metadata

license: cc-by-4.0
task_categories:
  - audio-classification
language:
  - de
  - en
  - es
  - fr
  - it
  - nl
  - pl
  - sv
tags:
  - speech
  - speech-classifiation
  - text-to-speech
  - spoofing
  - multilingualism
pretty_name: FLEURS-HS VITS
size_categories:
  - 10K<n<100K

FLEURS-HS VITS

An extension of the FLEURS dataset for synthetic speech detection using text-to-speech, featured in the paper Synthetic speech detection with Wav2Vec 2.0 in various language settings.

This dataset is 1 of 3 used in the paper, the others being:

FLEURS-HS
- the default train, dev and test sets
- separated due to different licensing
ARCTIC-HS
- extension of the CMU_ARCTIC and L2-ARCTIC sets in a similar manner

Dataset Details

Dataset Description

The dataset features 8 languages originally seen in FLEURS:

German
English
Spanish
French
Italian
Dutch
Polish
Swedish

The synthetic samples are generated using:

Only the test VITS samples are provided. For every VITS voice, which is in practice specific model weights, one sample per transcript is provided.

Curated by: KONTXT by RealNetworks
Funded by: RealNetworks
Language(s) (NLP): English, German, Spanish, French, Italian, Dutch, Polish, Swedish
License: Apache 2.0 for the code, CC BY 4.0 for the dataset (but various licenses depending on the source for VITS samples)

Dataset Sources

The original FLEURS dataset was downloaded from HuggingFace.

FLEURS Repository: HuggingFace
FLEURS Paper: arXiv
Paper: Synthetic speech detection with Wav2Vec 2.0 in various language settings

Uses

This dataset is best used as a difficult test set. Each sample contains an Audio feature, and a label, which is always synthetic; this dataset does not include any human samples.

Direct Use

The following snippet of code demonstrates loading the training split for English:

from datasets import load_dataset

fleurs_hs = load_dataset(
    "realnetworks-kontxt/fleurs-hs-vits",
    "en_us",
    split="test",
    trust_remote_code=True,
)

To load a different language, change en_us into one of the following:

de_de for German
es_419 for Spanish
fr_fr for French
it_it for Italian
nl_nl for Dutch
pl_pl for Polish
sv_se for Swedish

This dataset only has a test split.

The trust_remote_code=True parameter is necessary because this dataset uses a custom loader. To check out which code is being ran, check out the loading script.

Dataset Structure

The dataset data is contained in the data directory.

There exists 1 directory per language.

Within that directory, there is a directory named splits; it contains 1 file per split:

test.tar.gz

That .tar.gz file contains 1 or more directories, named after the VITS model being used: ex. thorsten-vits

Each of these directories contain .wav files. Each .wav file is named after the ID of its transcript. Keep in mind that these directories can't be merged as they share their file names. An identical file name implies a speaker-voice pair, ex. human/123.wav and thorsten-vits/123.wav.

Finally, back to the language directory, it contains 3 metadata files, which are not used in the loaded dataset, but might be useful to researchers:

recording-metadata.csv
- contains the transcript ID, file name, split and gender of the original FLEURS samples
recording-transcripts.csv
- contains the transcrpits of the original FLEURS samples
voice-metadata.csv
- contains the groupation of TTS' used alongside the splits they were used for

Sample

A sample contains contains an Audio feature audio, and a string label.

{
  'audio': {
    'path': 'ljspeech-vits/1660.wav',
    'array': array([0.00119019, 0.00109863, 0.00106812, ..., 0., 0., 0.]),
    'sampling_rate': 16000
  },
  'label': 'synthetic'
}

Citation

The dataset is featured alongside our paper, Synthetic speech detection with Wav2Vec 2.0 in various language settings, which will be published on IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). We'll provide links once it's available online.

BibTeX:

Note, the following BibTeX is incomplete - we'll update it once the actual one is known.

@inproceedings{dropuljic-ssdww2v2ivls
  author={Dropuljić, Branimir and Šuflaj, Miljenko and Jertec, Andrej and Obadić, Leo}
  booktitle={2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}
  title={Synthetic speech detection with Wav2Vec 2.0 in various language settings}
  year={2024}
  volume={}
  number={}
  pages={1-5}
  keywords={Synthetic speech detection;text-to-speech;wav2vec 2.0;spoofing attack;multilingualism}
  doi={}
}

Dataset Card Authors

Miljenko Šuflaj

Dataset Card Contact

Miljenko Šuflaj