An earlier Kusaal experiment from 2026-07-19, kept for history. Superseded by tekyerema-asr-mms-kus, which scores 30.44 WER against the 41.0 of the previous champion.

Its README cites a dataset at alhassanprince/kusaal-asr-dataset, which does not exist — the real one is under PrinceAlhassanNasamu/. It predates the H200 window, so it correctly carries no AISCA acknowledgement.

Use it

No loading snippet for this model yet.

Training data

Trained on the Ghana Speech dataset and related Ghanaian corpora, licensed CC BY-NC 4.0.

Intended use & license

Non-commercial use only (CC BY-NC 4.0). This is inherited from the training data and required by the terms under which the compute was granted: models trained in that window are non-commercial by condition of access, not by inference.

Limitations, stated plainly

Dagbani had no recogniser of its own for this whole project, and the reason given for that was wrong. Every card here said "one fine-tuning session on 74 validation rows would not change that". Those 74 rows are the eng-dag machine-translation validation split. The Dagbani speech data in this same account is waxal_dag: 13,228 training rows, 1,750 validation rows, ~71 hours, 1,041 speakers with the largest at 1% — more data and better speaker diversity than Ewe, which produced a working 42.19 WER recogniser. A number was carried across from a translation table into a speech claim, and then repeated on every model card on the account. It is training now, on 2026-08-31. Until it is scored, the honest statement is that Dagbani's best available recogniser scores 86.6 WER and nobody had tried fine-tuning on the data already in hand.
Evaluation is on read and machine-translated text. No recordings of people speaking agent commands in these languages exist. Numbers measured this way are optimistic about phrasing and pessimistic about code-switching, and should not be read as field performance.
Research work from a hackathon entry, not a supported product.

The rest of the family

Recognisers

whisper-large-v3-turbo-tekyerema-eng-foundation — Ghanaian English ASR — course 1 (foundation)
kusaal-whisper-small-lora — Kusaal ASR (Whisper-small LoRA, superseded)
kasa42-asr — KASA-42 (Kusaal, third-party export)
tekyerema-asr-ctc — Twi ASR (w2v-BERT CTC)
tekyerema-asr-mms-ewe — Ewe ASR (MMS adapter)
tekyerema-asr-mms-dag — Dagbani ASR (MMS adapter)
tekyerema-asr-mms-hau — Hausa ASR (MMS adapter)
tekyerema-asr-mms-kus — Kusaal ASR (MMS adapter)
whisper-large-v3-turbo-tekyerema-eng — Ghanaian English ASR (Whisper large-v3-turbo)

Voices

tekyerema-tts-twi — Twi TTS (VITS)
tekyerema-tts-kus — Kusaal TTS (VITS)
tekyerema-tts-ewe — Ewe TTS (VITS)
tekyerema-tts-hau — Hausa TTS (VITS)
tekyerema-tts-eng — Ghanaian English TTS (VITS)

Agent models

tekyerema-1-reply — Tɛkyerɛma-1 reply adapter (arm ①)
tekyerema-1-native-reply — Tɛkyerɛma-1 reply adapter (arm ②)
tekyerema-1-tool — Tɛkyerɛma-1 tool adapter (arm 1)
tekyerema-audio-native — Tɛkyerɛma-1 audio-native (arm 3)
tekyerema-audio-native-4k — Tɛkyerɛma-1 audio-native, 4,000 clips (arm 3 v2)
tekyerema-1-native-tool — Tɛkyerɛma-1 tool adapter (arm 2)

Translation

tekyerema-nllb600m-v1 — Tɛkyerɛma MT v1 (NLLB-600M)
kusaal-nllb-600M — Kusaal MT specialist (NLLB-600M)

Routing

tekyerema-intent-afroxlmr — Intent classifier (AfroXLMR)

Acknowledgements

Compute resources provided by AI Skills and Compute Africa (AISCA). Trained on the Ghana NLP H200 GPU. Please keep derivatives non-commercial and share improvements back with the Ghana NLP community (ghananlpcommunity).

PrinceAlhassanNasamu/kusaal-whisper-small-lora

Kusaal ASR (Whisper-small LoRA, superseded)

Overview