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c13737d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # Load audio data
You can load an audio dataset using the [`Audio`] feature that automatically decodes and resamples the audio files when you access the examples.
Audio decoding is based on the [`soundfile`](https://github.com/bastibe/python-soundfile) python package, which uses the [`libsndfile`](https://github.com/libsndfile/libsndfile) C library under the hood.
## Installation
To work with audio datasets, you need to have the `audio` dependencies installed.
Check out the [installation](./installation#audio) guide to learn how to install it.
## Local files
You can load your own dataset using the paths to your audio files. Use the [`~Dataset.cast_column`] function to take a column of audio file paths, and cast it to the [`Audio`] feature:
```py
>>> audio_dataset = Dataset.from_dict({"audio": ["path/to/audio_1", "path/to/audio_2", ..., "path/to/audio_n"]}).cast_column("audio", Audio())
>>> audio_dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': 'path/to/audio_1',
'sampling_rate': 16000}
```
## AudioFolder
You can also load a dataset with an `AudioFolder` dataset builder. It does not require writing a custom dataloader, making it useful for quickly creating and loading audio datasets with several thousand audio files.
## AudioFolder with metadata
To link your audio files with metadata information, make sure your dataset has a `metadata.csv` file. Your dataset structure might look like:
```
folder/train/metadata.csv
folder/train/first_audio_file.mp3
folder/train/second_audio_file.mp3
folder/train/third_audio_file.mp3
```
Your `metadata.csv` file must have a `file_name` column which links audio files with their metadata. An example `metadata.csv` file might look like:
```text
file_name,transcription
first_audio_file.mp3,znowu si臋 duch z cia艂em zro艣nie w m艂odocianej wstaniesz wiosnie i mo偶esz skutkiem tych lek贸w umiera膰 wstawa膰 wiek wiek贸w dalej tam by艂y przestrogi jak sieka膰 g艂ow臋 jak nogi
second_audio_file.mp3,ju偶 u 藕wierzy艅ca podwoj贸w kr贸l zasiada przy nim ksi膮偶臋ta i panowie rada a gdzie wznios艂y kr膮偶y艂 ganek rycerze obok kochanek kr贸l skin膮艂 palcem zacz臋to igrzysko
third_audio_file.mp3,pewnie k臋dy艣 w ob艂臋dzie ubite min臋艂y szlaki zaczekajmy dzie艅 jaki po艣lemy szuka膰 wsz臋dzie dzi艣 jutro pewnie b臋dzie pos艂ali wsz臋dzie s艂ugi czekali dzie艅 i drugi gdy nic nie doczekali z p艂aczem chc膮 jecha膰 dali
```
`AudioFolder` will load audio data and create a `transcription` column containing texts from `metadata.csv`:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("audiofolder", data_dir="/path/to/folder")
>>> # OR by specifying the list of files
>>> dataset = load_dataset("audiofolder", data_files=["path/to/audio_1", "path/to/audio_2", ..., "path/to/audio_n"])
```
You can load remote datasets from their URLs with the data_files parameter:
```py
>>> dataset = load_dataset("audiofolder", data_files=["https://foo.bar/audio_1", "https://foo.bar/audio_2", ..., "https://foo.bar/audio_n"]
>>> # for example, pass SpeechCommands archive:
>>> dataset = load_dataset("audiofolder", data_files="https://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz")
```
Metadata can also be specified as JSON Lines, in which case use `metadata.jsonl` as the name of the metadata file. This format is helpful in scenarios when one of the columns is complex, e.g. a list of floats, to avoid parsing errors or reading the complex values as strings.
To ignore the information in the metadata file, set `drop_metadata=True` in [`load_dataset`]:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("audiofolder", data_dir="/path/to/folder", drop_metadata=True)
```
If you don't have a metadata file, `AudioFolder` automatically infers the label name from the directory name.
If you want to drop automatically created labels, set `drop_labels=True`.
In this case, your dataset will only contain an audio column:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("audiofolder", data_dir="/path/to/folder_without_metadata", drop_labels=True)
```
<Tip>
For more information about creating your own `AudioFolder` dataset, take a look at the [Create an audio dataset](./audio_dataset) guide.
</Tip>
For a guide on how to load any type of dataset, take a look at the <a class="underline decoration-sky-400 decoration-2 font-semibold" href="./loading">general loading guide</a>.
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