Izzyzlin/CFSDD

--- license: apache-2.0 pretty_name: CFSDD language: - zh tags: - audio - text - speech-deepfake-detection - telecom-fraud size_categories: - 100K<n<1M configs: - config_name: main default: true data_files: - split: train path: data/train/*.parquet - split: dev path: data/dev/*.parquet - split: test path: - data/test_clean/*.parquet - data/test_noise/*.parquet - data/test_ns/*.parquet - data/test_codec/*.parquet dataset_info: - config_name: main features: - name: key dtype: string - name: audio dtype: audio - name: text dtype: string - name: file_path dtype: string - name: speaker dtype: string - name: gender dtype: string - name: method dtype: string - name: label dtype: string --- # CFSDD Dataset ## 📌 Overview CFSDD is a Chinese speech deepfake benchmark for telecom fraud scenarios. Unlike conventional speech deepfake datasets that mainly focus on acoustic authenticity, CFSDD is designed from a risk-oriented perspective and jointly considers both acoustic authenticity and semantic intent. The benchmark explicitly distinguishes real benign speech from fake fraudulent speech, making it suitable for studying speech deepfake detection under realistic telecom-fraud conditions. It contains two classes of samples: - **Real benign speech**: real speech with benign semantic content, drawn from [MagicData-RAMC](https://github.com/MagicHub-io/MagicData-RAMC) - **Fake fraudulent speech**: synthesized speech with fraudulent content, whose transcripts are derived from [TeleAntiFraud-28k](https://github.com/JimmyMa99/TeleAntiFraud) ## 🗂️ Split Organization This repository is organized into three dataset splits: - `train`: training split - `dev`: development split - `test`: test split The `test` split aggregates four evaluation subsets: - `test_clean`: clean test data - `test_noise`: output after **noise addition** - `test_ns`: output after **noise suppression** - `test_codec`: output after **codec processing** This organization follows the disturbance-oriented evaluation protocol described in the paper. The clean test data are further expanded through a sequential pipeline consisting of **noise addition**, **noise suppression**, and **codec processing** to better approximate realistic voice-call conditions. To simulate realistic call conditions, the disturbance-oriented test subsets use the following external resources: - `Noise addition`: [Audioset](https://research.google.com/audioset), [Freesound](https://freesound.org/), [RIR](http://www.openslr.org/28/) (following [ICASSP 2023 Deep Noise Suppression Challenge](https://github.com/microsoft/dns-challenge)) - `Noise suppression`: [DeepFilterNet](https://github.com/Rikorose/DeepFilterNet) - `Codec processing`: [Opus](https://github.com/xiph/opus) ## 📊 Dataset Statistics CFSDD contains 766 hours of speech in total, with 394,908 utterances, 663 speakers, an average utterance duration of 6.98 seconds, and 10 TTS systems. The speakers in the train, development, and test splits are strictly disjoint. Five TTS systems appear in all splits, while the remaining five are reserved for the test split to evaluate generalization to unseen generators. **The detailed statistics for each split:** <div align="left"> <table> <thead> <tr> <th>Split</th> <th>Duration (h)</th> <th># Utterances</th> <th># Speakers</th> <th># Systems</th> </tr> </thead> <tbody> <tr> <td>Train</td> <td align="right">139</td> <td align="right">74,737</td> <td align="right">300</td> <td align="right">5</td> </tr> <tr> <td>Dev</td> <td align="right">49</td> <td align="right">25,893</td> <td align="right">100</td> <td align="right">5</td> </tr> <tr> <td>Test</td> <td align="right">578</td> <td align="right">294,278</td> <td align="right">263</td> <td align="right">10</td> </tr> <tr> <td>Total</td> <td align="right">766</td> <td align="right">394,908</td> <td align="right">663</td> <td align="right">10</td> </tr> </tbody> </table> </div> **Distribution by class, gender, and test conditions:** <p align="left"> <img src="assets/distribution1.png" alt="CFSDD distribution" width="450"> </p> **Distribution of TTS systems and utterance durations:** <p align="left"> <img src="assets/distribution2.png" alt="CFSDD generation and duration statistics" width="450"> </p> ## 💻 Example Usage ```python from datasets import load_dataset ds = load_dataset("Izzyzlin/CFSDD", "main") train_ds = ds["train"] dev_ds = ds["dev"] test_ds = ds["test"] print(train_ds[0]) print(dev_ds[0]) print(test_ds[0]) test_clean = test_ds.filter(lambda x: "test_clean" in x["key"]) test_noise = test_ds.filter(lambda x: "test_noise" in x["key"]) test_ns = test_ds.filter(lambda x: "test_ns" in x["key"]) test_codec = test_ds.filter(lambda x: "test_codec" in x["key"]) print(test_clean[0]) print(test_noise[0]) print(test_ns[0]) print(test_codec[0]) ``` ## 🙏 Acknowledgements CFSDD is built on top of valuable public resources. If you use this dataset, please also consider citing the original data sources and the TTS systems used to construct the fake fraudulent speech. ### 📚 Data Sources ```bibtex @article{yang2022open, title={Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational (RAMC) Speech Dataset}, author={Yang, Zehui and Chen, Yifan and Luo, Lei and Yang, Runyan and Ye, Lingxuan and Cheng, Gaofeng and Xu, Ji and Jin, Yaohui and Zhang, Qingqing and Zhang, Pengyuan and others}, journal={arXiv preprint arXiv:2203.16844}, year={2022} } @inproceedings{ma2025teleantifraud, title={TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection}, author={Ma, Zhiming and Wang, Peidong and Huang, Minhua and Wang, Jinpeng and Wu, Kai and Lv, Xiangzhao and Pang, Yachun and Yang, Yin and Tang, Wenjie and Kang, Yuchen}, booktitle={Proceedings of the 33rd ACM International Conference on Multimedia}, pages={5853--5862}, year={2025} } ``` ### 🤖 TTS Systems ```bibtex @inproceedings{chen2025f5, title={F5-tts: A fairytaler that fakes fluent and faithful speech with flow matching}, author={Chen, Yushen and Niu, Zhikang and Ma, Ziyang and Deng, Keqi and Wang, Chunhui and JianZhao, JianZhao and Yu, Kai and Chen, Xie}, booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={6255--6271}, year={2025} } @article{voxcpm2025, title={VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning}, author={Zhou, Yixuan and Zeng, Guoyang and Liu, Xin and Li, Xiang and Yu, Renjie and Wang, Ziyang and Ye, Runchuan and Sun, Weiyue and Gui, Jiancheng and Li, Kehan and Wu, Zhiyong and Liu, Zhiyuan}, journal={arXiv preprint arXiv:2509.24650}, year={2025} } @article{zhu2025zipvoice, title={Zipvoice: Fast and high-quality zero-shot text-to-speech with flow matching}, author={Zhu, Han and Kang, Wei and Yao, Zengwei and Guo, Liyong and Kuang, Fangjun and Li, Zhaoqing and Zhuang, Weiji and Lin, Long and Povey, Daniel}, journal={arXiv preprint arXiv:2506.13053}, year={2025} } @article{zhou2025indextts2, title={IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech}, author={Zhou, Siyi and Zhou, Yiquan and He, Yi and Zhou, Xun and Wang, Jinchao and Deng, Wei and Shu, Jingchen}, journal={arXiv preprint arXiv:2506.21619}, year={2025} } @article{wang2025spark, title={Spark-tts: An efficient llm-based text-to-speech model with single-stream decoupled speech tokens}, author={Wang, Xinsheng and Jiang, Mingqi and Ma, Ziyang and Zhang, Ziyu and Liu, Songxiang and Li, Linqin and Liang, Zheng and Zheng, Qixi and Wang, Rui and Feng, Xiaoqin and others}, journal={arXiv preprint arXiv:2503.01710}, year={2025} } @article{du2025cosyvoice, title={CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training}, author={Du, Zhihao and Gao, Changfeng and Wang, Yuxuan and Yu, Fan and Zhao, Tianyu and Wang, Hao and Lv, Xiang and Wang, Hui and Shi, Xian and An, Keyu and others}, journal={arXiv preprint arXiv:2505.17589}, year={2025} } @article{cui2025glm, title={Glm-tts technical report}, author={Cui, Jiayan and Yang, Zhihan and Li, Naihan and Tian, Jiankun and Ma, Xingyu and Zhang, Yi and Chen, Guangyu and Yang, Runxuan and Cheng, Yuqing and Zhou, Yizhi and others}, journal={arXiv preprint arXiv:2512.14291}, year={2025} } @article{hu2026qwen3, title={Qwen3-TTS Technical Report}, author={Hu, Hangrui and Zhu, Xinfa and He, Ting and Guo, Dake and Zhang, Bin and Wang, Xiong and Guo, Zhifang and Jiang, Ziyue and Hao, Hongkun and Guo, Zishan and others}, journal={arXiv preprint arXiv:2601.15621}, year={2026} } @article{xie2025fireredtts, title={Fireredtts-2: Towards long conversational speech generation for podcast and chatbot}, author={Xie, Kun and Shen, Feiyu and Li, Junjie and Xie, Fenglong and Tang, Xu and Hu, Yao}, journal={arXiv preprint arXiv:2509.02020}, year={2025} } @article{liao2026fish, title={Fish Audio S2 Technical Report}, author={Liao, Shijia and Wang, Yuxuan and Liu, Songting and Cheng, Yifan and Zhang, Ruoyi and Li, Tianyu and Li, Shidong and Zheng, Yisheng and Liu, Xingwei and Wang, Qingzheng and others}, journal={arXiv preprint arXiv:2603.08823}, year={2026} } ```license: apache-2.0 pretty_name: CFSDD language: - zh tags: - audio - text - speech-deepfake-detection - telecom-fraud size_categories: - 100K<n<1M configs: - config_name: main default: true data_files: - split: train path: data/train/*.parquet - split: dev path: data/dev/*.parquet - split: test path: - data/test_clean/*.parquet - data/test_noise/*.parquet - data/test_ns/*.parquet - data/test_codec/*.parquet dataset_info: - config_name: main features: - name: key dtype: string - name: audio dtype: audio - name: text dtype: string - name: file_path dtype: string - name: speaker dtype: string - name: gender dtype: string - name: method dtype: string - name: label dtype: string # CFSDD 数据集 ## 📌 数据集概述 CFSDD是面向电信诈骗场景的中文语音深度伪造基准数据集。与传统语音深度伪造数据集仅聚焦声学真实性不同，CFSDD从风险导向视角出发，同时兼顾声学真实性与语义意图。该基准数据集明确区分真实良性语音与伪造诈骗语音，适用于真实电信诈骗场景下的语音深度伪造检测研究。 数据集包含两类样本： - **真实良性语音**：源自[MagicData-RAMC](https://github.com/MagicHub-io/MagicData-RAMC)的带有良性语义内容的真实语音 - **伪造诈骗语音**：带有诈骗语义内容的合成语音，其文本转录内容源自[TeleAntiFraud-28k](https://github.com/JimmyMa99/TeleAntiFraud) ## 🗂️ 拆分组织 本数据集仓库分为三个拆分子集： - `train`：训练集 - `dev`：开发集 - `test`：测试集 `test`集包含四个评估子子集： - `test_clean`：干净测试数据 - `test_noise`：经过**加噪处理**后的测试数据 - `test_ns`：经过**噪声抑制**后的测试数据 - `test_codec`：经过**编解码（codec）处理**后的测试数据 该拆分方式遵循论文中提出的面向干扰的评估协议。为更贴近真实语音通话场景，干净测试数据通过**加噪、噪声抑制、编解码**的串行处理流程进行扩展。 为模拟真实通话场景，面向干扰的测试子集使用了以下外部资源： - **加噪处理**：使用[Audioset](https://research.google.com/audioset)、[Freesound](https://freesound.org/)、[RIR](http://www.openslr.org/28/)数据集（遵循[ICASSP 2023深度噪声抑制挑战赛](https://github.com/microsoft/dns-challenge)的规范） - **噪声抑制**：使用[DeepFilterNet](https://github.com/Rikorose/DeepFilterNet)工具 - **编解码处理**：使用[Opus](https://github.com/xiph/opus)编解码器 ## 📊 数据集统计信息 CFSDD总计包含766小时语音数据，共394908条语音片段，涉及663名说话人，单条语音平均时长为6.98秒，涵盖10款文本转语音（Text-to-Speech, TTS）系统。训练集、开发集与测试集的说话人严格互斥。其中5款TTS系统在所有拆分中均有覆盖，剩余5款仅用于测试集，用于评估模型对未见过的生成器的泛化能力。 **各拆分子集的详细统计信息：** <div align="left"> <table> <thead> <tr> <th>拆分子集</th> <th>时长（小时）</th> <th>语音片段数</th> <th>说话人数量</th> <th>TTS系统数量</th> </tr> </thead> <tbody> <tr> <td>训练集</td> <td align="right">139</td> <td align="right">74,737</td> <td align="right">300</td> <td align="right">5</td> </tr> <tr> <td>开发集</td> <td align="right">49</td> <td align="right">25,893</td> <td align="right">100</td> <td align="right">5</td> </tr> <tr> <td>测试集</td> <td align="right">578</td> <td align="right">294,278</td> <td align="right">263</td> <td align="right">10</td> </tr> <tr> <td>总计</td> <td align="right">766</td> <td align="right">394,908</td> <td align="right">663</td> <td align="right">10</td> </tr> </tbody> </table> </div> **按类别、性别与测试条件的分布：** <p align="left"> <img src="assets/distribution1.png" alt="CFSDD 数据分布" width="450"> </p> **TTS系统与语音片段时长分布：** <p align="left"> <img src="assets/distribution2.png" alt="CFSDD 生成器与时长统计" width="450"> </p> ## 💻 示例用法 python from datasets import load_dataset ds = load_dataset("Izzyzlin/CFSDD", "main") train_ds = ds["train"] dev_ds = ds["dev"] test_ds = ds["test"] print(train_ds[0]) print(dev_ds[0]) print(test_ds[0]) test_clean = test_ds.filter(lambda x: "test_clean" in x["key"]) test_noise = test_ds.filter(lambda x: "test_noise" in x["key"]) test_ns = test_ds.filter(lambda x: "test_ns" in x["key"]) test_codec = test_ds.filter(lambda x: "test_codec" in x["key"]) print(test_clean[0]) print(test_noise[0]) print(test_ns[0]) print(test_codec[0]) ## 🙏 致谢 CFSDD基于诸多宝贵的公开资源构建。若您使用本数据集，请同时引用原始数据源与用于生成伪造诈骗语音的TTS系统相关文献。 ### 📚 数据源 bibtex @article{yang2022open, title={Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational (RAMC) Speech Dataset}, author={Yang, Zehui and Chen, Yifan and Luo, Lei and Yang, Runyan and Ye, Lingxuan and Cheng, Gaofeng and Xu, Ji and Jin, Yaohui and Zhang, Qingqing and Zhang, Pengyuan and others}, journal={arXiv preprint arXiv:2203.16844}, year={2022} } @inproceedings{ma2025teleantifraud, title={TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection}, author={Ma, Zhiming and Wang, Peidong and Huang, Minhua and Wang, Jinpeng and Wu, Kai and Lv, Xiangzhao and Pang, Yachun and Yang, Yin and Tang, Wenjie and Kang, Yuchen}, booktitle={Proceedings of the 33rd ACM International Conference on Multimedia}, pages={5853--5862}, year={2025} } ### 🤖 TTS系统 bibtex @inproceedings{chen2025f5, title={F5-tts: A fairytaler that fakes fluent and faithful speech with flow matching}, author={Chen, Yushen and Niu, Zhikang and Ma, Ziyang and Deng, Keqi and Wang, Chunhui and JianZhao, JianZhao and Yu, Kai and Chen, Xie}, booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={6255--6271}, year={2025} } @article{voxcpm2025, title={VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning}, author={Zhou, Yixuan and Zeng, Guoyang and Liu, Xin and Li, Xiang and Yu, Renjie and Wang, Ziyang and Ye, Runchuan and Sun, Weiyue and Gui, Jiancheng and Li, Kehan and Wu, Zhiyong and Liu, Zhiyuan}, journal={arXiv preprint arXiv:2509.24650}, year={2025} } @article{zhu2025zipvoice, title={Zipvoice: Fast and high-quality zero-shot text-to-speech with flow matching}, author={Zhu, Han and Kang, Wei and Yao, Zengwei and Guo, Liyong and Kuang, Fangjun and Li, Zhaoqing and Zhuang, Weiji and Lin, Long and Povey, Daniel}, journal={arXiv preprint arXiv:2506.13053}, year={2025} } @article{zhou2025indextts2, title={IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech}, author={Zhou, Siyi and Zhou, Yiquan and He, Yi and Zhou, Xun and Wang, Jinchao and Deng, Wei and Shu, Jingchen}, journal={arXiv preprint arXiv:2506.21619}, year={2025} } @article{wang2025spark, title={Spark-tts: An efficient llm-based text-to-speech model with single-stream decoupled speech tokens}, author={Wang, Xinsheng and Jiang, Mingqi and Ma, Ziyang and Zhang, Ziyu and Liu, Songxiang and Li, Linqin and Liang, Zheng and Zheng, Qixi and Wang, Rui and Feng, Xiaoqin and others}, journal={arXiv preprint arXiv:2503.01710}, year={2025} } @article{du2025cosyvoice, title={CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training}, author={Du, Zhihao and Gao, Changfeng and Wang, Yuxuan and Yu, Fan and Zhao, Tianyu and Wang, Hao and Lv, Xiang and Wang, Hui and Shi, Xian and An, Keyu and others}, journal={arXiv preprint arXiv:2505.17589}, year={2025} } @article{cui2025glm, title={Glm-tts technical report}, author={Cui, Jiayan and Yang, Zhihan and Li, Naihan and Tian, Jiankun and Ma, Xingyu and Zhang, Yi and Chen, Guangyu and Yang, Runxuan and Cheng, Yuqing and Zhou, Yizhi and others}, journal={arXiv preprint arXiv:2512.14291}, year={2025} } @article{hu2026qwen3, title={Qwen3-TTS Technical Report}, author={Hu, Hangrui and Zhu, Xinfa and He, Ting and Guo, Dake and Zhang, Bin and Wang, Xiong and Guo, Zhifang and Jiang, Ziyue and Hao, Hongkun and Guo, Zishan and others}, journal={arXiv preprint arXiv:2601.15621}, year={2026} } @article{xie2025fireredtts, title={Fireredtts-2: Towards long conversational speech generation for podcast and chatbot}, author={Xie, Kun and Shen, Feiyu and Li, Junjie and Xie, Fenglong and Tang, Xu and Hu, Yao}, journal={arXiv preprint arXiv:2509.02020}, year={2025} } @article{liao2026fish, title={Fish Audio S2 Technical Report}, author={Liao, Shijia and Wang, Yuxuan and Liu, Songting and Cheng, Yifan and Zhang, Ruoyi and Li, Tianyu and Li, Shidong and Zheng, Yisheng and Liu, Xingwei and Wang, Qingzheng and others}, journal={arXiv preprint arXiv:2603.08823}, year={2026} }