README.md

---
language: 
- bm  # ISO 639-1 code for Bambara
- fr  # ISO 639-1 code for French
pretty_name: "Jeli-ASR Audio Dataset"
version: "1.0.1"  # Explicit versioning
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  # Identifying languages in audio
- keyword-spotting              # Detecting keywords in audio
annotations_creators:
- semi-expert
language_creators:
- crowdsourced  # If the data was annotated or recorded by a team
source_datasets:
- jeli-asr  
size_categories:
- 10GB<
- 10K<n<100K
dataset_info:
  audio_format: "arrow"
  features:
  - name: audio
    dtype: audio
  - name: duration
    dtype: float
  - name: bam
    dtype: string
  - name: french
    dtype: string
  total_audio_files: 33643
  total_duration_hours: ~32

configs:
- config_name: jeli-asr-rmai
  data_files:
  - split: train
    path: "jeli-asr-rmai/train/data-*.arrow"
  - split: test
    path: "jeli-asr-rmai/test/data-*.arrow"
- config_name: bam-asr-oza
  data_files:
  - split: train
    path: "bam-asr-oza/train/data-*.arrow"
  - split: test
    path: "bam-asr-oza/test/data-*.arrow"
- config_name: jeli-asr
  default: true
  data_files:
  - split: train
    path: 
      - "jeli-asr-rmai/train/data-*.arrow"
      - "bam-asr-oza/train/data-*.arrow"
  - split: test
    path: 
      - "jeli-asr-rmai/test/data-*.arrow"
      - "bam-asr-oza/test/data-*.arrow"
description: |
  The **Jeli-ASR Audio Dataset** is a multilingual dataset converted into the optimized Arrow format, 
  ensuring fast access and compatibility with modern data workflows. It contains audio samples in Bambara 
  with semi-expert transcriptions and French translations. Each subset of the dataset is organized by 
  configuration (`jeli-asr-rmai`, `bam-asr-oza`, and `jeli-asr`) and further split into training and testing sets. 
  The dataset is designed for tasks like automatic speech recognition (ASR), text-to-speech synthesis (TTS), 
  and translation. Data was recorded in Mali with griots, then transcribed and translated into French.
---

# Jeli-ASR Dataset

This repository contains the **Jeli-ASR** dataset, which is primarily a reviewed version of Aboubacar Ouattara's **Bambara-ASR** dataset (drawn from jeli-asr and available at [oza75/bambara-asr](https://huggingface.co/datasets/oza75/bambara-asr)) combined with the best data retained from the former version: `jeli-data-manifest`. This dataset features improved data quality for automatic speech recognition (ASR) and translation tasks, with variable length Bambara audio samples, Bambara transcriptions and French translations.

## Important Notes

1. 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.

---

## **Key Changes in Version 1.0.1**

Jeli-ASR 1.0.1 introduces several updates and enhancements, focused entirely on the transcription side of the dataset. There have been no changes to the audio files since version 1.0.0. Below are the key updates:

1. **Symbol Removal:**  
   All non-vocabulary symbols deemed unnecessary for Automatic Speech Recognition (ASR) were removed, including:  
   `[` `]` `(` `)` `«` `»` `°` `"` `<` `>`  

2. **Punctuation Removal:**  
   Common punctuation marks were removed to streamline the dataset for ASR use cases. These include:  
   `:` `,` `;` `.` `?` `!`  
   The exception is the hyphen (`-`), which remains as it is used in both Bambara and French compound words. While this punctuation removal enhances ASR performance, the previous version with full punctuation may still be better suited for other applications. You can still reconstruct the previous version with the archives.

3. **Bambara Normalization:**  
   The transcription were normalized using the [Bambara Normalizer](https://pypi.org/project/bambara-normalizer/), a python package designed to normalize Bambara text for different NLP applications.

4. **Optimized Data Format:**  
   This version introduces `.arrow` files for efficient data storage and retrieval and compatibility with HuggingFace tools.

Let us know if you have feedback or additional use suggestions for the dataset by opening a discussion or a pull request. You can find a record or updates of the dataset in [VERSIONING.md](VERSIONING.md)

---

## **Dataset Details**

- **Total Duration**: 32.48 hours
- **Number of Samples**: 33,643
  - **Training Set**: 32,180 samples (\~95%)
  - **Testing Set**: 1,463 samples (\~5%)

### **Subsets**:

- **Oza's Bambara-ASR**: \~29 hours (clean subset).
- **Jeli-ASR-RMAI**: \~3.5 hours (filtered subset).

Note that since the two subsets were drawn from the original Jeli-ASR dataset, they are just different variation of the same data.

---

## **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.

### Downloading the Dataset:
```bash

# Clone dataset repository maintaining directory structure for quick setup with Nemo
git clone --depth 1 https://huggingface.co/datasets/RobotsMali/jeli-asr
```

**OR**

```python

from datasets import load_dataset

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

### Finetuning Example in NeMo:

```python
from nemo.collectisr.models import ASRModel
train_manifest = 'jeli-asr/manifests/train-manifest.json'
test_manifest = 'jeli-asr/manifests/test-manifest.json'

asr_model = ASRModel.from_pretrained("QuartzNet15x5Base-En")

# Adapt the model's vocab before training
asr_model.setup_training_data(train_data_config={'manifest_filepath': train_manifest})
asr_model.setup_validation_data(val_data_config={'manifest_filepath': test_manifest})
```

## **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
- Inaccurate translations

---

## **Citation**

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

- **Jeli-ASR dataset**: [Original Jeli-ASR Dataset](https://github.com/robotsmali-ai/jeli-asr). 
- **Oza's Bambara-ASR dataset**: [oza75/bambara-asr](https://huggingface.co/datasets/oza75/bambara-asr)