Datasets:
metadata
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 (mn_mn) Mongolian benchmark dataset, cleaned for use with 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
from datasets import load_dataset
ds = load_dataset("btsee/fleurs-mn")
sample = ds["train"][0]