AudioAugmentor 0.0.4

Python package for simple application of wide range of audio augmentations.

AudioAugmentor

Python library for augmenting audio data

This library is designed to augment audio data for machine learning purposes. It combines several tools and libraries for audio data augmentation and provides unified interface which can be used to apply large set of audio augmentations in one place.

The library is designed to be used with the PyTorch machine learning framework. It can also work solely on just simple audio waveforms and augment those.

This library specifically combines theese libraries and tools:

• torchaudio
• audiomentations
• torch-audiomentations
• pyroomacoustics
• ffmpeg-python

Table below shows which library was used to apply specific audio augmentation/codec.

	audiomentations	torch-audiomentations	torchaudio	pyroomacoustics	ffmpeg
AddBackgroundNoise		✅
AddColoredNoise / AddGaussianSNR		✅
AddGaussianNoise	✅
AddShortNoises	✅
AdjustDuration	✅
AirAbsorption	✅
ApplyImpulseResponse		✅
BandPassFilter		✅
BandStopFilter		✅
ClippingDistortion	✅
Volume / Gain			✅
GainTransition	✅
HighPassFilter		✅
HighShelfFilter	✅
Lambda
Limiter	✅
LoudnessNormalization	✅
LowPassFilter		✅
LowShelfFilter	✅
Mp3Compression	✅
Normalize	✅
Padding	✅
PeakNormalization		✅
PeakingFilter	✅
PitchShift			✅
PolarityInversion		✅
RepeatPart
Resample
Time inversion / Reverse		✅
RoomSimulator				✅
SevenBandParametricEQ	✅
Shift		✅
Speed			✅
SpecChannelShuffle
SpecFrequencyMask			✅
TanhDistortion	✅
TimeMask			✅
TimeStretch
Trim
ac3			✅
adpcm_ima_wav			✅
adpcm_ms			✅
adpcm_yamaha			✅
eac3			✅
flac			✅
libmp3lame			✅
mp2			✅
pcm_alaw			✅
pcm_f32le			✅
pcm_mulaw			✅
pcm_s16le			✅
pcm_s24le			✅
pcm_s32le			✅
pcm_u8			✅
wmav1			✅
wmav2			✅
g726					✅
gsm					✅
amr					✅