Create README.md

README.md

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+---
+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
+size_categories:
+- 10K<n<100K
+---
+
+# FLEURS-HS
+
+An extension of the [FLEURS](https://huggingface.co/datasets/google/fleurs) dataset for synthetic speech detection using text-to-speech.
+
+## 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](https://cloud.google.com/text-to-speech)
+- [Azure Text-To-Speech](https://azure.microsoft.com/en-us/products/ai-services/text-to-speech)
+- [Amazon Polly](https://aws.amazon.com/polly/)
+
+The resulting dataset features roughly twice the samples per language (every `human` sample usually has its `synthetic` counterpart).
+
+
+- **Curated by:** [KONTXT by RealNetworks](https://realnetworks.com/kontxt)
+- **Funded by:** [RealNetworks](https://realnetworks.com/)
+- **Language(s) (NLP):** English, German, Spanish, French, Italian, Dutch, Polish, Swedish
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) for the code, [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) for the dataset
+
+### Dataset Sources
+
+The original FLEURS dataset was downloaded from [HuggingFace](https://huggingface.co/datasets/google/fleurs).
+
+- **FLEURS Repository:** [HuggingFace](https://huggingface.co/datasets/google/fleurs)
+- **FLEURS Paper:** [arXiv](https://arxiv.org/abs/2205.12446)
+
+- **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:
+
+```python
+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](./fleurs-hs.py).
+
+## Dataset Structure
+
+The dataset data is contained in the [data directory](./data).
+
+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](https://huggingface.co/suflaj)
+
+## Dataset Card Contact
+
+- [Miljenko Šuflaj](mailto:[email protected])