NMikka/Common-Voice-Geo-Cleaned

Common Voice Georgian — Cleaned for TTS/STT

A high-quality subset of Mozilla Common Voice Georgian cleaned and filtered specifically for text-to-speech fine-tuning.

Dataset Summary

Total samples	21,421
Total duration	35.0 hours
Speakers	12
Sample rate	24 kHz mono WAV
Language	Georgian (kat)
Source	Mozilla Common Voice 19.0
License	CC-0 (public domain)

Splits

Split	Samples	Description
train	20,300	Training data
eval	1,001	Validation data
test	120	Best quality speaker references (top NISQA scores)

Quality Pipeline

The dataset was cleaned from ~71K raw Common Voice recordings through a 6-stage pipeline:

1. Standardize — Resample to 24 kHz mono, normalize loudness to −23 LUFS, filter duration to [0.5s, 30s]
2. Enhance — VoiceFixer audio restoration + Sox spectral noise subtraction
3. NISQA Filter — NISQA MOS ≥ 3.0 (neural speech quality assessment)
4. Duration Outlier — IQR-based character duration filter (removes misaligned/rushed/slow speech)
5. Transcript Verify — Round-trip ASR (Meta Omnilingual 7B, 1.9% CER on Georgian) with CER ≤ 0.20 threshold
6. Speaker Select — Keep speakers with ≥ 1800 seconds total audio

Fields

Field	Type	Description
id	string	Common Voice clip ID
audio	Audio	24 kHz mono WAV
text	string	Georgian transcript
speaker_id	string	Anonymized speaker ID (0–11)
duration	float	Duration in seconds

Speaker Distribution

Speaker	Samples	Duration
0	5,683	8.8h
1	1,164	1.8h
2	2,970	5.3h
3	3,240	5.3h
4	2,595	3.6h
5	1,556	2.8h
6	1,131	1.8h
7	1,130	2.1h
8	470	0.8h
9	544	1.0h
10	607	1.0h
11	331	0.7h

Statistics

• Duration: min 2.4s, mean 5.9s, max 10.6s

Usage

from datasets import load_dataset

ds = load_dataset("NMikka/Common-Voice-Geo-Cleaned")

# Training
for sample in ds["train"]:
    print(sample["text"], sample["duration"])

# Validation
for sample in ds["eval"]:
    print(sample["text"])

# Best speaker references (for TTS inference/voice cloning)
for sample in ds["test"]:
    print(sample["text"], sample["speaker_id"])

Citation

If you use this dataset, please cite Mozilla Common Voice:

@inproceedings{ardila2020common,
  title={Common Voice: A Massively-Multilingual Speech Corpus},
  author={Ardila, Rosana and others},
  booktitle={LREC},
  year={2020}
}