Audiomentations

A Python library for audio data augmentation. Inspired by albumentations. Useful for deep learning. Runs on CPU. Supports mono audio and multichannel audio. Can be integrated in training pipelines in e.g. Tensorflow/Keras or Pytorch. Has helped people get world-class results in Kaggle competitions. Is used by companies making next-generation audio products.

Need a Pytorch-specific alternative with GPU support? Check out torch-audiomentations!

Setup

pip install audiomentations

Usage example

from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift, Shift
import numpy as np

augment = Compose([
    AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=0.5),
    TimeStretch(min_rate=0.8, max_rate=1.25, p=0.5),
    PitchShift(min_semitones=-4, max_semitones=4, p=0.5),
    Shift(min_fraction=-0.5, max_fraction=0.5, p=0.5),
])

# Generate 2 seconds of dummy audio for the sake of example
samples = np.random.uniform(low=-0.2, high=0.2, size=(32000,)).astype(np.float32)

# Augment/transform/perturb the audio data
augmented_samples = augment(samples=samples, sample_rate=16000)

Documentation

See https://iver56.github.io/audiomentations/

Transforms

• AddBackgroundNoise
• AddGaussianNoise
• AddGaussianSNR
• AddShortNoises
• AirAbsorption
• ApplyImpulseResponse
• BandPassFilter
• BandStopFilter
• Clip
• ClippingDistortion
• Gain
• GainTransition
• HighPassFilter
• HighShelfFilter
• Lambda
• Limiter
• LoudnessNormalization
• LowPassFilter
• LowShelfFilter
• Mp3Compression
• Normalize
• Padding
• PeakingFilter
• PitchShift
• PolarityInversion
• Resample
• Reverse
• RoomSimulator
• SevenBandParametricEQ
• Shift
• SpecChannelShuffle
• SpecFrequencyMask
• TanhDistortion
• TimeMask
• TimeStretch
• Trim

Changelog

See https://iver56.github.io/audiomentations/changelog/

Acknowledgements

Thanks to Nomono for backing audiomentations.

Thanks to all contributors who help improving audiomentations.