btsee/fleurs-mn

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]

License

CC BY 4.0