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--- |
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license: apache-2.0 |
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--- |
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# SpeechFake Dataset |
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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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chmod +x download/get_speechfake.sh |
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./download/get_speechfake.sh |
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``` |
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## Label Distribution |
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The dataset is organized into four experiment types: |
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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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### 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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### 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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### 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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## Data Attributes |
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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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## How to Import |
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```python |
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import pandas as pd |
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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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