Principle:Huggingface Datasets Audio Feature Handling
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, NLP |
| Last Updated | 2026-02-14 18:00 GMT |
Overview
Handling audio data with decoding and resampling support enables datasets to store, load, and preprocess audio for speech and audio ML tasks.
Description
Audio feature handling provides a complete pipeline for working with audio data in datasets. Audio can be supplied as file paths, dictionaries with path/bytes keys, dictionaries with array/sampling_rate keys, or torchcodec AudioDecoder objects. The feature stores audio in an Arrow struct (bytes + path) and lazily decodes it on access using torchcodec. Key capabilities include automatic resampling to a target sampling rate, channel conversion (mono/stereo), and stream index selection. When decoding is disabled, the raw path/bytes dictionary is returned for efficient batch operations.
Usage
Use audio feature handling when your dataset contains speech recordings, music, environmental sounds, or any audio data. The feature type abstracts away the complexity of audio file formats, resampling, and channel management, providing a consistent interface for audio ML pipelines.
Theoretical Basis
Like image features, audio features use a two-layer abstraction: Arrow-level storage (struct of bytes and path) and Python-level presentation (decoded audio objects with array and sampling rate). The resampling capability is essential because different audio sources may have different sampling rates, while models typically expect a fixed rate. The torchcodec-based decoder provides efficient, lazy decoding that avoids loading entire audio files until they are actually needed. Channel conversion support enables standardization between mono and stereo formats.