--- 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: 61502963330.0 num_examples: 120220 download_size: 51061104488 dataset_size: 61502963330.0 license: cc-by-4.0 task_categories: - text-to-speech - automatic-speech-recognition language: - ca size_categories: - 100K 4.0` are kept. This removes clips with consistently poor subjective or automatic quality ratings. 2. **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. 3. **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 ```bibtex @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 ```bibtex @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](https://arxiv.org/abs/2202.07790) · Code: [NVIDIA/CleanUNet](https://github.com/NVIDIA/CleanUNet) * **FlashSR** — Im & Nam, *"FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation"*, 2025. Paper: [arXiv:2501.10807](https://arxiv.org/abs/2501.10807) · Code: [ysharma3501/FlashSR](https://github.com/ysharma3501/FlashSR) --- ## Acknowledgements * Mozilla Common Voice contributors * Projecte AINA * CleanUNet authors (Kong, Ping, Dantrey & Catanzaro) * FlashSR authors (Jaekwon Im & Juhan Nam)