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# Audio Dataset | ||
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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](https://huggingface.co/collections/datasets-examples/audio-dataset-66aca0b73e8f69e3d069e607). | ||
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A dataset with a supported structure and [file formats](./datasets-adding#file-formats) automatically has a Dataset Viewer on its page on the Hub. | ||
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--- | ||
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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`). | ||
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Alternatively, audio files can be in Parquet files or in TAR archives following the [WebDataset](https://github.com/webdataset/webdataset) format. | ||
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## Only audio files | ||
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If your dataset only consists of one column with audio, you can simply store your audio files at the root: | ||
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```plaintext | ||
my_dataset_repository/ | ||
├── 1.wav | ||
├── 2.wav | ||
├── 3.wav | ||
└── 4.wav | ||
``` | ||
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or in a subdirectory: | ||
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```plaintext | ||
my_dataset_repository/ | ||
└── audio | ||
├── 1.wav | ||
├── 2.wav | ||
├── 3.wav | ||
└── 4.wav | ||
``` | ||
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Multiple [formats](./datasets-adding#file-formats) are supported at the same time, including AIFF, FLAC, MP3, OGG and WAV. | ||
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```plaintext | ||
my_dataset_repository/ | ||
└── audio | ||
├── 1.aiff | ||
├── 2.ogg | ||
├── 3.mp3 | ||
└── 4.flac | ||
``` | ||
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If you have several splits, you can put your audio files into directories named accordingly: | ||
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```plaintext | ||
my_dataset_repository/ | ||
├── train | ||
│ ├── 1.wav | ||
│ └── 2.wav | ||
└── test | ||
├── 3.wav | ||
└── 4.wav | ||
``` | ||
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See [File names and splits](./datasets-file-names-and-splits) for more information and other ways to organize data by splits. | ||
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## Additional columns | ||
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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](https://huggingface.co/tasks/text-to-speech) or [automatic speech recognition](https://huggingface.co/tasks/automatic-speech-recognition). | ||
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```plaintext | ||
my_dataset_repository/ | ||
├── 1.wav | ||
├── 2.wav | ||
├── 3.wav | ||
├── 4.wav | ||
└── metadata.csv | ||
``` | ||
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Your `metadata.csv` file must have a `file_name` column which links image files with their metadata: | ||
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```csv | ||
file_name,animal | ||
1.wav,cat | ||
2.wav,cat | ||
3.wav,dog | ||
4.wav,dog | ||
``` | ||
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You can also use a [JSONL](https://jsonlines.org/) file `metadata.jsonl`: | ||
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```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"} | ||
``` | ||
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## Relative paths | ||
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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: | ||
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```plaintext | ||
my_dataset_repository/ | ||
└── test | ||
├── audio | ||
│ ├── 1.wav | ||
│ ├── 2.wav | ||
│ ├── 3.wav | ||
│ └── 4.wav | ||
└── metadata.csv | ||
``` | ||
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In this case, the `file_name` column must be a full relative path to the audio files, not just the filename: | ||
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```csv | ||
file_name,animal | ||
audio/1.wav,cat | ||
audio/2.wav,cat | ||
audio/3.wav,dog | ||
audio/4.wav,dog | ||
``` | ||
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Metadata file cannot be put in subdirectories of a directory with the audio files. | ||
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In this example, the `test` directory is used to setup the name of the training split. See [File names and splits](./datasets-file-names-and-splits) for more information. | ||
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## Audio classification | ||
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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: | ||
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```plaintext | ||
my_dataset_repository/ | ||
├── cat | ||
│ ├── 1.wav | ||
│ └── 2.wav | ||
└── dog | ||
├── 3.wav | ||
└── 4.wav | ||
``` | ||
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The dataset created with this structure contains two columns: `audio` and `label` (with values `cat` and `dog`). | ||
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You can also provide multiple splits. To do so, your dataset directory should have the following structure (see [File names and splits](./datasets-file-names-and-splits) for more information): | ||
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```plaintext | ||
my_dataset_repository/ | ||
├── test | ||
│ ├── cat | ||
│ │ └── 2.wav | ||
│ └── dog | ||
│ └── 4.wav | ||
└── train | ||
├── cat | ||
│ └── 1.wav | ||
└── dog | ||
└── 3.wav | ||
``` | ||
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You can disable this automatic addition of the `label` column in the [YAML configuration](./datasets-manual-configuration). If your directory names have no special meaning, set `drop_labels: true` in the README header: | ||
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```yaml | ||
configs: | ||
- config_name: default # Name of the dataset subset, if applicable. | ||
drop_labels: true | ||
``` | ||
## Large scale datasets | ||
### WebDataset format | ||
The [WebDataset](./datasets-webdataset) format is well suited for large scale audio datasets (see [AlienKevin/sbs_cantonese](https://huggingface.co/datasets/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. | ||
```plaintext | ||
my_dataset_repository/ | ||
├── train-0000.tar | ||
├── train-0001.tar | ||
├── ... | ||
└── train-1023.tar | ||
``` | ||
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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: | ||
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```plaintext | ||
train-0000/ | ||
├── 000.flac | ||
├── 000.json | ||
├── 001.flac | ||
├── 001.json | ||
├── ... | ||
├── 999.flac | ||
└── 999.json | ||
``` | ||
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Note that for user convenience and to enable the [Dataset Viewer](./datasets-viewer), every dataset hosted in the Hub is automatically converted to Parquet format up to 5GB. | ||
Read more about it in the [Parquet format](./datasets-viewer#access-the-parquet-files) documentation. | ||
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### Parquet format | ||
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Instead of uploading the audio files and metadata as individual files, you can embed everything inside a [Parquet](https://parquet.apache.org/) 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. | ||
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```plaintext | ||
my_dataset_repository/ | ||
└── train.parquet | ||
``` | ||
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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: | ||
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```yaml | ||
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](../datasets/audio_load). |
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