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Datasets:
djelia
/
bambara-audio

Tasks:
Automatic Speech Recognition
Text-to-Speech
Modalities:
Audio
Text
Formats:
parquet
Languages:
Bambara
Size:
10K - 100K
Libraries:
Datasets
Dask
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Djelia Bambara Audio Dataset

Dataset Description

The Djelia Bambara Audio Dataset is a comprehensive resource aimed at supporting research and development in Bambara language processing. This dataset consists of audio extracted from YouTube videos, denoised and diarized to ensure high-quality segments. Additionally, it features a semi-annotated subset with transcriptions generated using the Djelia Whisper v1 model.

Features

  • Audio: High-quality audio clips extracted from YouTube videos.
  • Duration: Each audio clip includes a precise duration field.
  • Speaker: Metadata includes speaker information identified through diarization.
  • Semi-Annotated Config: The dataset includes transcriptions generated using the Djelia Whisper v1 model.

Configuration: Semi-Annotated

The semi-annotated subset contains audio clips with corresponding transcriptions generated by the Djelia Whisper v1 model. This subset is particularly suited for applications in automatic speech recognition (ASR) and Text-to-Speech (TTS).

Statistics

  • Total Hours: 171.0382 hours of audio.
  • Source: Audio segments extracted from publicly available YouTube channels.

Project

This dataset is part of a larger initiative aimed at empowering Bambara speakers to access global knowledge without language barriers. Our goal is to eliminate the need for Bambara speakers to learn a secondary language before they can acquire new information or skills. By providing a robust dataset for Text-to-Speech (TTS) applications, we aim to support the creation of tools for Bambara language, thus democratizing access to knowledge.

Bambara Language

Bambara, also known as Bamanankan, is a Mande language spoken primarily in Mali by millions of people as a mother tongue and second language. It serves as a lingua franca in Mali and is also spoken in neighboring countries, including Burkina Faso and Ivory Coast. Bambara is written in both the Latin script and N'Ko script, and it has a rich oral tradition that is integral to Malian culture.

Data Collection and Processing

The dataset was created by extracting audio from YouTube videos. To ensure the quality and usability of the data, the following steps were performed:

  1. Audio Denoising: We utilized tools from Resemble AI to remove background noise and enhance audio clarity.
  2. Speaker Diarization: Using PyAnnote, we identified distinct speakers in the audio segments, ensuring accurate speaker metadata.
  3. Semi-Annotation: The Djelia Whisper v1 model was used to transcribe the audio clips, creating the semi-annotated configuration.

Acknowledgements

We extend our gratitude to the following contributors and tools that made this dataset possible:

  • RAS BATH, DIANY.ML_FM, and ORTM YouTube Channel for the audio content.
  • Resemble AI for providing advanced denoising tools.
  • PyAnnote for their speaker diarization toolkit.
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