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  1. README.md +82 -0
  2. baseline/dev_all.parquet +3 -0
  3. baseline/dev_en.parquet +3 -0
  4. baseline/dev_zh.parquet +3 -0
  5. baseline/test_all.parquet +3 -0
  6. baseline/test_en.parquet +3 -0
  7. baseline/test_zh.parquet +3 -0
  8. baseline/train_all.parquet +3 -0
  9. baseline/train_en.parquet +3 -0
  10. baseline/train_zh.parquet +3 -0
  11. cross_generator/dev_NV.parquet +3 -0
  12. cross_generator/dev_TTS.parquet +3 -0
  13. cross_generator/dev_VC.parquet +3 -0
  14. cross_generator/test_NV.parquet +3 -0
  15. cross_generator/test_TTS.parquet +3 -0
  16. cross_generator/test_VC.parquet +3 -0
  17. cross_generator/train_NV.parquet +3 -0
  18. cross_generator/train_TTS.parquet +3 -0
  19. cross_generator/train_VC.parquet +3 -0
  20. cross_lingual/dev.parquet +3 -0
  21. cross_lingual/test.parquet +3 -0
  22. cross_lingual/test_en.parquet +3 -0
  23. cross_lingual/test_es.parquet +3 -0
  24. cross_lingual/test_et.parquet +3 -0
  25. cross_lingual/test_fi.parquet +3 -0
  26. cross_lingual/test_gl.parquet +3 -0
  27. cross_lingual/test_he.parquet +3 -0
  28. cross_lingual/test_hu.parquet +3 -0
  29. cross_lingual/test_id.parquet +3 -0
  30. cross_lingual/test_is.parquet +3 -0
  31. cross_lingual/test_it.parquet +3 -0
  32. cross_lingual/test_ko.parquet +3 -0
  33. cross_lingual/test_lv.parquet +3 -0
  34. cross_lingual/test_zh.parquet +3 -0
  35. cross_lingual/train.parquet +3 -0
  36. cross_speaker/test_diff_spk.parquet +3 -0
  37. cross_speaker/test_overlap1.parquet +3 -0
  38. cross_speaker/test_overlap2.parquet +3 -0
  39. cross_speaker/test_same_spk.parquet +3 -0
  40. cross_speaker/train.parquet +3 -0
README.md ADDED
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+ license: apache-2.0
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+
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+ # SpeechFake Dataset
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+
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+ Please use the download scripts from https://github.com/XIAOYixuan/AUDDT/tree/yixuan-dev
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+ to download and process the dataset.
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+
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+ ```
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+ chmod +x download/get_speechfake.sh
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+ ./download/get_speechfake.sh
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+ ```
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+
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+ ## Label Distribution
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+
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+ The dataset is organized into four experiment types:
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+
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+ ### baseline
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+ - **train_all**: 704,862 samples (spoof: 629,154, bonafide: 75,708)
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+ - **train_en**: 428,266 samples (spoof: 389,866, bonafide: 38,400)
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+ - **train_zh**: 276,596 samples (spoof: 239,288, bonafide: 37,308)
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+ - **test_all**: 346,313 samples (spoof: 309,065, bonafide: 37,248)
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+ - **test_en**: 208,655 samples (spoof: 189,455, bonafide: 19,200)
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+ - **test_zh**: 137,658 samples (spoof: 119,610, bonafide: 18,048)
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+ - **dev_all**: 117,463 samples (spoof: 104,845, bonafide: 12,618)
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+ - **dev_en**: 71,370 samples (spoof: 64,970, bonafide: 6,400)
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+ - **dev_zh**: 46,093 samples (spoof: 39,875, bonafide: 6,218)
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+
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+ ### cross_generator
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+ - **train_TTS**: 276,422 samples (all spoof)
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+ - **train_VC**: 192,210 samples (all spoof)
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+ - **train_NV**: 160,522 samples (all spoof)
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+ - **test_TTS**: 132,733 samples (all spoof)
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+ - **test_VC**: 96,059 samples (all spoof)
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+ - **test_NV**: 80,273 samples (all spoof)
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+ - **dev_TTS**: 46,065 samples (all spoof)
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+ - **dev_VC**: 32,031 samples (all spoof)
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+ - **dev_NV**: 26,749 samples (all spoof)
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+
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+ ### cross_lingual
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+ - **train**: 263,399 samples (spoof: 203,399, bonafide: 60,000)
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+ - **test**: 133,707 samples (spoof: 103,707, bonafide: 30,000)
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+ - **test_ko**: 85,228 samples (spoof: 82,728, bonafide: 2,500)
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+ - **test_zh**: 63,283 samples (spoof: 48,283, bonafide: 15,000)
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+ - **test_en**: 70,424 samples (spoof: 55,424, bonafide: 15,000)
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+ - **test_it**: 44,989 samples (spoof: 39,989, bonafide: 5,000)
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+ - **test_hu**: 44,981 samples (spoof: 39,981, bonafide: 5,000)
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+ - **test_id**: 44,904 samples (spoof: 39,936, bonafide: 4,968)
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+ - **test_es**: 48,868 samples (spoof: 43,868, bonafide: 5,000)
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+ - **test_gl**: 44,838 samples (spoof: 39,838, bonafide: 5,000)
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+ - **test_lv**: 37,784 samples (spoof: 32,845, bonafide: 4,939)
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+ - **test_fi**: 36,578 samples (spoof: 31,619, bonafide: 4,959)
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+ - **test_et**: 33,350 samples (spoof: 28,392, bonafide: 4,958)
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+ - **test_he**: 21,210 samples (spoof: 20,605, bonafide: 605)
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+ - **test_is**: 13,388 samples (spoof: 13,373, bonafide: 15)
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+ - **dev**: 44,563 samples (spoof: 34,563, bonafide: 10,000)
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+
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+ ### cross_speaker
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+ - **train**: 34,305 samples (spoof: 27,734, bonafide: 6,571)
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+ - **test_overlap2**: 18,976 samples (spoof: 12,377, bonafide: 6,599)
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+ - **test_same_spk**: 20,470 samples (spoof: 13,871, bonafide: 6,599)
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+ - **test_overlap1**: 19,428 samples (spoof: 13,871, bonafide: 5,557)
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+ - **test_diff_spk**: 17,934 samples (spoof: 12,377, bonafide: 5,557)
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+
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+ ## Data Attributes
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+
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+ ```
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+ ID path label dataset_name
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+ 0 0 Real/LibriTTS/train-clean-100/1841/150351/1841_150351_000026_000002.wav bonafide SpeechFake
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+ 1 1 Real/LibriTTS/train-clean-100/1116/132851/1116_132851_000040_000002.wav bonafide SpeechFake
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+ 2 2 Real/LibriTTS/train-clean-100/83/9960/83_9960_000022_000000.wav bonafide SpeechFake
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+ ```
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+
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+ ## How to Import
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+
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+ ```python
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+ import pandas as pd
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+
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+ # Example: Load baseline train_all split
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+ df = pd.read_parquet("baseline/train_all.parquet")
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+ print(df.head())
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+ ```
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+
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