---
language:
- sw
tags:
- whisper
- automatic-speech-recognition
- swahili
- nonstandard-speech
- low-resource
datasets:
- ElizabethMwangi/swahili_afrivoice_all_domains_v0.1
- cdli/common_voice_swahili_small
- kenyan_swahili_nonstandard_speech_v1.0
base_model: ElizabethMwangi/whisper-small-swahili-afrivoice_cv
license: apache-2.0
---

# Whisper Small — Swahili (Non-Standard Speech, No SpecAugment)

Fine-tuned version of [ElizabethMwangi/whisper-small-swahili-afrivoice_cv](https://huggingface.co/ElizabethMwangi/whisper-small-swahili-afrivoice_cv) for automatic speech recognition of **non-standard (dysarthric) Swahili speech**.

This is **Stage 2** of a two-stage fine-tuning pipeline, further trained on the Kenyan Swahili Non-Standard Speech dataset.

## Model Details

- **Base model:** ElizabethMwangi/whisper-small-swahili-afrivoice_cv
- **Language:** Swahili (`sw`)
- **Task:** Automatic Speech Recognition (ASR)
- **Training data:** Kenyan Swahili Non-Standard Speech v1.0

## Evaluation Results (NSS test set, 849 examples)

| Metric | Value |
|--------|-------|
| Overall WER (normalised) | 45.5% |
| Overall CER (normalised) | 21.7% |
| Avg WER (normalised) | 36.8% |
| Avg CER (normalised) | 16.2% |

### Per-severity breakdown

| Severity | Avg WER | Avg CER |
|----------|---------|--------|
| Mild | 0.29 | 0.12 |
| Moderate | 0.36 | 0.15 |
| Severe | 0.44 | 0.20 |

## Usage

```python
from transformers import pipeline

asr = pipeline("automatic-speech-recognition", model="ElizabethMwangi/whisper-small-swahili-nss_afrivoice_cv")
result = asr("audio.wav")
print(result["text"])
```

## Training Details

- Fine-tuned using Hugging Face `Seq2SeqTrainer`
- Language token: `sw`
- Learning rate: 5e-5 with polynomial decay
- Batch size: 16
- Total steps: 1000 
- Spec augment: **disabled** (no SpecAugment)


## Citation

If you use this model, please cite the AfriVoice and Kenyan Swahili Non-Standard Speech datasets and this repository.