README.md

---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- ca
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: openslr
task_categories:
- text-to-speech
task_ids: []
pretty_name: openslr-slr69-ca-reviewed
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: transcription
    dtype: string
  - name: speaker_id
    dtype: string
  splits:
  - name: train
    num_bytes: 3639831130.625
    num_examples: 12435
  download_size: 3331720350
  dataset_size: 3639831130.625
---
# Dataset Card for festcat_trimmed_denoised

This is a post-processed version of the Catalan Festcat speech dataset. 

The original data can be found [here](http://festcat.talp.cat/ca/download-legacy.php).

Same license is maintained: [Creative Commons Attribution-ShareAlike 3.0 Spain License](http://creativecommons.org/licenses/by-sa/3.0/es/).

## Dataset Details

### Dataset Description

We processed the data of the Catalan Festcat with the following recipe:

- **Trimming:** Long silences from the start and the end of clips have been removed.
  - [py-webrtcvad](https://pypi.org/project/webrtcvad/) -> Python interface to the Voice Activity Detector (VAD) developed by Google for the WebRTC.
- **Resampling:** From 48000 Hz to 22050 Hz, which is the most common sampling rate for training TTS models
  - Resampler from [CoquiTTS](https://github.com/coqui-ai/TTS/tree/dev) framework
- **Denoising:** Although base quality of the audios is high, we could remove some background noise and small artifcats thanks to the CleanUNet denoiser developed by NVIDIA.
  - [CleanUNet](https://github.com/NVIDIA/CleanUNet) - [arXiv](https://arxiv.org/abs/2202.07790)

We kept the same number of samples, filenames, also the original anonymized speaker IDs and transcriptions. 
Our dataset version, after trimming, accumulates a total of 22.91h (divided by speaker IDs) as follows:

- bet (female): 0.97h
- mar (female): 0.96h
- pol (male): 0.97h
- eli (female): 0.99
- ona (female): 6.86h
- teo (male): 0.80h
- eva (female): 1.11h
- pau (male): 7.32h
- uri (male): 1.04h
- jan (male): 0.98h
- pep (male): 0.91h


## Uses

The purpose of this dataset is mainly for training text-to-speech and automatic speech recognition models in Catalan.

### Languages

The dataset is in Catalan (`ca-ES`).


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset consists of a single split, providing audios and transcriptions:
```
DatasetDict({
    train: Dataset({
        features: ['audio', 'transcription'],
        num_rows: 12435
    })
})
```
Each data point is structured as:
```
>> data['train'][0]['audio']

{'path': 'upc_ca_eli_204478.wav', 'array': array([ 0.00000000e+00,  0.00000000e+00, -3.05175781e-05, ...,
        0.00000000e+00,  0.00000000e+00, -3.05175781e-05]), 'sampling_rate': 22050}

>> data['train'][0]['transcription']

"Què potser el seu fill tenia l'endemà el matí lliure? Si era el cas, el podia convidar a jugar una partideta de golf."
```

### Dataset Splits

- <u>```audio (dict)```</u>: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus, it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].
 
  * path (str): The path to the audio file.
  * array (array): Decoded audio array.
  * sampling_rate (int): Audio sampling rate.


- <u>```transcription (str)```</u>: The sentence the user was prompted to speak.


## Dataset Creation

### Source Data

*FestCat: Speech Synthesis in Catalan using Festival*

The goal of this dataset is to provide a Catalan Speech Corpora. This corpora
is needed to produce quality synthetic voices in Catalan language. The main propouse of this
voices will be to be used in future voice synthesis applications.
This project has been developed by the Universitat Politècnica de Catalunya (UPC) within
the Speech Technology Department (TSC), in the TALP Research Center. This project is included
in the TALP’s FestCat project, which principal objective is to produce an open and high quality
voice synthesizer for Catalan.

The data set has been manually quality checked, but there might still be errors.

Please report any issues in the following issue tracker on GitHub. https://github.com/FestCat/festival-ca/issues

The original dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
See [LICENSE](https://github.com/FestCat/festival-ca/blob/upstream/LICENSE-gpl-2.0.txt) and [LICENSE](https://github.com/FestCat/festival-ca/blob/upstream/LICENSE-lgpl-2.1.txt) files as well as 
[https://github.com/google/language-resources#license](https://github.com/FestCat/festival-ca) for license information under License.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
This is a post-processed version of the Catalan [FestCat](http://festcat.talp.cat/download.php) dataset. 
For more inormation about the original data collection and processing refer to [this website](http://festcat.talp.cat/).


#### Who are the source data producers?

Format: http://www.debian.org/doc/packaging-manuals/copyright-format/1.0/

Upstream-Name: FestCat

Upstream-Contact: Sergio Oller <sergioller@gmail.com>, Antonio Bonafonte <antonio.bonafonte@upc.edu>

Source: http://festcat.talp.cat

Copyright: 2007-2012, Antonio Bonafonte
           2007-2012, Universitat Politècnica de Catalunya
           2007-2012, Sergio Oller <sergioller@gmail.com>
           2023, Language Technologies Unit (LangTech) at Barcelona Supercomputing Center 

License: LGPL-2.1

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
(N/A)

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->


### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

This dataset is a post-processed version of another previously created dataset. Please, refer to its documentation to know about any possible risks, biases and limitations.

## Citation

These are the relevant publications related to the creation and development of the festcat dataset:

```
@inproceedings{bonafonte2008corpus,
  title={Corpus and Voices for Catalan Speech Synthesis.},
  author={Bonafonte, Antonio and Adell, Jordi and Esquerra, Ignasi and Gallego, Silvia and Moreno, Asunci{\'o}n and P{\'e}rez, Javier},
  booktitle={LREC},
  year={2008}
}
```

```
@article{bonafonte2009recent,
  title={Recent work on the FESTCAT database for speech synthesis},
  author={Bonafonte, Antonio and Aguilar, Lourdes and Esquerra, Ignasi and Oller, Sergio and Moreno, Asunci{\'o}n},
  journal={Proc. SLTECH},
  pages={131--132},
  year={2009}
}
```

```
@article{gallego2010corpus,
  title={Corpus ling{\"u}{\'\i}stic pel desenvolupament d'una veu sint{\`e}tica en catal{\`a} per a Festival},
  author={Gallego Gonz{\`a}lez, Silvia},
  year={2010},
  publisher={Universitat Polit{\`e}cnica de Catalunya}
}
```

```
@phdthesis{moyano2007desenvolupament,
  title={Desenvolupament d'una veu en catal{\`a} per a Festival},
  author={Moyano, Francesc Jarque},
  year={2007}
}
```
**APA:**

## Funding
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project] (https://projecteaina.cat/).

## Dataset Card Contact
langtech@bsc.es