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Datasets:
RobotsMali
/
bam-asr-all

Tasks:
Automatic Speech Recognition
Text-to-Speech
Translation
Modalities:
Audio
Text
Formats:
arrow
Sub-tasks:
audio-language-identification
keyword-spotting
Languages:
Bambara
French
Size:
10K - 100K
Tags:
audio
transcription
multilingual
Bambara
French
Libraries:
Datasets
License:
Dataset card Data Studio Files Community
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bam-asr-all / README.md
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metadata
language:
  - bm
  - fr
pretty_name: Bambara-ASR-All Audio Dataset
version: 1.0.0
tags:
  - audio
  - transcription
  - multilingual
  - Bambara
  - French
license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
  - text-to-speech
  - translation
task_ids:
  - audio-language-identification
  - keyword-spotting
annotations_creators:
  - semi-expert
language_creators:
  - crowdsourced
source_datasets:
  - jeli-asr
  - mali-pense
  - reading-tutor-data-collection
size_categories:
  - 10GB<
  - 10K<n<100K
dataset_info:
  audio_format: wav
  features:
    - name: audio
      dtype: audio
    - name: duration
      dtype: float
    - name: bam
      dtype: string
    - name: french
      dtype: string
  total_audio_files: 38769
  total_duration_hours: ~37
configs:
  - config_name: jeli-asr-rmai
    data_files:
      - split: train
        path: jeli-asr-rmai/train/*
      - split: test
        path: jeli-asr-rmai/test/*
  - config_name: bam-asr-oza
    data_files:
      - split: train
        path: bam-asr-oza/train/*
      - split: test
        path: bam-asr-oza/test/*
  - config_name: oza-mali-pense
    data_files:
      - split: train
        path: oza-mali-pense/train/*
  - config_name: rt-data-collection
    data_files:
      - split: train
        path: rt-data-collection/*
  - config_name: jeli-asr
    data_files:
      - split: train
        path:
          - jeli-asr-rmai/train/*
          - bam-asr-oza/train/*
      - split: test
        path:
          - jeli-asr-rmai/test/*
          - bam-asr-oza/test/*
  - config_name: bam-asr-all
    default: true
    data_files:
      - split: train
        path:
          - jeli-asr-rmai/train/*
          - bam-asr-oza/train/*
          - oza-mali-pense/train/*
          - rt-data-collection/*
      - split: test
        path:
          - jeli-asr-rmai/test/*
          - bam-asr-oza/test/*
description: >
  This Dataset is a multilingual audio dataset containing audio samples in
  Bambara with semi-expert transcriptions and French translations. 

  Each audio file is paired with its transcription in Bambara or its translation
  in French (available in manifest files). 

  The dataset is designed for tasks like automatic speech recognition (ASR) and
  translation. Data come from all publicly available sources providing audios
  and aligned bambara transcription.

All Bambara ASR Dataset

This dataset aims to gather all publicly available Bambara ASR datasets. It is primarily composed of the Jeli-ASR dataset (available at RobotsMali/jeli-asr), along with the Mali-Pense data curated and published by Aboubacar Ouattara (available at oza75/bambara-tts). Additionally, it includes 1 hour of audio recently collected by the RobotsMali AI4D Lab, featuring children's voices reading some of RobotsMali GAIFE books. This dataset is desihgned for automatic speech recognition (ASR) task primarily.

Important Note

Please note that this dataset is currently in development and is therefore not fixed. The structure, content, and availability of the dataset may change as improvements and updates are made.

Directory Structure 

bam-asr-all/
|
β”œβ”€β”€ README.md
β”œβ”€β”€ metadata.jsonl
β”œβ”€β”€ manifests/
β”‚   β”œβ”€β”€ jeli-asr-rmai-test-manifest.json
β”‚   β”œβ”€β”€ jeli-asr-rmai-train-manifest.json
β”‚   β”œβ”€β”€ oza-bam-asr-test-manifest.json
β”‚   └── oza-bam-asr-train-manifest.json
β”‚   └── oza-mali-pense-train-manifest.json
β”‚   └── reading-tutor-train-manifest.json
β”‚   └── train-manifest.json # jeli-asr-rmai-train-manifest.json + oza-bam-asr-train-manifest.json
β”‚   └── test-manifest.json # jeli-asr-rmai-test-manifest.json + oza-bam-asr-test-manifest.json
β”‚
β”œβ”€β”€ french-manifests/
β”‚   β”œβ”€β”€ jeli-asr-rmai-test-french-manifest.json
β”‚   β”œβ”€β”€ jeli-asr-rmai-train-french-manifest.json
β”‚   β”œβ”€β”€ oza-bam-asr-test-french-manifest.json
β”‚   └── oza-bam-asr-train-french-manifest.json
β”‚   └── oza-mali-pense-train-french-manifest.json
β”‚
β”œβ”€β”€ jeli-asr-rmai/
β”‚   β”œβ”€β”€ train/
β”‚   └── test/
β”‚
β”œβ”€β”€ bam-asr-oza/
β”‚   β”œβ”€β”€ train/
β”‚   └── test/
|
β”œβ”€β”€ oza-mali-pense/
β”‚   β”œβ”€β”€ train/
|
β”œβ”€β”€ oza-mali-pense/

manifests Directory

This directory contains the manifest files used for training speech recognition (ASR) and text-to-speech (TTS) models. Those are JSON files:

Each line in the manifest files is a JSON object with the following structure:

{
  "audio_filepath": "bam-asr-all/rt-data-collection/zctn7pFmtmR45FKym7d5.wav", 
  "duration": 10.24, 
  "text": "Ni Birituban dΙ” tun bΙ› se ka piyano fΙ”, a tun bΙ› fara SΙ› kan dΙ”nkilida la."
}
  • audio_filepath: The relative path to the corresponding audio file.
  • duration: The duration of the audio file in seconds.
  • text: The transcription of the audio in Bambara.

3. french-manifests/

This directory contains French equivalent manifest files for the dataset. The structure is similar to the manifests/ directory but with French transcriptions

Dataset Details

  • Total Duration: 37.41 hours
  • Number of Samples: 38,769
    • Training Set: 37,306 samples
    • Testing Set: 1,463 samples

Subsets:

  • Oza's Bambara-ASR: ~29 hours (clean subset).
  • Jeli-ASR-RMAI: ~3.5 hours (filtered subset).
  • oza-tts-mali-pense: ~4 hours
  • reading-tutor-data-collection: ~1 hour

Usage

The manifest files are specifically created for training Automatic Speech Recognition (ASR) models in NVIDIA NeMo framework, but they can be used with any other framework that supports manifest-based input formats or reformatted for other use cases.

To use the dataset, simply load the manifest files (train-manifest.json and test-manifest.json) in your training script. The file paths for the audio files and the corresponding transcriptions are already provided in these manifest files You can also load it directly in a HuggingFace dataset object.

Downloading the Dataset: 

from datasets import load_dataset

# Clone dataset repository maintaining directory structure
!git clone https://huggingface.co/datasets/RobotsMali/jeli-asr

#  Or

# Load the dataset into Hugging Face Dataset object
dataset = load_dataset("RobotsMali/bam-asr-all")

Known Issues

While significantly improved, this dataset may still contain a few Slightly misaligned samples. It has conserved most of the issues of the original dataset such as: 

  • Inconsistent transcriptions
  • Non-standardized naming conventions.
  • Language and spelling issues

Citation

If you use this dataset in your research or project, please credit the creators of the original datasets.