Datasets:
File size: 2,790 Bytes
4d68066 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | ---
license: cc-by-nc-sa-4.0
language: [ru]
pretty_name: "PyAra — Russian Audio Deepfake Detection"
task_categories: [audio-classification]
size_categories: [100K<n<1M]
configs:
- config_name: default
data_files:
- {split: test, path: "data/test-*.parquet"}
tags:
- anti-spoofing
- audio-deepfake-detection
- speech
- benchmark
- arena-ready
- russian
arxiv: []
---
# PyAra — Russian Audio Deepfake Detection
Benchmark-ready packaging of **PyAra**, a Russian-language audio anti-spoofing
dataset (source: https://www.kaggle.com/datasets/alep079/pyara).
## Overview
Binary classification: **bonafide** (genuine Russian human recordings) vs.
**spoof** (synthetic / converted speech). The spoof side is produced by **five**
generation algorithms (`alg_1` … `alg_5`). Label is the top-level source
directory (`Real/` → bonafide, `Fake/` → spoof), per the `fake` column of
`final_dataset.tsv`.
## License & redistribution
Released under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0
International (CC BY-NC-SA 4.0)** license. Redistribution and adaptation are
permitted for **non-commercial** use with attribution and ShareAlike; this
packaging is itself CC BY-NC-SA 4.0. See `LICENSE.txt`. Audio is the original
16 kHz mono WAV, embedded bit-exactly (no re-encode — a full decode probe of all
clips passed cleanly).
## Schema
| Column | Type | Description |
|--------|------|-------------|
| `path` | `string` | source-relative path, e.g. `Fake/alg_1_0.wav`, unique |
| `audio` | `Audio(16000)` | 16 kHz mono WAV |
| `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) |
| `notes` | `string` | JSON: `utterance_id`, `algorithm`, `gender`, `age`, `length` |
`notes` example:
```json
{"utterance_id": "alg_1_0", "algorithm": "alg_1", "gender": "male", "age": "twenties", "length": "5.55"}
```
`utterance_id` is the source filename stem (`real_12713`, `alg_1_0`) — unique
across the whole set.
## Quick Start
```python
from datasets import load_dataset
ds = load_dataset("SpeechAntiSpoofingBenchmarks/PyAra", split="test")
print(ds[0])
```
## Stats
| Stat | Value |
|------|-------|
| Total trials | 201,778 |
| Bonafide (real) | 73,583 |
| Spoof | 128,195 |
| Spoof algorithms | alg_1 (46,505), alg_2 (10,065), alg_3 (11,165), alg_4 (25,412), alg_5 (35,048) |
| Language | Russian |
| Sample rate | 16 kHz mono |
## Source provenance
- Dataset: PyAra, https://www.kaggle.com/datasets/alep079/pyara
- No associated academic paper.
## Evaluation
For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md).
## Citation
```
PyAra — Russian Audio Deepfake Detection dataset.
https://www.kaggle.com/datasets/alep079/pyara
```
## Maintainer
Contact: k.n.borodin@mtuci.ru
|