Today we're releasing whisper-som-small, our first public Somali ASR checkpoint. Fine-tuned from OpenAI's Whisper-small on 320 hours of aligned Somali audio, it achieves 11.4% WER on held-out radio speech — a 38% relative improvement over the zero-shot multilingual baseline.
Background
Whisper is a strong multilingual ASR model, but Somali is severely underrepresented in its training data. The zero-shot Whisper-small baseline on our radio test set gives 18.4% WER — usable in some contexts, but not reliable enough for production transcription or downstream NLP tasks that depend on accurate text.
Fine-tuning on in-domain data is the obvious fix. The challenge is data: high-quality aligned speech is expensive to produce. Our SomNLP-STT-Corpus, released last year, gave us the raw material to do this properly.
The dataset
SomNLP-STT-Corpus contains 320 hours of Somali audio across four sources: radio broadcasts (Radio Mogadishu, HornAfrik), government speeches, storytelling recordings, and community-contributed reading passages.
320h
Total audio
280h
Training split
20h
Validation
20h
Test split
Word-level alignment was generated using Montreal Forced Aligner with our custom Somali acoustic model, then manually corrected on a 10% random sample. Alignment accuracy on the corrected sample was 96%.