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tekyerema-asr-mms-kus

Automatic Speech Recognition
Kusaal
ghana-nlp
ghana-speech
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metadata 
license: cc-by-nc-4.0
language:
  - kus
tags:
  - ghana-nlp
  - ghana-speech
  - automatic-speech-recognition
base_model: facebook/mms-1b-all
pipeline_tag: automatic-speech-recognition
model-index:
  - name: tekyerema-asr-mms-kus
    results:
      - task:
          type: automatic-speech-recognition
        dataset:
          name: kusaal_scripture
          type: PrinceAlhassanNasamu/tekyerema-pa-asr-restricted
        metrics:
          - type: wer
            value: 30.44
          - type: cer
            value: 13.52

Kusaal ASR (MMS adapter)

Author: Prince Nasamu Alhassan

Overview

A per-language MMS adapter over facebook/mms-1b-all.

This is now the best Kusaal recogniser in the project. The previous champion, KASA-42, scores 41.0 / 19.2 and cannot be fine-tuned from — it ships raw .pt files and an inference-only int8 ONNX export with no architectures or model_type. This model beats it by 10.6 WER points and can be trained further.

Use it

This repo holds only the adapter (about 9 MB), not a whole model. Load the base and apply it — and take the head width from the adapter, not from the tokenizer: they differ by the added special tokens, and letting the tokenizer decide raises a size mismatch.

AutoConfig.from_pretrained on this repo fails with "Unrecognized model … should have a model_type key", and AutoModelForCTC fails too. That is expected, not a broken upload: there is no model here, only an adapter and its tokenizer. AutoProcessor.from_pretrained does work. A config.json is deliberately not shipped — the base's would advertise a vocabulary of 154 where this head is 58–62 wide, and would invite loading weights that are not in the repo.

import torch, soundfile as sf
from huggingface_hub import hf_hub_download, list_repo_files
from safetensors.torch import load_file
from transformers import AutoProcessor, Wav2Vec2ForCTC

repo = "PrinceAlhassanNasamu/tekyerema-asr-mms-kus"
adapter = [f for f in list_repo_files(repo)
           if f.startswith("adapter.") and f.endswith(".safetensors")][0]
sd = load_file(hf_hub_download(repo, adapter))

proc  = AutoProcessor.from_pretrained(repo)
model = Wav2Vec2ForCTC.from_pretrained(
    "facebook/mms-1b-all",
    vocab_size=sd["lm_head.weight"].shape[0],   # the ADAPTER decides this
    ignore_mismatched_sizes=True).eval()

missing, unexpected = model.load_state_dict(sd, strict=False)
assert not unexpected, unexpected   # never transcribe with a half-loaded model

wav, sr = sf.read("clip.wav", dtype="float32")
inp = proc(wav, sampling_rate=16_000, return_tensors="pt")
with torch.no_grad():
    logits = model(**inp).logits
print(proc.batch_decode(logits.argmax(-1))[0])

Training data

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

Measured

On kusaal_scripture, same items and same scorer as the baseline:

model WER / CER
baseline it was fine-tuned from 59.34 / 22.10
this model 30.44 / 13.52

A WER alone is not informative — compared against another language it means nothing. Compared against the model it started from, it means everything.

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 did get a recogniser, and the claim that it could not was wrong twice over. Every card on this account used to say that "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%. Trained on it, tekyerema-asr-mms-dag scores 36.94 / 11.71, against the 86.59 / 33.95 this project had believed was the ceiling. It still loses to FarmerlineML/w2v-bert-2.0_2026_dagbani_ASR at 29.20 / 9.27, which is what the agent actually serves. A number carried across from a translation table into a speech claim was then repeated on every card here until 2026-09-22.
  • 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

Voices

Agent models

Translation

Routing

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).