv3 以包含在该项目中（经 LGPLv2 允许）。GSMFullRateCompressor插件使用 libgsm，它在 ISC 许可证下获得许可并与 GPLv3 兼容。VST 是 Steinberg Media Technologies GmbH 的注册商标。
