Codex12/TeleAntiFraud-bucket123/Mainbucket - Storage Bucket

TeleAntiFraud

Sanitized public release of the TeleAntiFraud audio-text fraud detection dataset.

This repository contains public metadata splits, audio archives, and a small preview set for quick inspection on the dataset page.

License

Copyright 2025 Zhiming Ma. All rights reserved.

Licensed under the Apache License, Version 2.0.

Overview

TeleAntiFraud is a Chinese audio-text fraud detection dataset designed for:

• binary fraud detection from call audio
• multi-turn audio-text instruction tuning
• speech understanding and fraud-risk reasoning

The public release removes machine-specific paths from the original research environment and normalizes audio references to relative paths.

Contents

• binary_classification.zip
  • train.json: 4,000 binary fraud classification samples
  • test.json: 400 binary fraud classification samples
• sft.zip
  • train.jsonl: 27,146 multi-turn SFT samples
  • test.jsonl: 6,807 multi-turn SFT samples
• audio.zip
  • referenced audio files normalized under audio/...
• dataset_manifest.json
• preview/
  • a few small MP3 examples for quick listening on the Hub page
• viewer/
  • lightweight parquet files used by the Hugging Face dataset viewer

Splits

Package	File	Samples	Description
binary_classification.zip	train.json	4,000	binary call-level fraud classification
binary_classification.zip	test.json	400	binary call-level fraud classification
sft.zip	train.jsonl	27,146	multi-turn SFT data with audio-grounded prompts
sft.zip	test.jsonl	6,807	multi-turn SFT data with audio-grounded prompts

Schema Summary

Binary classification

Each sample keeps a prompt-style structure and a label:

{
  "prompt": [
    {
      "role": "system",
      "content": "..."
    },
    {
      "role": "user",
      "content": [
        {
          "type": "audio",
          "audio_url": "audio/..."
        },
        {
          "type": "text",
          "text": "..."
        }
      ]
    }
  ],
  "answer": "fraud"
}

SFT

Each line in train.jsonl or test.jsonl is a JSON object containing multi-turn messages and audio-grounded prompts for scene understanding, fraud judgment, and related reasoning tasks.

Preview

Small preview files are provided for direct listening without downloading the full audio.zip.

Example	Label	Audio	Notes
normal_example.mp3	normal	link	binary classification sample
fraud_example_1.mp3	fraud	link	binary classification sample
fraud_example_2.mp3	fraud	link	binary classification sample

Preview metadata is also available in preview/preview_samples.json.

Viewer Support

The Hugging Face dataset viewer is configured with lightweight parquet files in viewer/train.parquet and viewer/test.parquet. These files expose a stable preview table with:

• id
• task
• audio_path
• instruction
• label

Sanitization

• Absolute local paths from the original research environment were removed.
• Audio references were normalized to relative paths under audio/.
• The original field structure was kept whenever possible to avoid breaking downstream scripts.

Usage Notes

• This release is packaged as zip archives to make distribution of the audio assets more manageable.
• Audio references inside JSON / JSONL files are relative paths, not absolute local paths.
• If you unpack audio.zip, the metadata files can be used directly with the normalized audio/... paths.
• For project code and evaluation scripts, see the GitHub repository below.

Related Resources

• TeleAntiFraud-28k paper: https://huggingface.co/papers/2503.24115
• GitHub: https://github.com/JimmyMa99/TeleAntiFraud
• Evaluation scripts: https://github.com/JimmyMa99/TeleAntiFraud/tree/main/evaluation
• ModelScope: https://www.modelscope.cn/datasets/JimmyMa99/TeleAntiFraud-28k
• SAFE-QAQ (ACL 2026): https://arxiv.org/abs/2601.01392