TorchCodec 0.14 is out! It is compatible with torch >= 2.11
. It comes with two major additions: a fast audio WavDecoder, and support for
Fast wav decoder #
TorchCodec now has a dedicated WavDecoder for decoding WAV files. It bypasses FFmpeg entirely and reads WAV data directly, resulting in significantly faster decoding. It supports multiple sample formats (int16, int32, float32, etc.), and can decode from files, bytes, or file-like objects.
from torchcodec.decoders import WavDecoder
decoder = WavDecoder("audio.wav")
samples = decoder.get_all_samples() # AudioSamples with data and sample_rate
HDR Video Decoding #
VideoDecoder
now supports HDR (High Dynamic Range) video decoding without losing precision. When output_dtype=torch.float32
is specified, the decoder outputs RGB float32 frames in [0, 1]
, preserving the full HDR color range. This is supported for both CPU and CUDA!
import torch
from torchcodec.decoders import VideoDecoder
decoder = VideoDecoder("hdr_video.mp4", output_dtype=torch.float32)
frame = decoder[0] # Full HDR precision in float32
Other Improvements #
Improved audio seeking:seeking is now much faster (AudioDecoder
#1449)Dropped NPP dependency:TorchCodec
no longer depends on NVIDIA's NPP library, which will simplify installing and using TorchCodec for CUDA decoding.