| --- |
| language: |
| - mn |
| license: cc-by-4.0 |
| task_categories: |
| - automatic-speech-recognition |
| task_ids: [] |
| pretty_name: "FLEURS — Mongolian (Clean)" |
| tags: |
| - mongolian |
| - speech |
| - audio |
| - fleurs |
| --- |
| |
| # FLEURS — Mongolian (Quality-Filtered) |
|
|
| A quality-filtered version of the [FLEURS](https://huggingface.co/datasets/google/fleurs) (`mn_mn`) Mongolian benchmark dataset, cleaned for use with [oron-tts](https://github.com/btseee/oron-tts) (F5-TTS / Flow Matching). |
|
|
| ## Source |
|
|
| Derived from `google/fleurs` config `mn_mn`. FLEURS is the speech version of the FLoRes machine translation benchmark, covering 2,009 n-way parallel sentences across 102 languages. |
|
|
| ## Cleaning Pipeline |
|
|
| 6-stage automated quality filter, thresholds calibrated for Mongolian TTS training (low-resource language; DeepFilterNet denoising applied downstream in oron-tts): |
|
|
| | Stage | Method | Threshold | |
| |---|---|---| |
| | 1. Format normalization | librosa | mono · 16 kHz | |
| | 2. Voice activity detection | Silero VAD | ≥25 % speech frames | |
| | 3. SNR filter | RMS-based SNR | ≥8 dB | |
| | 4. Pitch metadata | CREPE F0 | recorded when available; not a rejection gate | |
| | 5. AI quality score | DNSMOS P.835 | OVR ≥2.2 · SIG ≥2.4 · BAK ≥2.0 | |
| | 6. Full sentence verification | Whisper large-v3 + CER | CER ≤0.35, or ≤0.50 when length ratio is 0.75–1.25 | |
|
|
| Ground truth for sentence verification: `raw_transcription` field. |
| Clips are kept between **1–30 seconds** to match oron-tts training limits. All passing clips are peak-normalized to −1 dBFS and resampled to **24 kHz**. |
|
|
| ## Schema |
|
|
| All original FLEURS fields preserved, plus computed quality metrics: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | int32 | Sample ID | |
| | `num_samples` | int32 | Number of audio samples | |
| | `path` | string | Audio file path | |
| | `audio` | Audio(24000) | Cleaned audio resampled to 24 kHz | |
| | `raw_transcription` | string | Original (unnormalized) transcription | |
| | `transcription` | string | Normalized transcription | |
| | `gender` | int32 | Speaker gender class | |
| | `lang_id` | int32 | Language class ID | |
| | `language` | string | Language name | |
| | `lang_group_id` | int32 | Language group class ID | |
| | `snr_db` | float32 | SNR in dB | |
| | `mean_f0_hz` | float32 | Mean F0 (Hz) | |
| | `pitch_confidence` | float32 | CREPE pitch confidence | |
| | `dnsmos_sig` | float32 | DNSMOS signal quality | |
| | `dnsmos_bak` | float32 | DNSMOS background noise | |
| | `dnsmos_ovr` | float32 | DNSMOS overall MOS | |
| | `dnsmos_p808` | float32 | DNSMOS P.808 MOS | |
| | `cer` | float32 | CER vs. raw_transcription | |
| | `asr_transcript` | string | Whisper large-v3 output | |
| | `duration_s` | float32 | Duration in seconds | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("btsee/fleurs-mn") |
| sample = ds["train"][0] |
| ``` |
|
|
| ## License |
|
|
| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
|
|
