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---
language:
- zu
- xh
- nr
- nso
- st
- ss
- tn
- ts
- ve
license: mit
task_categories:
- automatic-speech-recognition
pretty_name: isiZulu ASR Augmented Dataset
size_categories:
- 1K<n<10K
---
# isiZulu Speech Recognition Augmented Dataset - SpecAugment
## Dataset Description
This dataset contains augmented speech recordings and transcriptions for isiZulu, one of South Africa's official languages.
The dataset has been optimized for use with OpenAI's Whisper ASR models.
## Dataset Statistics
- **Number of samples**: 635
- **Language**: isiZulu (Zul)
- **Audio format**: WAV, 16kHz, mono, 16-bit
- **Maximum duration**: 30 seconds (truncated for Whisper compatibility)
- **Transcription format**: Cleaned text (lowercase, no punctuation)
## Audio Processing
All audio files have been processed with the following optimizations:
- Resampled to 16kHz (Whisper's native sample rate)
- Converted to mono
- Truncated to maximum 30 seconds
- 16-bit PCM encoding
## Transcription Cleaning
Transcriptions have been cleaned:
- Converted to lowercase
- Removed all punctuation
- Removed POS markers (e.g., [n], [v])
- Normalized whitespace
## Dataset Structure
Each sample contains:
- `audio`: Audio file path and array
- `transcription`: Cleaned transcription text
- `file_id`: Unique identifier for the recording
- `subfolder`: Original subfolder location
## Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("zionia/isizulu-lwazi-asr-specaugment")
# Access samples
print(dataset['train'][0])
```
## Citation
If you use this dataset, please cite the original Lwazi corpus:
```
@inproceedings{lwazi2011,
title={The Lwazi corpus: an African speech resource},
author={Barnard, E. and Davel, M. H. and Van Heerden, C.},
booktitle={Proceedings of the 22nd Annual Symposium of the Pattern Recognition Association of South Africa},
year={2011}
}
```
## License
This dataset is released under the MIT License.
## Language
isiZulu is a language spoken primarily in South Africa.
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