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Audio Captioning datasets for PyTorch.

Project description

Audio Captioning datasets for PyTorch

Python PyTorch Code style: black Build Documentation Status

Audio Captioning unofficial datasets source code for AudioCaps [1], Clotho [2], MACS [3], and WavCaps [4], designed for PyTorch.

Installation

pip install aac-datasets

If you want to check if the package has been installed and the version, you can use this command:

aac-datasets-info

Examples

Create Clotho dataset

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

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 :

dataset = Clotho(root=".", subset="dev", download=True)

However, if you want to download datasets from a script, you can also use the following command :

aac-datasets-download --root "." clotho --subsets "dev"

Datasets information

Here is the statistics for each dataset :

Dataset Sampling
rate (kHz)
Estimated
size (GB)
Source Subsets
AudioCaps 32 43 AudioSet train
val
test
train_v2
Clotho 44.1 53 Freesound dev
val
eval
dcase_aac_test
dcase_aac_analysis
dcase_t2a_audio
dcase_t2a_captions
MACS 48 13 TAU Urban Acoustic Scenes 2019 full
WavCaps 32 941 AudioSet
BBC Sound Effects
FreeSound
SoundBible
as
as_noac
bbc
fsd
fsd_nocl
sb

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 and MACS:

AudioCaps/train Clotho/dev MACS/full WavCaps/full
Nb audios 49,838 3,840 3,930 403,050
Total audio duration (h) 136.61 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 words2 402,482 217,362 160,006 3,161,823
Sentence size2 2-52 8-20 5-40 2-38
Vocabulary2 4724 4369 2721 24600

1 This duration is estimated on the total duration of 46230/49838 files of 126.7h.

2 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.

You can also override their paths for AudioCaps:

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 package to download AudioCaps, you will have to respect the AudioCaps folder tree:

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 :

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, Etienne},
    license = {MIT},
    month = {01},
    title = {{aac-datasets}},
    url = {https://github.com/Labbeti/aac-datasets/},
    version = {0.5.0},
    year = {2024}
}

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