whisper-large-v3_finetuned_ugandan_luganda_waxal_7_standard_speech_v1.0

Dataset

The model has been fine-tuned using google/WaxalNLP. The lug_asr subset of the dataset was used.

Training

The train split was used for training, and the dev split for selecting the best checkpoint.

This Whisper model was fine-tuned and is decoded using the Swahili (sw) language setting — out of all languages Whisper supports, the one most similar to Luganda.

All model parameters (encoder, decoder, and output projection) were fine-tuned, with SpecAugment enabled.

Evaluation

This model was evaluated on the test split of the dataset. Utterances longer than 30 seconds were excluded:

• Examples evaluated: 503
• Speakers: 194

For decoding we ran Whisper with language=sw, task=transcribe, greedy search (num_beams=1, do_sample=False).

We report two complementary word error rate (WER) metrics, both computed on text normalized with Whisper's BasicTextNormalizer:

• Standard (corpus-level) WER — the usual error rate, pooling all reference words and edit errors across the entire test set.
• Per-utterance averaged WER — WER computed separately for each utterance, each capped at 1.0, then averaged across utterances.

The per-utterance averaged WER bounds each utterance to [0, 1] and weights all utterances equally, so it reflects typical performance without a few catastrophic utterances dominating — but it is not a true error rate and isn't directly comparable to other published WER, hence we report the standard, corpus-level WER as well.

Results

Overall Results