README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - ca
license:
  - cc-by-nc-4.0
multilinguality:
  - monolingual
pretty_name: TV3Parla
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - sequence-modeling
  - speech-processing
task_ids:
  - language-modeling
  - automatic-speech-recognition

Dataset Card for TV3Parla

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://collectivat.cat/asr#tv3parla
• Repository:
• Paper: Building an Open Source Automatic Speech Recognition System for Catalan
• Point of Contact: Col·lectivaT

Dataset Summary

This corpus includes 240 hours of Catalan speech from broadcast material. The details of segmentation, data processing and also model training are explained in Külebi, Öktem; 2018. The content is owned by Corporació Catalana de Mitjans Audiovisuals, SA (CCMA); we processed their material and hereby making it available under their terms of use.

This project was supported by the Softcatalà Association.

Supported Tasks and Leaderboards

The dataset can be used for:

• Language Modeling.
• Automatic Speech Recognition (ASR) transcribes utterances into words.

Languages

The dataset is in Catalan (ca).

Dataset Structure

Data Instances

{
  'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
  'audio': {'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
   'array': array([-0.01168823,  0.01229858,  0.02819824, ...,  0.015625  ,
          0.01525879,  0.0145874 ]),
   'sampling_rate': 16000},
  'text': 'algunes montoneres que que et feien anar ben col·locat i el vent també hi jugava una mica de paper bufava vent de cantó alguns cops o de cul i el pelotón el vent el porta molt malament hi havia molts nervis'
}

Data Fields

• path (str): Path to the audio file.
• audio (dict): 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].
• text (str): Transcription of the audio file.

Data Splits

The dataset is split into "train" and "test".

	train	test
Number of examples	159242	2220

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

Creative Commons Attribution-NonCommercial 4.0 International.

Citation Information

@inproceedings{kulebi18_iberspeech,
  author={Baybars Külebi and Alp Öktem},
  title={{Building an Open Source Automatic Speech Recognition System for Catalan}},
  year=2018,
  booktitle={Proc. IberSPEECH 2018},
  pages={25--29},
  doi={10.21437/IberSPEECH.2018-6}
}

Contributions

Thanks to @albertvillanova for adding this dataset.