Deep learning with PyTorch and audio
Project description
Deep learning with PyTorch and audio.
audtorch collects audio related data sets, transforms and other useful additions that are useful.
If you are interested in PyTorch and audio you should also check out the efforts to integrate more audio directly into PyTorch:
Installation
audtorch is supported by Python 3.5 or higher. To install it run (preferable in a virtual environment):
pip install audtorchUsage
audtorch automates the data iteration process for deep neural network training using PyTorch. It provides a set of feature extraction transforms that can be implemented on-the-fly on the CPU.
The following example creates a data set of speech samples that are cut to a fixed length of 10240 samples. In addition they are augmented on the fly during data loading by a transform that adds samples from another data set:
>>> import sounddevice as sd
>>> from audtorch import datasets, transforms
>>> noise = datasets.WhiteNoise(duration=10240, sampling_rate=16000)
>>> augment = transforms.Compose([transforms.RandomCrop(10240),
... transforms.RandomAdditiveMix(noise)])
>>> data = datasets.LibriSpeech(root='~/LibriSpeech', sets='dev-clean',
... download=True, transform=augment)
>>> signal, label = data[8]
>>> sd.play(signal.transpose(), data.sampling_rate)Besides data sets and transforms the package provides standard evaluation metrics, samplers, and necessary collate functions for training deep neural networks for audio tasks.
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