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ElizabethMwangi/
whisper-small-swahili-afrivoice_cv

Name: whisper-small-swahili-afrivoice_cv
Availability: InStock

safetensors

whisper

automatic-speech-recognition

swahili

low-resource

ElizabethMwangi/swahili_afrivoice_all_domains_v0.1

cdli/common_voice_swahili_small

openai/whisper-small

finetune

apache-2.0

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Whisper Small — Swahili (AfriVoice + CommonVoice, No SpecAugment)

Fine-tuned version of openai/whisper-small for automatic speech recognition of Swahili, trained on standard speech from the AfriVoice Swahili dataset and CommonVoice Swahili.

This is Stage 1 of a two-stage fine-tuning pipeline. Stage 2 further fine-tunes this model on non-standard Swahili speech — see ElizabethMwangi/whisper-small-swahili-nss_afrivoice_cv.

Model Details

Base model: openai/whisper-small
Language: Swahili (sw)
Task: Automatic Speech Recognition (ASR)
Training data: AfriVoice Swahili + CommonVoice Swahili

Evaluation Results (AfriVoice Swahili test set)

Metric	Value
WER (adjusted, normalised)	47.41%
CER (adjusted, normalised)	19.82%
WER (unadjusted, normalised)	48.02%
CER (unadjusted, normalised)	19.99%

Usage

python

from transformers import pipeline

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

Training Details

Fine-tuned using Hugging Face Seq2SeqTrainer
Language token: sw
Learning rate: 1e-5 with polynomial decay
Batch size: 32
Total steps: 1000
Spec augment: disabled (no SpecAugment)

Citation

If you use this model, please cite the AfriVoice Swahili dataset and this repository.