Datasets:

keypa
/

common_voice_22

license: apache-2.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*

📀 Common Voice 22.0 — Parquet Repack (Community Version)

⚠️ Unofficial community repackage. Not affiliated with Mozilla.
This dataset is a community-generated Parquet version of the original Mozilla Common Voice 22.0 release.
It was converted to a more training-friendly format to speed up loading, improve compatibility with modern ML frameworks, and support distributed training.

🚀 TL;DR

Original Source: Mozilla Common Voice 22.0 (public domain voice dataset)
This Version: Restructured into Parquet for faster I/O + easier ML training
Why: To make fine-tuning speech models (Whisper, MMS, Wav2Vec2, etc.) less painful
License: Apache-2.0 for this repack only — original audio remains under CC-0 by Mozilla contributors

📚 Dataset Details

📝 What This Is

This is not the official dataset — it’s a re-packaged mirror for convenience & performance.

The raw .tar.gz archives from Mozilla were:

Downloaded from the official Common Voice hosting
Extracted + validated
Converted to .parquet with structured metadata + 16kHz audio
Splits preserved: train, dev, test

🌍 Languages

Currently includes: French (fr) from CV22
(Extendable to other languages if the community wants to contribute)

🤝 Credits

Role	Entity
Original data creators	Mozilla + Common Voice community
Re-packaged by	Community for educational & research use
Affiliation	❌ No affiliation with Mozilla

📂 Dataset Structure

Field	Type	Description
`client_id`	string	Anonymous speaker ID
`path`	string	Audio file path
`sentence_id`	string	Sample unique ID
`sentence`	string	Ground-truth transcription
`sentence_domain`	string	Domain / category of sentence
`up_votes`	string	Community upvotes
`down_votes`	string	Community downvotes
`age`	string	Optional user self-reported
`gender`	string	Optional user self-reported
`accents`	string	Accent info if provided
`variant`	null	Unused
`locale`	string	Locale code
`audio`	audio (16kHz)	Audio object
`segment`	null	Unused

🧠 Intended Uses

✅ Good For

Fine-tuning speech-to-text models (Whisper, Wav2Vec2, MMS, etc.)
ASR evaluation and benchmarking
Research on accents, pronunciation, and speech diversity
Training small and large-scale ASR models efficiently (Parquet = faster)

❌ Not Recommended For

Commercial use without reviewing original Common Voice licensing
Speaker identification or deanonymization research
Training models intended to profile demographic or identity attributes

🧪 Dataset Creation

Why This Exists

Loading CV22 from raw MP3/TSV is slow as hell for modern GPU pipelines.
This repack aims to:

Reduce dataset loading overhead
Improve compatibility with HF datasets / PyTorch / JAX / TPU
Make community fine-tuning more accessible

Source Data Collection

All audio was originally donated by volunteers to Mozilla under CC-0.
This version applies no extra filtering beyond the original release.

⚖️ Personal & Sensitive Info

Contains voice data, which is inherently biometric
Age, gender, and accent are self-reported and optional
All speaker identifiers are anonymized IDs from Mozilla

Users should avoid re-identification or any non-ethical use of voice data.

⚠️ Bias, Risks & Limitations

Age/gender/accent labels may be inaccurate or incomplete
Speech data may not represent all demographics equally
Model trained on this may reflect bias from accents or speaker distribution
Not ideal for extremely low-resource or domain-specific speech tasks

Recommendations

Combine with other datasets for balanced performance
Evaluate ASR models across demographics + accents to detect bias

📎 Citation

If you use this dataset, cite both:

Mozilla Common Voice

@misc{mozilla_common_voice_2023,
  title = {Mozilla Common Voice Dataset},
  howpublished = {https://commonvoice.mozilla.org/},
  year = {2023}
}
@misc{common_voice_22_parquet_community,
  title = {Common Voice 22.0 — Community Parquet Repack},
  year = {2025},
  note = {Unofficial preprocessing for research/education}
}