README.md

metadata

language:
  - ka
tags:
  - asr
  - georgian
  - common-voice
  - speech
  - quality-control
  - dataset-curation
license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
pretty_name: Common Voice 23 — Georgian ASR Quality Control Dataset
size_categories:
  - 1K<n<10K
citation:
  - >-
    @dataset{giorgadze2025asrge_qc, title={Common Voice 23 — Georgian ASR
    Quality Control Dataset}, author={Giorgi Giorgadze}, year={2025},
    organization={ASR.GE / IRMS}, note={Quality-control annotations generated
    with custom Georgian ASR models.}}

🗣️ Common Voice 23 — Georgian ASR Quality Control Dataset

Overview

This dataset was created as part of a quality control (QC) process for the Mozilla Common Voice Corpus 23 (Georgian subset).
The main goal is to identify corrupted, low-quality, or mislabeled recordings that might have passed through Common Voice validation but are unsuitable for training or evaluation.

🧩 Methodology

• User Selection
  Unique users were selected from the Common Voice 23 Georgian corpus, including only those with at least 3 valid audio samples.

• Automatic Transcription
  Each audio clip was transcribed using custom Georgian ASR models developed at ASR.GE.

• Error Metrics
  The transcriptions were compared to the reference sentences from Common Voice.
  The following metrics were computed for each sample:

  • CER (Character Error Rate)
  • WER (Word Error Rate)

  High CER/WER values often correspond to faulty recordings, such as noise, silence, or non-speech segments.

📊 Dataset Fields

Column	Description
path	Relative path to the original Common Voice audio file
reference	Ground-truth text provided by Common Voice
cer_min	Minimum Character Error Rate across model predictions
wer_min	Minimum Word Error Rate across model predictions

🎯 Purpose and Use Cases

This dataset enables:

• Detection of problematic users or recordings in the Common Voice Georgian subset.
• Improved dataset curation by filtering out noisy or low-quality samples.
• ASR benchmarking on real-world Georgian data.

It can be directly linked with Common Voice 23 (ka) using the path field, which matches the original file structure.

import pandas as pd

qc = pd.read_parquet("preds2_pub.parquet")
print(qc.head())