| Name | aac-datasets JSON |
| Version |
0.5.2
JSON |
| download |
| home_page | None |
| Summary | Audio Captioning datasets for PyTorch. |
| upload_time | 2024-03-23 14:13:47 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2022 Étienne Labbé (Labbeti) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
audio
deep-learning
pytorch
dataset
caption
datasets
captioning
audio-captioning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- # -*- 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 3.7+-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 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_v2` |
| 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`<br>`bbc`<br>`freesound`<br>`freesound_no_clotho`<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 on the train subset for AudioCaps, Clotho, 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.
### 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 install on Ubuntu using `sudo apt install ffmpeg` and **yt-dlp** from the [official repo](https://github.com/yt-dlp/yt-dlp).
<!-- programs can be downloaded on Ubuntu using `sudo apt install ffmpeg`. -->
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",
)
```
## 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, 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. (2013). aac-datasets: Audio Captioning datasets for PyTorch.", or use the following BibTeX citation:
```
@software{
Labbe_aac_datasets_2024,
author = {Labbé, Étienne},
license = {MIT},
month = {03},
title = {{aac-datasets}},
url = {https://github.com/Labbeti/aac-datasets/},
version = {0.5.2},
year = {2024}
}
```
## 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 3.7+-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 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_v2` |\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`<br>`bbc`<br>`freesound`<br>`freesound_no_clotho`<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 on the train subset for AudioCaps, Clotho, 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.\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 install on Ubuntu using `sudo apt install ffmpeg` and **yt-dlp** from the [official repo](https://github.com/yt-dlp/yt-dlp).\n<!-- programs can be downloaded on Ubuntu using `sudo apt install ffmpeg`. -->\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\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, 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. (2013). aac-datasets: Audio Captioning datasets for PyTorch.\", or use the following BibTeX citation:\n\n```\n@software{\n Labbe_aac_datasets_2024,\n author = {Labb\u00e9, \u00c9tienne},\n license = {MIT},\n month = {03},\n title = {{aac-datasets}},\n url = {https://github.com/Labbeti/aac-datasets/},\n version = {0.5.2},\n year = {2024}\n}\n```\n\n## Contact\nMaintainer:\n- [\u00c9tienne Labb\u00e9](https://labbeti.github.io/) \"Labbeti\": labbeti.pub@gmail.com\n",
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