afrivoices-edge-asr

wbertcur — Unified Edge ASR for Six East African Languages

A single, edge-deployable speech-to-text model for six East African languages, built for the AfriVoices East Africa ASR Hackathon (Multilingual Edge ASR track).

Edge compliance (validated)

See HARDWARE_VALIDATION_REPORT.md in this repo for the full methodology.

Results

Metric = unweighted mean of per-language WER (macro).

Usage

import numpy as np, torch, pyctcdecode, soundfile as sf
from transformers import Wav2Vec2BertForCTC, Wav2Vec2BertProcessor

REPO = "Pricile/afrivoices-edge-asr"      # self-contained: weights + LMs in this repo
model = Wav2Vec2BertForCTC.from_pretrained(REPO).eval()
proc  = Wav2Vec2BertProcessor.from_pretrained(REPO)

# character vocabulary sorted by index (required by pyctcdecode)
vocab = [t for t, i in sorted(proc.tokenizer.get_vocab().items(), key=lambda x: x[1])]
# per-language 4-gram KenLM binaries are in this repo under lm/ (huggingface_hub.hf_hub_download)
decoder = pyctcdecode.build_ctcdecoder(vocab, kenlm_model_path="lm/som_final_4gram.binary",
                                       alpha=0.5, beta=2.0)   # per-language α/β (see code/ab.json)

audio, sr = sf.read("clip.wav")     # 16 kHz mono
feats = proc.feature_extractor(audio, sampling_rate=16000, return_tensors="pt").input_features
with torch.no_grad():
    logits = model(feats).logits[0].numpy()
print(decoder.decode(logits, beam_width=100))

For CPU edge deployment, apply dynamic int8 quantisation:

torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)

Training (summary)

Fine-tuned from scratch on top of w2v-bert-2.0 with a character-level CTC head, using a length curriculum (max audio 15 s → 30 s → 45 s → 60 s) and language re-weighting (kln×4, mas×4, som×2) since the macro metric is dominated by the low-resource languages. LR 5e-5, cosine schedule, effective batch 32, bf16, EMA (0.9999), seed 42; best at step 16 500. Full methodology and ablations in TECHNICAL_REPORT.md.

Dataset & data card

Trained on AfriVoices-KE (African Next Voices — Pilot Data Collection in Kenya), released by the KenCorpus Consortium (Maseno University, USIU-Africa, Kabarak University, DeKUT, LDRI) and Digital Umuganda, funded by the Gates Foundation. License: CC BY 4.0. The organisers' full data card is reproduced in DATA_CARD.md.

• Competition: AfriVoices East Africa: ASR Hackathon (Kaggle · host: Digital Umuganda)
• Test set: digitalumuganda/anv-test-data-nt · Training sources: Anv-ke (kik/luo/kln/mas/som), DigitalUmuganda/Afrivoice_Swahili, DigitalUmuganda/Afrivoice (Somali Mogadishu)

Composition (organisers' figures):

Splits are speaker-disjoint (train 85 % / dev 5 % / dev_test 5 % / test 5 %). For LM shallow fusion, extra public text was used for Somali (CC-100) and Kalenjin (Zenodo).

Please cite the dataset (Wanzare et al., 2026 — accepted at LREC SIGUL 2026): AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages, arXiv:2604.08448.

Limitations

• Kalenjin and Maasai remain the hardest languages (genuine low-resource acoustic difficulty).
• The KenLM decoder is per-language; language identity is assumed known at inference (or run LID first).
• Trained and evaluated on the AfriVoices-KE distribution (read + spontaneous speech).

License & citation

MIT. Backbone w2v-bert-2.0 is likewise MIT. Please cite the AfriVoices Hackathon and this repository.