[](https://aclanthology.org/2025.emnlp-main.559/)
[](https://africa.dlnlp.ai/simba/)
[](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
[](https://github.com/UBC-NLP/simba)
[](https://huggingface.co/collections/UBC-NLP/simba-speech-series)
[](https://huggingface.co/datasets/UBC-NLP/SimbaBench_dataset)
## SibmaBench Data Release & Benchmarking
To evaluate your model on **SimbaBench** across all supported tasks (ASR, TTS, and SLID), simply load the corresponding configuration for the task and language you wish to benchmark.
Each task is organized by configuration name (e.g., `asr_test_afr`, `tts_test_wol`, `slid_61_test`). Loading a configuration provides the standardized evaluation split for that specific benchmark.
Example:
```python
from datasets import load_dataset
data = load_dataset("UBC-NLP/SimbaBench_dataset", "asr_test_afr")
```
```
DatasetDict({
test: Dataset({
features: ['split', 'benchmark_id', 'audio', 'text', 'duration_s', 'lang_iso3', 'lang_name'],
num_rows: 1000
})
})
```
``` python
data['test'][0]
```
```
{'split': 'test',
'benchmark_id': 'afr_Lwazi_afr_test_idx3889',
'audio': {'path': None,
'array': array([ 4.27246094e-04, 7.62939453e-04, 6.71386719e-04, ...,
-3.05175781e-04, -2.13623047e-04, -6.10351562e-05]),
'sampling_rate': 16000},
'text': 'watter, verontwaardiging sou daar, in ons binneste gewees het?',
'duration_s': 5.119999885559082,
'lang_iso3': 'afr',
'lang_name': 'Afrikaans'}
```
## 📌 ASR Evaluation Configurations