Audio Dataset

This guide will show you how to configure your dataset repository with audio files. You can find accompanying examples of repositories in this Audio datasets examples collection.

A dataset with a supported structure and file formats automatically has a Dataset Viewer on its page on the Hub.

Additional information about your audio files - such as transcriptions - is automatically loaded as long as you include this information in a metadata file (metadata.csv/metadata.jsonl).

Alternatively, audio files can be in Parquet files or in TAR archives following the WebDataset format.

Only audio files

If your dataset only consists of one column with audio, you can simply store your audio files at the root:

my_dataset_repository/
├── 1.wav
├── 2.wav
├── 3.wav
└── 4.wav

or in a subdirectory:

my_dataset_repository/
└── audio
    ├── 1.wav
    ├── 2.wav
    ├── 3.wav
    └── 4.wav

Multiple formats are supported at the same time, including AIFF, FLAC, MP3, OGG and WAV.

my_dataset_repository/
└── audio
    ├── 1.aiff
    ├── 2.ogg
    ├── 3.mp3
    └── 4.flac

If you have several splits, you can put your audio files into directories named accordingly:

my_dataset_repository/
├── train
│   ├── 1.wav
│   └── 2.wav
└── test
    ├── 3.wav
    └── 4.wav

See File names and splits for more information and other ways to organize data by splits.

Additional columns

If there is additional information you’d like to include about your dataset, like the transcription, add it as a metadata.csv file in your repository. This lets you quickly create datasets for different audio tasks like text-to-speech or automatic speech recognition.

my_dataset_repository/
├── 1.wav
├── 2.wav
├── 3.wav
├── 4.wav
└── metadata.csv

Your metadata.csv file must have a file_name column which links image files with their metadata:

file_name,animal
1.wav,cat
2.wav,cat
3.wav,dog
4.wav,dog

You can also use a JSONL file metadata.jsonl:

{"file_name": "1.wav","text": "cat"}
{"file_name": "2.wav","text": "cat"}
{"file_name": "3.wav","text": "dog"}
{"file_name": "4.wav","text": "dog"}

Relative paths

Metadata file must be located either in the same directory with the audio files it is linked to, or in any parent directory, like in this example:

my_dataset_repository/
└── test
    ├── audio
    │   ├── 1.wav
    │   ├── 2.wav
    │   ├── 3.wav
    │   └── 4.wav
    └── metadata.csv

In this case, the file_name column must be a full relative path to the audio files, not just the filename:

file_name,animal
audio/1.wav,cat
audio/2.wav,cat
audio/3.wav,dog
audio/4.wav,dog

Metadata file cannot be put in subdirectories of a directory with the audio files.

In this example, the test directory is used to setup the name of the training split. See File names and splits for more information.

Audio classification

For audio classification datasets, you can also use a simple setup: use directories to name the audio classes. Store your audio files in a directory structure like:

my_dataset_repository/
├── cat
│   ├── 1.wav
│   └── 2.wav
└── dog
    ├── 3.wav
    └── 4.wav

The dataset created with this structure contains two columns: audio and label (with values cat and dog).

You can also provide multiple splits. To do so, your dataset directory should have the following structure (see File names and splits for more information):

my_dataset_repository/
├── test
│   ├── cat
│   │   └── 2.wav
│   └── dog
│       └── 4.wav
└── train
    ├── cat
    │   └── 1.wav
    └── dog
        └── 3.wav

You can disable this automatic addition of the label column in the YAML configuration. If your directory names have no special meaning, set drop_labels: true in the README header:

configs:
  - config_name: default  # Name of the dataset subset, if applicable.
    drop_labels: true

Large scale datasets

WebDataset format

The WebDataset format is well suited for large scale audio datasets (see AlienKevin/sbs_cantonese for example). It consists of TAR archives containing audio files and their metadata and is optimized for streaming. It is useful if you have a large number of audio files and to get streaming data loaders for large scale training.

my_dataset_repository/
├── train-0000.tar
├── train-0001.tar
├── ...
└── train-1023.tar

To make a WebDataset TAR archive, create a directory containing the audio files and metadata files to be archived and create the TAR archive using e.g. the tar command. The usual size per archive is generally around 1GB. Make sure each audio file and metadata pair share the same file prefix, for example:

train-0000/
├── 000.flac
├── 000.json
├── 001.flac
├── 001.json
├── ...
├── 999.flac
└── 999.json

Note that for user convenience and to enable the Dataset Viewer, every dataset hosted in the Hub is automatically converted to Parquet format up to 5GB. Read more about it in the Parquet format documentation.

Parquet format

Instead of uploading the audio files and metadata as individual files, you can embed everything inside a Parquet file. This is useful if you have a large number of audio files, if you want to embed multiple audio columns, or if you want to store additional information about the audio in the same file. Parquet is also useful for storing data such as raw bytes, which is not supported by JSON/CSV.

my_dataset_repository/
└── train.parquet

Audio columns are of type struct, with a binary field "bytes" for the audio data and a string field "path" for the image file name or path. You should specify the feature types of the columns directly in YAML in the README header, for example:

dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: caption
    dtype: string

Alternatively, Parquet files with Audio data can be created using the datasets library by setting the column type to Audio() and using the .to_parquet(...) method or .push_to_hub(...). You can find a guide on loading audio datasets in datasets here.