Hugging Face's logo

Datasets:
nilc-nlp
/
CORAA-NURC-SP-Audio-Corpus

Dataset card Files Community
Dataset Viewer
Full Screen

The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support (you can use convert_to_parquet from the datasets library). If this is not possible, please open a discussion for direct help.

YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

NURC-SP Corpus

NURC-SP Corpus CORAA ASR is a publicly available dataset for Automatic Speech Recognition (ASR) in the Brazilian Portuguese language containing 239.68 hours of audios ( 239.30 when filtered ) and their respective transcriptions (170k+ segmented audios).

The audios were either validated by annotators or transcripted for the first time aiming at the ASR task.

How to Use

The datasets library allows easy loading of the dataset with the load_dataset() function.

To load the dataset, you need to pass the name "original" (full list of audio segments) or "filtered" (audio segments with empty transcription removed)

from datasets import load_dataset

dataset_original = load_dataset("nilc-nlp/CORAA-NURC-SP-Audio-Corpus", name="original")
dataset_filtered = load_dataset("nilc-nlp/CORAA-NURC-SP-Audio-Corpus", name="filtered")

Metadata

  • audio_name: The name given to the audio in the database. All audios extracted from the same source have the same name.
  • file_path: The path to the audio file.
  • text: The human-verified trancription for the given audio.
  • start_time: The time the audio segment starts in the original source in seconds.
  • end_time: The time the audio segment ends in the original source in seconds.
  • duration: The duration of the audio segment in seconds.
  • quality: Whether or not the audio had parts that could not be transcribed properly. Audios without this characteristic are rated 'high' and audios with it are rated 'low'.
  • speech_genre: The speech genre of the original source of the segment. Divided into 'dialogue', 'interview' or 'lecture and talks'.
  • speech_style: The speech style of the original source of the segment. All segments are categorized as 'spontaneous speech'.
  • variety: The audio language. All segments are categorized as 'pt-br'.
  • accent: The speaker's accent. All segments are categorized as 'sp-city'. Note that some audio sources have more than one speaker, so in that case the accent refers to the main speaker or speakers.
  • sex: The speaker's sex. Divided into 'F', 'M', 'F e F', 'F e M' and 'M e M' ('F' stands for female and 'M' stands for male). Note that some audio sources have more than one speaker, so in that case the sex refers to the main speaker or speakers.
  • age_range: The speaker's age range. Divided into 'I' (25 to 35), 'II' (36 to 55) and 'III' (over 55). Note that some audio sources have more than one speaker, so in that case the age range refers to the main speaker or speakers.
  • num_speakers: The number of speakers in the original source of the segment. This field was automatically writter by WhisperX, so it might not be accurate.
  • speaker_id: The speaker in the segment (each different speaker in the original source was given an integer id). This field was automatically writter by WhisperX, so it might not be accurate.

Downloads

Currently, download is only available in the Files section

Hugging Face
Download Link

Citation

Lima, R., Leal, S.E., Candido Junior, A., Aluísio, S.M. A Large Dataset of Spontaneous Speech with the Accent Spoken in São Paulo for Automatic Speech Recognition Evaluation. Proceedings of the 34th Brazilian Conference on Intelligent Systems (BRACIS) (2024).

@InProceedings{{nurc-sp-audio-corpus-2024,
    author = {Rodrigo Lima and Sidney Evaldo Leal and Arnaldo Candido Junior and Sandra Maria Aluisio},
    title = {A Large Dataset of Spontaneous Speech with the Accent Spoken in São Paulo for Automatic Speech Recognition Evaluation},
    booktitle  = {Proceedings of 34th Brazilian Conference on Intelligent Systems (BRACIS)},
    year = {2024}
}

Sponsors / Funding

This work was carried out at the Center for Artificial Intelligence (C4AI-USP), with support by the São Paulo Research Foundation (FAPESP grant #2019/07665-4) and by the IBM Corporation. This project was also supported by the Ministry of Science, Technology and Innovation, with resources of Law No. 8.248, of October 23, 1991, within the scope of PPI-SOFTEX, coordinated by Softex and published Residence in TIC 13, DOU 01245.010222/2022-44.

Downloads last month
2
Edit dataset card