# TorchCodec 0.14: HDR Video Decoding for CPU and CUDA, and Fast Wav Decoder

> Source: <https://github.com/meta-pytorch/torchcodec/releases/tag/v0.14.0>
> Published: 2026-06-10 14:08:36+00:00

[TorchCodec 0.14](https://meta-pytorch.org/torchcodec/0.14/) is out! It is compatible with `torch >= 2.11`

. It comes with two major additions: a fast audio [ WavDecoder](https://meta-pytorch.org/torchcodec/0.14/generated/torchcodec.decoders.WavDecoder.html#torchcodec.decoders.WavDecoder), and support for

[HDR video decoding](https://meta-pytorch.org/torchcodec/0.14/generated_examples/decoding/hdr_decoding.html)!

## Fast wav decoder

TorchCodec now has a dedicated [ WavDecoder](https://meta-pytorch.org/torchcodec/0.14/generated/torchcodec.decoders.WavDecoder.html#torchcodec.decoders.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.

``` python
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](https://meta-pytorch.org/torchcodec/0.14/generated_examples/decoding/hdr_decoding.html) (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!

``` python
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](https://github.com/meta-pytorch/torchcodec/pull/1449))**Dropped NPP dependency**:`TorchCodec`

no longer depends on NVIDIA's NPP library, which will simplify installing and using TorchCodec for CUDA decoding.