meta-pytorch/torchcodec

Health v2 · maintenance only

Adoption not part of the score

1160 stars · 123 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TorchCodec is a PyTorch-native library for decoding and encoding videos, audio, and images into PyTorch tensors on CPU and CUDA GPU, built on top of FFmpeg. It provides Pythonic APIs for frame-level video access, audio decoding, and encoding, designed for ML training and inference pipelines.

Use cases

• decode video files into pytorch tensors for training
• load video frames by index or timestamp for model training
• decode audio into tensors for speech models
• encode tensors back into video or audio files
• gpu-accelerated video decoding with cuda
• convert images to tensors without ffmpeg
• build video data loading pipelines for pytorch

When to choose

• you train or run inference with PyTorch on video, audio, or image data
• you want tensors directly instead of numpy arrays or raw frames
• you need fast GPU (CUDA) media decoding
• you want a simple Pythonic API over FFmpeg's complexity

When to avoid

• you need a general-purpose media tool like ffmpeg CLI
• your project does not use PyTorch
• you need exotic codecs or container formats outside FFmpeg's coverage
• you need streaming playback rather than tensor decoding

Facets

library · maturity active

video-processing audio-processing image-processing machine-learning machine-learning deep-learning media image-processing python windows cross-platform pytorch ffmpeg tensor media-decoding media-encoding cuda video-decoder data-loading video audio linux macos gpu

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