| Name | aac-datasets JSON |
| Version |
0.7.0
JSON |
| download |
| home_page | None |
| Summary | Audio Captioning datasets for PyTorch. |
| upload_time | 2025-07-18 22:41:01 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Étienne Labbé (Labbeti)
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|
| keywords |
audio
deep-learning
pytorch
dataset
caption
datasets
captioning
audio-captioning
|
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<!-- # -*- coding: utf-8 -*- -->
<div align="center">
# Audio Captioning datasets for PyTorch
<a href="https://www.python.org/"><img alt="Python" src="https://img.shields.io/badge/-Python 3.8+-blue?style=for-the-badge&logo=python&logoColor=white"></a>
<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/-PyTorch 1.10.1+-ee4c2c?style=for-the-badge&logo=pytorch&logoColor=white"></a>
<a href="https://black.readthedocs.io/en/stable/"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-black.svg?style=for-the-badge&labelColor=gray"></a>
<a href="https://github.com/Labbeti/aac-datasets/actions"><img alt="Build" src="https://img.shields.io/github/actions/workflow/status/Labbeti/aac-datasets/python-package-pip.yaml?branch=main&style=for-the-badge&logo=github"></a>
<a href='https://aac-datasets.readthedocs.io/en/stable/?badge=stable'>
<img src='https://readthedocs.org/projects/aac-datasets/badge/?version=stable&style=for-the-badge' alt='Documentation Status' />
</a>
Audio Captioning unofficial datasets source code for **AudioCaps** [[1]](#audiocaps), **Clotho** [[2]](#clotho), **MACS** [[3]](#macs), and **WavCaps** [[4]](#wavcaps), designed for PyTorch.
</div>
## Installation
```bash
pip install aac-datasets
```
If you want to check if the package has been installed and the version, you can use this command:
```bash
aac-datasets-info
```
## Examples
### Create Clotho dataset
```python
from aac_datasets import Clotho
dataset = Clotho(root=".", download=True)
item = dataset[0]
audio, captions = item["audio"], item["captions"]
# audio: Tensor of shape (n_channels=1, audio_max_size)
# captions: list of str
```
### Build PyTorch dataloader with Clotho
```python
from torch.utils.data.dataloader import DataLoader
from aac_datasets import Clotho
from aac_datasets.utils.collate import BasicCollate
dataset = Clotho(root=".", download=True)
dataloader = DataLoader(dataset, batch_size=4, collate_fn=BasicCollate())
for batch in dataloader:
# batch["audio"]: list of 4 tensors of shape (n_channels, audio_size)
# batch["captions"]: list of 4 lists of str
...
```
## Download datasets
To download a dataset, you can use `download` argument in dataset construction :
```python
dataset = Clotho(root=".", subset="dev", download=True)
```
However, if you want to download datasets from a script, you can also use the following command :
```bash
aac-datasets-download --root "." clotho --subsets "dev"
```
## Datasets information
`aac-datasets` package contains 4 different datasets :
<!-- | | AudioCaps | Clotho | MACS | WavCaps |
|:---:|:---:|:---:|:---:|:---:|
| Subsets | `train`, `val`, `test` | `dev`, `val`, `eval`, `dcase_aac_test`, `dcase_aac_analysis`, `dcase_t2a_audio`, `dcase_t2a_captions` | `full` | `as`, `as_noac`, `bbc`, `fsd`, `fsd_nocl`, `sb` |
| Sample rate (kHz) | 32 | 44.1 | 48 | 32 |
| Estimated size (GB) | 43 | 53 | 13 | 941 |
| Audio source | AudioSet | Freesound | TAU Urban Acoustic Scenes 2019 | AudioSet, BBC Sound Effects, Freesound, SoundBible | -->
| Dataset | Sampling<br>rate (kHz) | Estimated<br>size (GB) | Source | Subsets |
|:---:|:---:|:---:|:---:|:---:|
| AudioCaps | 32 | 43 | AudioSet | `train`<br>`val`<br>`test`<br>`train_fixed` |
| Clotho | 44.1 | 53 | Freesound | `dev`<br>`val`<br>`eval`<br>`dcase_aac_test`<br>`dcase_aac_analysis`<br>`dcase_t2a_audio`<br>`dcase_t2a_captions` |
| MACS | 48 | 13 | TAU Urban Acoustic Scenes 2019 | `full` |
| WavCaps | 32 | 941 | AudioSet<br>BBC Sound Effects<br>Freesound<br>SoundBible | `audioset`<br>`audioset_no_audiocaps_v1`<br>`bbc`<br>`freesound`<br>`freesound_no_clotho_v2`<br>`soundbible` |
For Clotho, the **dev** subset should be used for training, val for validation and eval for testing.
Here is additional statistics of the train subsets from AudioCaps (v1), Clotho (v2.1), MACS and WavCaps:
| | AudioCaps/train | Clotho/dev | MACS/full | WavCaps/full |
|:---:|:---:|:---:|:---:|:---:|
| Nb audios | 49,838 | 3,840 | 3,930 | 403,050 |
| Total audio duration (h) | 136.6<sup>1</sup> | 24.0 | 10.9 | 7563.3 |
| Audio duration range (s) | 0.5-10 | 15-30 | 10 | 1-67,109 |
| Nb captions per audio | 1 | 5 | 2-5 | 1 |
| Nb captions | 49,838 | 19,195 | 17,275 | 403,050 |
| Total nb words<sup>2</sup> | 402,482 | 217,362 | 160,006 | 3,161,823 |
| Sentence size<sup>2</sup> | 2-52 | 8-20 | 5-40 | 2-38 |
| Vocabulary<sup>2</sup> | 4724 | 4369 | 2721 | 24,600 |
| Annotated by | Human | Human | Human | Machine |
| Corrected by | Human | Human | None | None |
<sup>1</sup> This duration is estimated on the total duration of 46230/49838 files of 126.7h.
<sup>2</sup> The sentences are cleaned (lowercase+remove punctuation) and tokenized using the spacy tokenizer to count the words.
## Requirements
This package has been developped for Ubuntu 20.04, and it is expected to work on most Linux-based distributions.
It has been tested with Python versions 3.7 and 3.13.
### Python packages
Python requirements are automatically installed when using pip on this repository.
```
torch >= 1.10.1
torchaudio >= 0.10.1
py7zr >= 0.17.2
pyyaml >= 6.0
tqdm >= 4.64.0
huggingface-hub >= 0.15.1
numpy >= 1.21.2
```
### External requirements (AudioCaps only)
The external requirements needed to download **AudioCaps** are **ffmpeg** and **yt-dlp**.
**ffmpeg** can be installed on Ubuntu using `sudo apt install ffmpeg` and **yt-dlp** from the [official repo](https://github.com/yt-dlp/yt-dlp).
You can also override their paths for AudioCaps:
```python
from aac_datasets import AudioCaps
dataset = AudioCaps(
download=True,
ffmpeg_path="/my/path/to/ffmpeg",
ytdl_path="/my/path/to/ytdlp",
)
```
Since YouTube prevents bots to download videos, you might want to use `ytdlp_opts` argument to use cookies to overcome failed downloads, e.g. `AudioCaps(ytdlp_opts=["--cookies-from-browser", "firefox"])`. See more information [on the documentation of yt-dlp](https://github.com/yt-dlp/yt-dlp/wiki/FAQ#how-do-i-pass-cookies-to-yt-dlp).
## Additional information
### Compatibility with audiocaps-download
If you want to use [audiocaps-download 1.0](https://github.com/MorenoLaQuatra/audiocaps-download) package to download AudioCaps (v1 only), you will have to respect the AudioCaps folder tree:
```python
from audiocaps_download import Downloader
root = "your/path/to/root"
downloader = Downloader(root_path=f"{root}/AUDIOCAPS/audio_32000Hz/", n_jobs=16)
downloader.download(format="wav")
```
Then disable audio download and set the correct audio format before init AudioCaps :
```python
from aac_datasets import AudioCaps
dataset = AudioCaps(
root=root,
subset="train",
download=True,
audio_format="wav",
download_audio=False, # this will only download labels and metadata files
)
```
## References
#### AudioCaps
[1] C. D. Kim, B. Kim, H. Lee, and G. Kim, “Audiocaps: Generating captions for audios in the wild,” in NAACL-HLT, 2019. Available: https://aclanthology.org/N19-1011/
#### Clotho
[2] K. Drossos, S. Lipping, and T. Virtanen, “Clotho: An Audio Captioning Dataset,” arXiv:1910.09387 [cs, eess], Oct. 2019, Available: http://arxiv.org/abs/1910.09387
#### MACS
[3] F. Font, A. Mesaros, D. P. W. Ellis, E. Fonseca, M. Fuentes, and B. Elizalde, Proceedings of the 6th Workshop on Detection and Classication of Acoustic Scenes and Events (DCASE 2021). Barcelona, Spain: Music Technology Group - Universitat Pompeu Fabra, Nov. 2021. Available: https://doi.org/10.5281/zenodo.5770113
#### WavCaps
[4] X. Mei et al., “WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research,” arXiv preprint arXiv:2303.17395, 2023, [Online]. Available: https://arxiv.org/pdf/2303.17395.pdf
## Cite the aac-datasets package
If you use this software, please consider cite it as "Labbe, E. (2025). aac-datasets: Audio Captioning datasets for PyTorch.", or use the following BibTeX citation:
```
@software{
Labbe_aac_datasets_2025,
author = {Labbé, Étienne},
license = {MIT},
month = {07},
title = {{aac-datasets}},
url = {https://github.com/Labbeti/aac-datasets/},
version = {0.7.0},
year = {2025}
}
```
## See also
- [AudioCaps official data repository](https://github.com/cdjkim/audiocaps/tree/master)
- [Clotho official data repository](https://zenodo.org/records/4783391)
- [MACS official data repository](https://zenodo.org/records/5114771)
- [WavCaps official data repository](https://huggingface.co/datasets/cvssp/WavCaps)
## Contact
Maintainer:
- [Étienne Labbé](https://labbeti.github.io/) "Labbeti": labbeti.pub@gmail.com
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"description": "<!-- # -*- coding: utf-8 -*- -->\n\n<div align=\"center\">\n\n# Audio Captioning datasets for PyTorch\n\n<a href=\"https://www.python.org/\"><img alt=\"Python\" src=\"https://img.shields.io/badge/-Python 3.8+-blue?style=for-the-badge&logo=python&logoColor=white\"></a>\n<a href=\"https://pytorch.org/get-started/locally/\"><img alt=\"PyTorch\" src=\"https://img.shields.io/badge/-PyTorch 1.10.1+-ee4c2c?style=for-the-badge&logo=pytorch&logoColor=white\"></a>\n<a href=\"https://black.readthedocs.io/en/stable/\"><img alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-black.svg?style=for-the-badge&labelColor=gray\"></a>\n<a href=\"https://github.com/Labbeti/aac-datasets/actions\"><img alt=\"Build\" src=\"https://img.shields.io/github/actions/workflow/status/Labbeti/aac-datasets/python-package-pip.yaml?branch=main&style=for-the-badge&logo=github\"></a>\n<a href='https://aac-datasets.readthedocs.io/en/stable/?badge=stable'>\n <img src='https://readthedocs.org/projects/aac-datasets/badge/?version=stable&style=for-the-badge' alt='Documentation Status' />\n</a>\n\nAudio Captioning unofficial datasets source code for **AudioCaps** [[1]](#audiocaps), **Clotho** [[2]](#clotho), **MACS** [[3]](#macs), and **WavCaps** [[4]](#wavcaps), designed for PyTorch.\n\n</div>\n\n## Installation\n```bash\npip install aac-datasets\n```\n\nIf you want to check if the package has been installed and the version, you can use this command:\n```bash\naac-datasets-info\n```\n\n## Examples\n\n### Create Clotho dataset\n\n```python\nfrom aac_datasets import Clotho\n\ndataset = Clotho(root=\".\", download=True)\nitem = dataset[0]\naudio, captions = item[\"audio\"], item[\"captions\"]\n# audio: Tensor of shape (n_channels=1, audio_max_size)\n# captions: list of str\n```\n\n### Build PyTorch dataloader with Clotho\n\n```python\nfrom torch.utils.data.dataloader import DataLoader\nfrom aac_datasets import Clotho\nfrom aac_datasets.utils.collate import BasicCollate\n\ndataset = Clotho(root=\".\", download=True)\ndataloader = DataLoader(dataset, batch_size=4, collate_fn=BasicCollate())\n\nfor batch in dataloader:\n # batch[\"audio\"]: list of 4 tensors of shape (n_channels, audio_size)\n # batch[\"captions\"]: list of 4 lists of str\n ...\n```\n\n## Download datasets\nTo download a dataset, you can use `download` argument in dataset construction :\n```python\ndataset = Clotho(root=\".\", subset=\"dev\", download=True)\n```\nHowever, if you want to download datasets from a script, you can also use the following command :\n```bash\naac-datasets-download --root \".\" clotho --subsets \"dev\"\n```\n\n## Datasets information\n`aac-datasets` package contains 4 different datasets :\n\n<!-- | | AudioCaps | Clotho | MACS | WavCaps |\n|:---:|:---:|:---:|:---:|:---:|\n| Subsets | `train`, `val`, `test` | `dev`, `val`, `eval`, `dcase_aac_test`, `dcase_aac_analysis`, `dcase_t2a_audio`, `dcase_t2a_captions` | `full` | `as`, `as_noac`, `bbc`, `fsd`, `fsd_nocl`, `sb` |\n| Sample rate (kHz) | 32 | 44.1 | 48 | 32 |\n| Estimated size (GB) | 43 | 53 | 13 | 941 |\n| Audio source | AudioSet | Freesound | TAU Urban Acoustic Scenes 2019 | AudioSet, BBC Sound Effects, Freesound, SoundBible | -->\n\n| Dataset | Sampling<br>rate (kHz) | Estimated<br>size (GB) | Source | Subsets |\n|:---:|:---:|:---:|:---:|:---:|\n| AudioCaps | 32 | 43 | AudioSet | `train`<br>`val`<br>`test`<br>`train_fixed` |\n| Clotho | 44.1 | 53 | Freesound | `dev`<br>`val`<br>`eval`<br>`dcase_aac_test`<br>`dcase_aac_analysis`<br>`dcase_t2a_audio`<br>`dcase_t2a_captions` |\n| MACS | 48 | 13 | TAU Urban Acoustic Scenes 2019 | `full` |\n| WavCaps | 32 | 941 | AudioSet<br>BBC Sound Effects<br>Freesound<br>SoundBible | `audioset`<br>`audioset_no_audiocaps_v1`<br>`bbc`<br>`freesound`<br>`freesound_no_clotho_v2`<br>`soundbible` |\n\nFor Clotho, the **dev** subset should be used for training, val for validation and eval for testing.\n\nHere is additional statistics of the train subsets from AudioCaps (v1), Clotho (v2.1), MACS and WavCaps:\n\n| | AudioCaps/train | Clotho/dev | MACS/full | WavCaps/full |\n|:---:|:---:|:---:|:---:|:---:|\n| Nb audios | 49,838 | 3,840 | 3,930 | 403,050 |\n| Total audio duration (h) | 136.6<sup>1</sup> | 24.0 | 10.9 | 7563.3 |\n| Audio duration range (s) | 0.5-10 | 15-30 | 10 | 1-67,109 |\n| Nb captions per audio | 1 | 5 | 2-5 | 1 |\n| Nb captions | 49,838 | 19,195 | 17,275 | 403,050 |\n| Total nb words<sup>2</sup> | 402,482 | 217,362 | 160,006 | 3,161,823 |\n| Sentence size<sup>2</sup> | 2-52 | 8-20 | 5-40 | 2-38 |\n| Vocabulary<sup>2</sup> | 4724 | 4369 | 2721 | 24,600 |\n| Annotated by | Human | Human | Human | Machine |\n| Corrected by | Human | Human | None | None |\n\n<sup>1</sup> This duration is estimated on the total duration of 46230/49838 files of 126.7h.\n\n<sup>2</sup> The sentences are cleaned (lowercase+remove punctuation) and tokenized using the spacy tokenizer to count the words.\n\n## Requirements\n\nThis package has been developped for Ubuntu 20.04, and it is expected to work on most Linux-based distributions.\nIt has been tested with Python versions 3.7 and 3.13.\n\n### Python packages\n\nPython requirements are automatically installed when using pip on this repository.\n```\ntorch >= 1.10.1\ntorchaudio >= 0.10.1\npy7zr >= 0.17.2\npyyaml >= 6.0\ntqdm >= 4.64.0\nhuggingface-hub >= 0.15.1\nnumpy >= 1.21.2\n```\n\n### External requirements (AudioCaps only)\n\nThe external requirements needed to download **AudioCaps** are **ffmpeg** and **yt-dlp**.\n**ffmpeg** can be installed on Ubuntu using `sudo apt install ffmpeg` and **yt-dlp** from the [official repo](https://github.com/yt-dlp/yt-dlp).\n\nYou can also override their paths for AudioCaps:\n```python\nfrom aac_datasets import AudioCaps\ndataset = AudioCaps(\n download=True,\n ffmpeg_path=\"/my/path/to/ffmpeg\",\n ytdl_path=\"/my/path/to/ytdlp\",\n)\n```\n\nSince YouTube prevents bots to download videos, you might want to use `ytdlp_opts` argument to use cookies to overcome failed downloads, e.g. `AudioCaps(ytdlp_opts=[\"--cookies-from-browser\", \"firefox\"])`. See more information [on the documentation of yt-dlp](https://github.com/yt-dlp/yt-dlp/wiki/FAQ#how-do-i-pass-cookies-to-yt-dlp).\n\n## Additional information\n### Compatibility with audiocaps-download\nIf you want to use [audiocaps-download 1.0](https://github.com/MorenoLaQuatra/audiocaps-download) package to download AudioCaps (v1 only), you will have to respect the AudioCaps folder tree:\n```python\nfrom audiocaps_download import Downloader\nroot = \"your/path/to/root\"\ndownloader = Downloader(root_path=f\"{root}/AUDIOCAPS/audio_32000Hz/\", n_jobs=16)\ndownloader.download(format=\"wav\")\n```\n\nThen disable audio download and set the correct audio format before init AudioCaps :\n```python\nfrom aac_datasets import AudioCaps\ndataset = AudioCaps(\n root=root,\n subset=\"train\",\n download=True,\n audio_format=\"wav\",\n download_audio=False, # this will only download labels and metadata files\n)\n```\n\n## References\n#### AudioCaps\n[1] C. D. Kim, B. Kim, H. Lee, and G. Kim, \u201cAudiocaps: Generating captions for audios in the wild,\u201d in NAACL-HLT, 2019. Available: https://aclanthology.org/N19-1011/\n\n#### Clotho\n[2] K. Drossos, S. Lipping, and T. Virtanen, \u201cClotho: An Audio Captioning Dataset,\u201d arXiv:1910.09387 [cs, eess], Oct. 2019, Available: http://arxiv.org/abs/1910.09387\n\n#### MACS\n[3] F. Font, A. Mesaros, D. P. W. Ellis, E. Fonseca, M. Fuentes, and B. Elizalde, Proceedings of the 6th Workshop on Detection and Classication of Acoustic Scenes and Events (DCASE 2021). Barcelona, Spain: Music Technology Group - Universitat Pompeu Fabra, Nov. 2021. Available: https://doi.org/10.5281/zenodo.5770113\n\n#### WavCaps\n[4] X. Mei et al., \u201cWavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research,\u201d arXiv preprint arXiv:2303.17395, 2023, [Online]. Available: https://arxiv.org/pdf/2303.17395.pdf\n\n## Cite the aac-datasets package\nIf you use this software, please consider cite it as \"Labbe, E. (2025). aac-datasets: Audio Captioning datasets for PyTorch.\", or use the following BibTeX citation:\n\n```\n@software{\n Labbe_aac_datasets_2025,\n author = {Labb\u00e9, \u00c9tienne},\n license = {MIT},\n month = {07},\n title = {{aac-datasets}},\n url = {https://github.com/Labbeti/aac-datasets/},\n version = {0.7.0},\n year = {2025}\n}\n```\n\n## See also\n- [AudioCaps official data repository](https://github.com/cdjkim/audiocaps/tree/master)\n- [Clotho official data repository](https://zenodo.org/records/4783391)\n- [MACS official data repository](https://zenodo.org/records/5114771)\n- [WavCaps official data repository](https://huggingface.co/datasets/cvssp/WavCaps)\n\n## Contact\nMaintainer:\n- [\u00c9tienne Labb\u00e9](https://labbeti.github.io/) \"Labbeti\": labbeti.pub@gmail.com\n",
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