Welcome to the TorchCodec documentation!#
TorchCodec is a Python library for decoding video and audio data into PyTorch tensors, on CPU and CUDA GPU. It also supports audio and video encoding! It aims to be fast, easy to use, and well integrated into the PyTorch ecosystem. If you want to use PyTorch to train ML models on videos and audio, TorchCodec is how you turn these into data.
We achieve these capabilities through:
Pythonic APIs that mirror Python and PyTorch conventions.
Relying on FFmpeg to do the decoding / encoding. TorchCodec uses the version of FFmpeg you already have installed. FFmpeg is a mature library with broad coverage available on most systems. It is, however, not easy to use. TorchCodec abstracts FFmpeg’s complexity to ensure it is used correctly and efficiently.
Returning data as PyTorch tensors, ready to be fed into PyTorch transforms or used directly to train models.
Installation instructions#
How to install TorchCodec
Decoding#
A simple video decoding example
A simple audio decoding example
A simple example demonstrating CUDA GPU decoding
How to efficiently decode videos from the cloud
How to decode a video with multiple processes or threads.
How to sample regular and random clips from a video
How to apply transforms while decoding
Tips for optimizing video decoding performance
Encoding#
How encode audio samples
How to encode video frames
