Datasets:

ebellob
/

annotated_catalan_common_voice_v17_cleaned_enhanced

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: client_id
      dtype: large_string
    - name: path
      dtype: large_string
    - name: sentence_id
      dtype: large_string
    - name: sentence
      dtype: large_string
    - name: sentence_domain
      dtype: large_string
    - name: up_votes
      dtype: int64
    - name: down_votes
      dtype: int64
    - name: age
      dtype: large_string
    - name: gender
      dtype: large_string
    - name: accents
      dtype: large_string
    - name: variant
      dtype: large_string
    - name: locale
      dtype: large_string
    - name: segment
      dtype: large_string
    - name: mean quality
      dtype: float64
    - name: stdev quality
      dtype: float64
    - name: annotated_accent
      dtype: large_string
    - name: annotated_accent_agreement
      dtype: large_string
    - name: annotated_gender
      dtype: large_string
    - name: annotated_gender_agreement
      dtype: large_string
    - name: propagated_gender
      dtype: large_string
    - name: propagated_accents
      dtype: large_string
    - name: propagated_accents_norm
      dtype: large_string
    - name: variant_norm
      dtype: large_string
    - name: assigned_accent
      dtype: large_string
    - name: assigned_gender
      dtype: large_string
    - name: quality
      dtype: float64
    - name: audio
      dtype:
        audio:
          sampling_rate: 48000
  splits:
    - name: train
      num_bytes: 61502963330
      num_examples: 120220
  download_size: 51061104488
  dataset_size: 61502963330
license: cc-by-4.0
task_categories:
  - text-to-speech
  - automatic-speech-recognition
language:
  - ca
size_categories:
  - 100K<n<1M
tags:
  - audio
pretty_name: Processed Annotated Catalan Common Voice v17 (CleanUNet + FlashSR)

Processed Annotated Catalan Common Voice v17 (CleanUNet + FlashSR)

Dataset Summary

This dataset is a processed and enhanced version of: projecte-aina/annotated_catalan_common_voice_v17. Furthermore, as this is a personal project, we give no guarantees that the audio is completely clean from any artifacts or noise the CleanUNet model could not remove. However, we have personally tested the corpus via the fine-tuning of some SOTA speech models and the results have been satisfactory.

It has been created to support high-quality speech research, particularly for:

Text-to-Speech (TTS)
Automatic Speech Recognition (ASR)
Speech enhancement and robustness studies

due to the lack of big and clean speech datasets in Catalan. This work is a derivation of the original Common Voice 17 dataset.

Find samples in: https://erikupv.github.io/CleanUNet-FlashSR-samples/

Main processing steps

Quality filtering Only samples with mean_quality > 4.0 are kept. This removes clips with consistently poor subjective or automatic quality ratings.
Speech denoising (CleanUNet) All audio samples are processed using CleanUNet, a neural speech enhancement model, to reduce background noise and artifacts while preserving speech content.
Audio super-resolution (FlashSR) Enhanced audio is further processed using FlashSR to improve temporal and spectral resolution, resulting in cleaner and more detailed waveforms suitable for modern TTS and ASR pipelines.

The metadata structure and annotations are preserved from the original dataset unless stated otherwise.

Dataset Structure

Configurations

This dataset currently provides a single train split.

Dataset Features

client_id: Speaker identifier
path: Relative audio file path
sentence_id: Sentence identifier
sentence: Spoken sentence text
sentence_domain: Domain/category of the sentence
up_votes / down_votes: Validation votes
age: Speaker age group
gender: Self-reported gender
accents: Self-reported accent
variant / variant_norm: Language variant
locale: Locale identifier
segment: Segment information
mean_quality / stdev_quality: Quality statistics (float)
annotated_*: Annotator-provided metadata
propagated_*: Automatically propagated metadata
assigned_accent / assigned_gender: Final assigned labels
quality: Numeric quality score
audio: Enhanced audio waveform

Dataset Size

Number of examples: 120,220
Download size: ~53.3 GB

Language

Catalan (ca)

Intended Uses

This dataset is suitable for:

High-quality Catalan TTS training
ASR model training and evaluation
Speech enhancement and super-resolution research
Accent- and gender-aware speech modeling
Robustness studies on enhanced speech data

Out-of-Scope Uses

Speaker identification or biometric profiling
Any attempt to deanonymize speakers
Uses violating the original Common Voice license or contributor consent

Processing Notes

Audio waveforms are not bit-identical to the original Common Voice release.
Speech content and textual annotations are unchanged.
Processing may introduce minor spectral or temporal artifacts inherent to neural enhancement models.

License

This dataset inherits the license of the original dataset:

Creative Commons Attribution 4.0 (CC-BY-4.0)

Please refer to the original dataset page for exact license terms and attribution requirements.

Citation

If you use this dataset, please cite both the original dataset and this processed version.

Original dataset

@dataset{aina_annotated_catalan_common_voice_v17,
  title = {Annotated Catalan Common Voice v17},
  author = {Projecte AINA},
  year = {2024},
  url = {https://huggingface.co/datasets/projecte-aina/annotated_catalan_common_voice_v17},
  doi = {10.57967/hf/5679}
}

This dataset

@dataset{processed_ca_common_voice_v17,
  title = {Processed Annotated Catalan Common Voice v17 (CleanUNet + FlashSR)},
  author = {Erik Beltrán},
  year = {2026},
  note = {Enhanced with CleanUNet denoising and FlashSR super-resolution}
}

Tools used

CleanUNet — Kong et al., "Speech Denoising in the Waveform Domain with Self-Attention", ICASSP 2022.
Paper: arXiv:2202.07790 · Code: NVIDIA/CleanUNet
FlashSR — Im & Nam, "FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation", 2025.
Paper: arXiv:2501.10807 · Code: ysharma3501/FlashSR

Acknowledgements

Mozilla Common Voice contributors
Projecte AINA
CleanUNet authors (Kong, Ping, Dantrey & Catanzaro)
FlashSR authors (Jaekwon Im & Juhan Nam)