Duplicate from AudioTuring/Audio-Turing-Test-Audios

.gitattributes

@@ -0,0 +1,59 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,65 @@
+---
+license: cc-by-nc-4.0
+language:
+- zh
+tags:
+- Audio
+- Corpus
+- mlcroissant
+task_categories:
+- text-to-speech
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: trap_audio
+    path: Trap_Audio/audio-*
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: audio
+    dtype: audio
+  download_size: 28587042
+  dataset_size: 29982460
+---
+
+# 📚 Audio Turing Test Audios
+
+> A high‑quality, multidimensional Chinese audio corpus generated from textual transcripts, designed to evaluate the human-likeness and naturalness of Text-to-Speech (TTS) systems—the “Audio Turing Test.”
+
+## About Audio Turing Test (ATT)
+
+ATT is an evaluation framework featuring a standardized human evaluation protocol and an accompanying dataset, addressing the lack of unified evaluation standards in TTS research. To enhance rapid iteration and evaluation, we trained the Auto-ATT model based on Qwen2-Audio-7B, enabling a model-as-a-judge evaluation on the ATT dataset. Full details and related resources are available in the [ATT Collection](https://huggingface.co/collections/AudioTuring/audio-turing-test-6826e24d2197bf91fae6d7f5).
+
+## Dataset Description
+
+The dataset includes 104 "trap" audio clips for attentiveness checks during evaluations:
+
+* **35 flawed synthetic audio clips:** intentionally synthesized to highlight obvious flaws and unnaturalness.
+* **69 authentic human recordings:** genuine human speech, serving as control samples.
+
+## How to Use This Dataset
+
+1. **Evaluate:** Use our [Auto-ATT evaluation model](https://huggingface.co/AudioTuring/Auto-ATT) to score your own or existing TTS audio clips.
+2. **Benchmark:** Compare your evaluation scores against these reference audio samples from top-performing TTS models described in our research paper and these "trap" audio clips.
+
+
+## Data Format
+
+Audio files are provided in high-quality `.wav` format.
+
+## Citation
+
+If you use this dataset, please cite:
+
+```
+@software{Audio-Turing-Test-Audios,
+  author = {Wang, Xihuai and Zhao, Ziyi and Ren, Siyu and Zhang, Shao and Li, Song and Li, Xiaoyu and Wang, Ziwen and Qiu, Lin and Wan, Guanglu and Cao, Xuezhi and Cai, Xunliang and Zhang, Weinan},
+  title = {Audio Turing Test: Benchmarking the Human-likeness and Naturalness of Large Language Model-based Text-to-Speech Systems in Chinese},
+  year = {2025},
+  url = {https://huggingface.co/datasets/AudioTuring/Audio-Turing-Test-Audios},
+  publisher = {huggingface},
+}
+```

Trap_Audio/audio-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00829a420651b2b60810290dcd20923b56d03c3e507487a4c08cc35dc3511a56
+size 28587042