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ElizabethMwangi/whisper-large-v3-swahili-afrivoice-cv

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Model Card

Whisper Large V3 — Swahili (AfriVoice All Domains + Common Voice)

Fine-tuned version of openai/whisper-large-v3 for Swahili ASR, trained on all 5 AfriVoice domains combined with CommonVoice Swahili.

Training Details

—Base model: openai/whisper-large-v3
—Language: Swahili (sw)
—Task: Automatic Speech Recognition (ASR)
—Training data: ElizabethMwangi/swahiliafrivoicealldomainsv0.1 (389,599 utts) + cdli/commonvoiceswahili_small (4,548 utts) = 394,147 total
—Full model training: encoder, decoder, and projection updated
—SpecAugment: On
—Learning rate: 1e-5 (polynomial decay, LR end 1e-8, power 4)
—Effective batch size: 32 (batch 4 x grad accum 8)
—Max steps: 1000 (run in two stages: 0-400, then 400-1000)
—Best checkpoint: Step 700 (AfriVoice dev WER 0.2393)
—Output dir: swafrivoicecvtunewhisperlargev0.1

Results

Dataset	WER	CER
AfriVoice dev	0.239	0.089
AfriVoice test	0.244	0.094
CommonVoice Swahili test	0.201	0.049
Swahili NSS test (885 examples)	0.394	0.172

Usage

from transformers import pipeline

asr = pipeline( 'automatic-speech-recognition', model='ElizabethMwangi/whisper-large-v3-swahili-afrivoice-cv' ) result = asr('audio.wav') print(result['text'])

Intended Use

Swahili ASR with a focus on Kenyan speakers, including nonstandard speech patterns. Part of CDLI research into inclusive ASR for African languages.

Citation

If you use this model, please cite the AfriVoice dataset (DigitalUmuganda/Afrivoice_Swahili) and this repository.