README.md

---
language:
- bm
- fr
license: cc-by-sa-4.0
task_categories:
- text-to-speech
dataset_info:
- config_name: default
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  - name: bambara
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  - name: french
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  - name: speaker_id
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configs:
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  data_files:
  - split: train
    path: data/train-*
- config_name: denoised
  data_files:
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    path: denoised/train-*
- config_name: enhanced
  data_files:
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- config_name: jeli_asr_enhanced
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- config_name: mali_pense
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- config_name: mali_pense_denoised
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- config_name: mali_pense_enhanced
  data_files:
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    path: mali_pense_enhanced/train-*
---

# Overview
## Project
This dataset is part of a larger initiative dedicated to enabling 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 (Burkina Faso, Ivory Coast etc...). 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.

# Dataset
## Source
The dataset was meticulously compiled with a focus on quality and utility. The source materials were obtained from a rich Bambara content available at [Mali Pense](https://www.mali-pense.net/).
Audio recordings were carefully processed to improve clarity and usability.

## Processing
Noise reduction was a critical step in preparing the audio data to ensure high-quality samples. This was achieved using **DeepFilterNet**, 
an advanced noise suppression algorithm accessible on GitHub [here](https://github.com/Rikorose/DeepFilterNet). The resulting clean audio provides clear and usable samples for TTS development.

To enhance the dataset's applicability in personalized TTS systems, speaker embeddings were generated using the [pyannote/embedding](https://huggingface.co/pyannote/embedding) model from Huggingface. 
This embedding captures unique speaker characteristics, allowing for speaker identification and differentiation in TTS applications.

## Clustering
Speaker embeddings were clustered using the [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html) algorithm *(via the hdbscan pip3 package)* to infer speaker identities within the dataset. 
While this clustering offers a basis for differentiating speakers, it is not **infallible**. Users are encouraged **to use the provided embeddings to refine** or generate their own speaker identification as needed for their specific applications.

## Dataset Structure
### Data Fields
The dataset includes the following fields:

- audio: This field contains the file path (loaded via huggingface datasets library) to the audio recording of spoken Bambara text. Each audio file corresponds to a single utterance of spoken text.
- bambara: A string field that contains the transcription of the spoken text in the Bambara language. This transcription corresponds to the content of the audio file.
- french: A string field with the French translation of the Bambara text. This provides a parallel corpus for those interested in bilingual applications.
- duration: A float64 field that represents the duration of the audio clip in seconds. It gives an indication of the length of the spoken utterance.
- speaker_embeddings: A sequence field that holds the numerical vector representing the speaker's voice characteristics. This embedding can be used for speaker identification or distinguishing between different speakers in the dataset.
- speaker_id: An int32 field that indicates the cluster ID assigned to the speaker based on the HDBSCAN algorithm. This ID helps to identify all utterances from the same speaker across the dataset.

### Data Instances
An example from the dataset looks like this:
```json 
{
  "audio": Audio({"array": [-2.5, 35...], "path": "path/to/audio.wav", "sampling_rate": 48000}),
  "bambara": "Jigi, i bolo degunnen don wa ?",
  "french": "Jigi, es-tu occupé ?",
  "duration": 2.646,
  "speaker_embeddings": [-2.564516305923462, -20.928389595581055, ...],
  "speaker_id": 5
}

```

### Usage
The dataset is designed for a variety of uses in the field of speech technology, including:

- **Text-to-Speech Synthesis:** Researchers and developers can utilize this dataset to train and fine-tune TTS models capable of converting Bambara text into natural-sounding speech.
- **Speech Recognition:** The audio samples can aid in the development of Automatic Speech Recognition (ASR) systems that transcribe Bambara speech.
- **Linguistic Research:** Linguists can explore the phonetic and prosodic features of Bambara speech.
- **Educational Content Creation:** Educators and content creators can develop voice-enabled educational resources in Bambara.

# Acknowledgements
This project was made possible through the contributions of various individuals and organizations dedicated to preserving and promoting the **Bambara language and culture**.
We extend our gratitude to [Mali Pense](https://www.mali-pense.net/) for providing the text sources, [Rikorose/DeepFilterNet](https://github.com/Rikorose/DeepFilterNet) for the noise reduction technology, and [Pyannote](https://huggingface.co/pyannote) for the speaker embedding model.

# Other Bambara Dataset
- Bambara French Parallel dataset: https://www.kaggle.com/datasets/ozaresearch1/bambara-french-parallel-dataset
- Corpus Bambara de reference: http://cormand.huma-num.fr/index.html
- Dictionnaries & other resources: https://www.lexilogos.com/bambara_dictionnaire.htm