README.md

---
language:
- nnh
- plt
- fub
license: cc-by-nc-sa-4.0
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
pretty_name: Multilingual African ASR (Ngiemboon & More)
tags:
- audio
- speech-recognition
- low-resource-languages
- bible
- aeneas
configs:
- config_name: fub_cm
  data_files:
  - split: train
    path: fub_cm/train-*
  - split: test
    path: fub_cm/test-*
- config_name: nnh_cm
  default: true
  data_files:
  - split: train
    path: nnh_cm/train-*
  - split: test
    path: nnh_cm/test-*
- config_name: plt_mdg
  data_files:
  - split: train
    path: plt_mdg/train-*
  - split: test
    path: plt_mdg/test-*
dataset_info:
- config_name: fub_cm
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
        num_channels: 1
  - name: text
    dtype: string
  - name: audio_file_name
    dtype: string
  splits:
  - name: train
    num_bytes: 8510848284
    num_examples: 39064
  - name: test
    num_bytes: 2407713127
    num_examples: 11343
  download_size: 6495729090
  dataset_size: 10918561411
- config_name: nnh_cm 
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
        num_channels: 1
  - name: text
    dtype: string
  - name: audio_file_name
    dtype: string
  splits:
  - name: train
    num_bytes: 2319359059
    num_examples: 10841
  - name: test
    num_bytes: 494019097
    num_examples: 2163
  download_size: 2448461231
  dataset_size: 2813378156
- config_name: plt_mdg
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
        num_channels: 1
  - name: text
    dtype: string
  - name: audio_file_name
    dtype: string
  splits:
  - name: train
    num_bytes: 7405654024
    num_examples: 27052
  - name: test
    num_bytes: 1724912714
    num_examples: 6801
  download_size: 6025600373
  dataset_size: 9130566738
---
# AFRILANG

# Dataset Card for Multilingual African ASR
**Dataset Description**
- **Primary Language:** Ngiemboon (ISO 639-3: `nnh`)
- **Secondary Languages:** Planned support for Bulu, Morisyen, Malagasy and other African languages.
- **Audio Format:** WAV (16kHz, Mono)
- **Task:** Automatic Speech Recognition (ASR).
### Dataset Summary
This dataset is a curated collection of aligned audio-text pairs for African languages, starting with **Ngiemboon**. The data originates from high-quality recordings of the New Testament, offering excellent phonetic coverage for these low-resource languages.
The processing pipeline utilizes **Apache Airflow** for orchestration and **Aeneas** for forced alignment. This allows the conversion of long-form chapters into clean, short segments (99% of the audio files are less than 30 seconds long.) perfectly optimized for fine-tuning state-of-the-art models like **OpenAI Whisper** or **Meta MMS**.

---
## Usage: How to Load for Fine-Tuning
This dataset is compatible with the Hugging Face `datasets` library. It includes an `Audio` feature that decodes sound files on the fly.

### 1. Installation
```bash
pip install datasets[audio] transformers
```

### 2. Load the dataset

```python 
from datasets import load_dataset, Audio

# Load the dataset
dataset = load_dataset("mimba/afrilang", "nnh_cm", split={"train": "train[:10%]", "test": "test[:2%]" })

# Essential: Resample to 16kHz for Whisper or MMS models
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))

# Access the first sample
example = dataset[0]
print(example["text"])
print(example["audio"]["array"])
```

### 3. Preparation for Training

The following snippet shows how to process the data for a Whisper model:

```python
def prepare_dataset(batch):
    audio = batch["audio"]
    # Extract features from the audio array
    batch["input_features"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
    # Tokenize the target text
    batch["labels"] = processor(text=batch["text"]).input_ids
    return batch

dataset = dataset.map(prepare_dataset)
```

## Dataset Creation & Methodology

#### 1. Source Data

* **Audio:** Studio-quality recordings of the Old or Testament.
* **Text:** Digital transcriptions following standard Ngiemboon orthography
* 
#### 2. Automated Pipeline

1. **Orchestration:** Apache Airflow manages the sequence of tasks.
2. **Alignment:** Aeneas (Forced Alignment) synchronizes text with the original audio chapters.
3. **Segmentation:** Audio is automatically sliced based on alignment timestamps using Pydub.
4. **Validation:** Checks are performed to ensure every audio segment has its corresponding text and vice-versa.

## Future Roadmap: Expanding the Dataset 🌍

We are committed to increasing the digital presence of African languages. Our roadmap includes:

* [x] **Ngiemboon (nnh)** *Cameroon:* Complete and ready for training.
* [x] **Adamawa Fulfulde (fub)** *Cameroon:* Complete and ready for training.
* [x] **Malagasy (plt)** *Malagasy:* Complete and ready for training.
* [ ] **Bulu (bum)** *Cameroon:* Currently in the alignment phase.
* [ ] **Morisyen (mfe)** *Mauritius*: Data collection ongoing.
* [ ] **Community Contributions:** Soon, users will be able to submit their own recordings to improve model robustness.

## Considerations

### Limitations

* **Domain:** Currently focused on religious and narrative text. General conversational performance may vary.
* **Acoustics:** Clean studio audio. We recommend adding "background noise" augmentation during fine-tuning for real-world applications.
* 
### Licensing Information

This dataset is released under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)** license.

### Contact

For questions or contributions, please open a discussion in the "Community" tab of this repository.


### BibTeX entry and citation info

```bibtex
@misc{
      title={afrilang: Small Out-of-domain resource for various africain languages}, 
      author={Mimba Ngouana Fofou},
      year={2026},
}
```

##### *Contact For all questions contact [@Mimba](baounabaouna@gmail.com).*