Whisper Large V3 - Swahili Afrivoice (Education Domain)
Fine-tuned version of openai/whisper-large-v3 on the Swahili Afrivoice education domain dataset.
Training Details
| Parameter | Value |
|---|---|
| Base Model | openai/whisper-large-v3 |
| Language | Swahili (sw) |
| Task | Transcribe |
| Dataset | Afrivoice Swahili - Education Domain |
| Max Steps | 1000 |
| Max Epochs | 5 |
| Batch Size | 4 |
| Gradient Accumulation | 8 (effective batch 32) |
| Learning Rate | 1e-5 |
| LR Scheduler | polynomial (power: 4) |
| LR Warmup Steps | 100 |
| LR End | 1e-8 |
| FP16 | True |
| SpecAugment | Enabled |
| Save Steps | 50 |
| Evaluation Steps | 50 |
| Max Generation Length | 128 |
Results
| Dataset | WER | CER |
|---|---|---|
| Afrivoice Swahili (Dev) | 13.5% | 4.2% |
| Afrivoice Swahili (Test) | 13.5% | 4.2% |
Zero-shot on Non-Standard Speech
| Dataset | WER | CER |
|---|---|---|
| Kenyan Swahili Non-Standard Speech | 46.7% | 21.7% |
Usage
from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="ElizabethMwangi/whisper-large-v3-swahili-afrivoice") result = asr("audio.wav") print(result["text"])
Training Checkpoints
Saved to: /jupyter_kernel/trained_models/sw_afrivoice_tune_whisper_large_2/best_model
Citation
@misc{afrivoice_swahili_whisper, title={Whisper Large V3 Fine-tuned on Swahili Afrivoice Education Domain}, author={Elizabeth Mwangi}, year={2026} }