Datasets:
Tasks:
Audio Classification
Formats:
parquet
Sub-tasks:
language-identification
Size:
100K - 1M
License:
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Babel-LID (Pilot — Metadata Only)
Open-source multi-duration language identification dataset with acoustic robustness tiers.
Pilot release: 4 languages (en, fr, de, es), 339,328 metadata rows. Real pipeline applied end-to-end on Modal.
Note: This release contains only the metadata (parquets). The 84,816 augmented WAV files (4 tiers × 2 channels × ~10K source files) are stored on the Modal Volume
babel-lid-v2and will be added in a subsequent release once the upload pipeline is optimized.
Stats
| Metric | Value |
|---|---|
| Languages | 4 (en, fr, de, es) |
| Source audios | 10,825 (LibriSpeech + MLS) |
| Verified annotations | 10,604 |
| Augmented WAV files | 84,816 (stored on Modal) |
| Metadata rows | 339,328 |
| Train / Val / Test | 236,544 / 51,936 / 50,848 |
Real Pipeline (all on Modal)
- Stage 1 — Download real speech from LibriSpeech (en) + MLS (fr, de, es)
- Stage 2 — Whisper large-v3 transcription verification (A10G GPU)
- Stage 3 — 3-model LID ensemble: Whisper + ECAPA-TDNN + MMS-LID-256 (A100 GPU)
- 92% high-confidence (3/3 unanimous)
- 5.6% medium-confidence (2/3)
- 2.2% rejected
- Stage 3b — Linguistic cross-verification via Whisper ASR confidence
- Stage 4 — Acoustic augmentation: 4 tiers × 2 channels = 8 variants per source
- Stage 5 — Duration block metadata: D1/D3/D5/D10
Augmentation Tiers
| Tier | Augmentation | Details |
|---|---|---|
| T0 | Clean | Original audio, 16kHz mono |
| T1 | Additive noise | White noise at SNR 5–15 dB |
| T2 | Reverb | Synthetic RIR, RT60 0.3–0.8s |
| T3 | Multi-distortion | Reverb + noise SNR 0–10 dB |
| C0 | Wideband | Full 16kHz bandwidth |
| C1 | PSTN telephony | 300–3400 Hz bandpass → 8kHz → μ-law G.711 → 50Hz hum → 16kHz |
License
CC-BY 4.0
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