FLEURS-HS

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 VITS
  • test set containing (generally) more difficult synthetic samples
  • 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 original FLEURS samples are used as human samples, while synthetic samples are generated using:

• Google Cloud Text-To-Speech
• Azure Text-To-Speech
• Amazon Polly

The resulting dataset features roughly twice the samples per language (every human sample usually has its synthetic counterpart).

• 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

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 to train synthetic speech detection. Each sample contains an Audio feature, and a label: human or synthetic.

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",
    "en_us",
    split="train",
    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

To load a different split, change the split value to dev or test.

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 those directories, there is a directory named splits; it contains 1 file per split:

• train.tar.gz
• dev.tar.gz
• test.tar.gz

Those .tar.gz files contain 2 directories:

• human
• synthetic

Each of these directories contain the .wav files for the label (and split). Keep in mind the the two directories can't be merged as they share most of their file names. An identical file name implies a speaker-voice pair, ex. human/123.wav and synthetic/123.wav.

Finally, back to the language directory, it contains 4 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-distribution.csv
  • contains the TTS vendor, TTS name, TTS engine, FLEURS gender and TTS gender for each ID-file name pair
  • useful for tracking what models were used to get specific synthetic 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': 'human/10004088536354799741.wav',
    'array': array([0., 0., 0., ..., 0., 0., 0.]),
    'sampling_rate': 16000
  },
  'label': 'human'
}

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