
# Dataset Card for TV3Parla

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://collectivat.cat/asr#tv3parla
- **Repository:**
- **Paper:** [Building an Open Source Automatic Speech Recognition System for Catalan](https://www.isca-speech.org/archive/iberspeech_2018/kulebi18_iberspeech.html)
- **Point of Contact:** [Col·lectivaT](mailto:info@collectivat.cat)

### 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](https://creativecommons.org/licenses/by-nc/4.0/).

### 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](https://github.com/albertvillanova) for adding this dataset.

task_categories:automatic-speech-recognition task_categories:text-generation task_ids:language-modeling annotations_creators:found language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:ca license:cc-by-nc-4.0 region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition", "text-generation"], "task_ids": ["language-modeling"], "pretty_name": "TV3Parla"}
TAGS
#task_categories-automatic-speech-recognition #task_categories-text-generation #task_ids-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Catalan #license-cc-by-nc-4.0 #region-us 

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: URL
* 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


### 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".



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### 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


Creative Commons Attribution-NonCommercial 4.0 International.


### Contributions


Thanks to @albertvillanova for adding this dataset.



### 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 ### 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".



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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


Creative Commons Attribution-NonCommercial 4.0 International. ### Contributions


Thanks to @albertvillanova for adding this dataset.
TAGS
#task_categories-automatic-speech-recognition #task_categories-text-generation #task_ids-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Catalan #license-cc-by-nc-4.0 #region-us 
 ### 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 ### 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".



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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


Creative Commons Attribution-NonCommercial 4.0 International. ### Contributions


Thanks to @albertvillanova for adding this dataset.
passage: TAGS
#task_categories-automatic-speech-recognition #task_categories-text-generation #task_ids-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Catalan #license-cc-by-nc-4.0 #region-us 
### 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### 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".



Dataset Creation
----------------