Metadata-Version: 2.1
Name: torchaudio-augmentations
Version: 0.1.4
Summary: Audio augmentations library for PyTorch, for audio in the time-domain.
Home-page: https://github.com/spijkervet/torchaudio-augmentations
Author: Janne Spijkervet
Author-email: janne.spijkervet@gmail.com
License: MIT
Description: 
        # Audio Augmentations
        
        Audio augmentations library for PyTorch for audio in the time-domain, with support for stochastic data augmentations as used often in self-supervised / contrastive learning.
        
        
        ## Usage
        We can define several audio augmentations, which will be applied sequentially to a raw audio waveform:
        ```
        from audio_augmentations import *
        
        audio, sr = torchaudio.load("tests/classical.00002.wav")
        
        num_samples = sr * 5
        transforms = [
            RandomResizedCrop(n_samples=num_samples),
            RandomApply([PolarityInversion()], p=0.8),
            RandomApply([Noise(min_snr=0.3, max_snr=0.5)], p=0.3),
            RandomApply([Gain()], p=0.2),
            RandomApply([HighLowPass(sample_rate=sr)], p=0.8),
            RandomApply([Delay(sample_rate=sr)], p=0.5),
            RandomApply([PitchShift(
                n_samples=num_samples,
                sample_rate=sr
            )], p=0.4),
            RandomApply([Reverb(sample_rate=sr)], p=0.3)
        ]
        ```
        
        We can return either one or many versions of the same audio example:
        ```
        transform = Compose(transforms=transforms)
        transformed_audio =  transform(audio)
        >> transformed_audio.shape[0] = 1
        ```
        
        ```
        audio = torchaudio.load("testing/classical.00002.wav")
        transform = ComposeMany(transforms=transforms, num_augmented_samples=4)
        transformed_audio = transform(audio)
        >> transformed_audio.shape[0] = 4
        ```
        
        Similar to the `torchvision.datasets` interface, an instance of the `Compose` or `ComposeMany` class can be supplied to a torchaudio dataloaders that accept `transform=`.
        
        
        ## Optional
        Install WavAugment for reverberation / pitch shifting:
        ```
        pip install git+https://github.com/facebookresearch/WavAugment
        ```
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: fancy feature
