| --- |
| license: cc-by-4.0 |
| language: [en] |
| pretty_name: CD-ADD |
| task_categories: [audio-classification] |
| size_categories: [10K<n<100K] |
| configs: |
| - config_name: default |
| data_files: |
| - {split: test, path: "data/test-*.parquet"} |
| tags: |
| - anti-spoofing |
| - audio-deepfake-detection |
| - speech |
| - benchmark |
| - arena-ready |
| paperswithcode_id: |
| arxiv: |
| - "2404.04904" |
| --- |
| |
| # CD-ADD |
|
|
| Benchmark-ready packaging of **CD-ADD** (Cross-Domain Audio Deepfake Detection) for |
| speech anti-spoofing / synthetic-voice detection. |
|
|
| ## Overview |
|
|
| CD-ADD pairs genuine speech with deepfakes produced by five advanced zero-shot |
| text-to-speech systems (OpenVoice, VALL-E, WhisperSpeech, YourTTS, Seamless). This |
| repo packages the **LibriTTS `test-clean`** subset together with **all TED talks** |
| from CD-ADD into a single dataset. The task is binary classification: **bonafide** |
| (genuine human speech) vs. **spoof** (zero-shot-TTS synthesized speech). Source |
| paper: https://arxiv.org/abs/2404.04904. |
|
|
| ## License & redistribution |
|
|
| Redistributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** |
| license; the full text is in `LICENSE.txt`. Audio is the original 16 kHz mono signal |
| losslessly re-encoded to FLAC (bit-exact samples); each `real` clip is labelled |
| bonafide and every TTS-generated clip is labelled spoof. |
|
|
| ## Schema |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | path | string | Source-relative path, unique within the dataset (e.g. `dataset_LibriTTS/test-clean/2300/131720/2300_131720_000002_000001/valle.wav`). | |
| | audio | Audio(16kHz mono) | Embedded 16 kHz mono audio. | |
| | label | ClassLabel[bonafide, spoof] | Index 0 = bonafide, 1 = spoof. | |
| | notes | string (JSON) | Contains a unique `utterance_id`, plus `source` (`libritts`/`ted`), `system` (`real`/`openvoice`/`valle`/`whisperSpeech`/`yourTTS`/`seamless`), `speaker_id`, and `transcript` when available. | |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("SpeechAntiSpoofingBenchmarks/CD-ADD", split="test") |
| ``` |
|
|
| ## Stats |
|
|
| | n_total | n_bonafide | n_spoof | total duration | |
| |---|---|---|---| |
| | 20,786 | 3,661 | 17,125 | ~58.4 h | |
| |
| ## Source provenance |
| |
| Built from the CD-ADD release (LibriTTS `test-clean` + all TED talks). Each source |
| utterance directory contributes one `real.wav` (bonafide) and up to five |
| zero-shot-TTS spoofs. LibriTTS `dev-clean` / `train-clean-100` are excluded. Every |
| decodable clip is included; clips that fail to decode via `soundfile` are dropped |
| (none were dropped in this build). |
| |
| ## Evaluation |
| |
| See `eval.yaml` and `submissions/README.md`. Primary metric: **EER (%)**, lower is |
| better. |
| |
| ## Citation |
| |
| **Original paper**: https://arxiv.org/abs/2404.04904 |
| |
| ```bibtex |
| @article{li2024crossdomain, |
| title = {Cross-Domain Audio Deepfake Detection: Dataset and Analysis}, |
| author = {Li, Yuang and Zhang, Min and Ren, Mengxin and Ma, Miaomiao and |
| Wei, Daimeng and Yang, Hao}, |
| journal = {arXiv preprint arXiv:2404.04904}, |
| year = {2024} |
| } |
| ``` |
| |
| ## Maintainer |
| |
| SpeechAntiSpoofingBenchmarks — contact k.n.borodin@mtuci.ru |
| |
