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Dataset Card for ParlamentParla

Dataset Summary

This is the ParlamentParla speech corpus for Catalan prepared by Col·lectivaT. The audio segments were extracted from recordings the Catalan Parliament (Parlament de Catalunya) plenary sessions, which took place between 2007/07/11 - 2018/07/17. We aligned the transcriptions with the recordings and extracted the corpus. The content belongs to the Catalan Parliament and the data is released conforming their terms of use.

Preparation of this corpus was partly supported by the Department of Culture of the Catalan autonomous government, and the v2.0 was supported by the Barcelona Supercomputing Center, within the framework of the project AINA of the Departament de Polítiques Digitals.

As of v2.0 the corpus is separated into 211 hours of clean and 400 hours of other quality segments. Furthermore, each speech segment is tagged with its speaker and each speaker with their gender. The statistics are detailed in the readme file.

Supported Tasks and Leaderboards

The dataset can be used for:

  • Language Modeling.
  • Automatic Speech Recognition (ASR) transcribes utterances into words.
  • Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing.

Languages

The dataset is in Catalan (ca).

Dataset Structure

Data Instances

{
  'path': 'clean_train/c/c/ccca4790a55aba3e6bcf_63.88_74.06.wav'
  'audio': {
    'path': 'clean_train/c/c/ccca4790a55aba3e6bcf_63.88_74.06.wav',
    'array': array([-6.10351562e-05, -6.10351562e-05, -1.22070312e-04, ...,  
                    -1.22070312e-04,  0.00000000e+00, -3.05175781e-05]),
    'sampling_rate': 16000
  },
  'speaker_id': 167,
  'sentence': "alguns d'ells avui aquí presents un agraïment a aquells que mantenen viva la memòria aquest acte de reparació i dignitat és",
  'gender': 0, 
  'duration': 10.18
}

Data Fields

  • path (str): The 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].
  • speaker_id (int): The speaker ID.
  • sentence (str): The sentence the user was prompted to speak.
  • gender (ClassLabel): The gender of the speaker (0: 'F', 1: 'M').
  • duration (float): Duration of the speech.

Data Splits

The dataset is split in: "train", "validation" and "test".

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 4.0 International.

Citation Information

@dataset{kulebi_baybars_2021_5541827,
  author       = {Külebi, Baybars},
  title        = {{ParlamentParla - Speech corpus of Catalan 
                   Parliamentary sessions}},
  month        = oct,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v2.0},
  doi          = {10.5281/zenodo.5541827},
  url          = {https://doi.org/10.5281/zenodo.5541827}
}

Funding

This work was funded by the Catalan Ministry of the Vice-presidency, Digital Policies and Territory within the framework of the Aina project.

Contributions

Thanks to @albertvillanova for adding this dataset.

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Models trained or fine-tuned on projecte-aina/parlament_parla