You can deploy this model and start earning money today!

boazsew/
Ethio-ASR-w2v-bert-2.0-uf

Name: Ethio-ASR-w2v-bert-2.0-uf
Availability: InStock

Views

No views yet

Ethio-ASR-w2v-bert-2.0-uf

Reproduction of the paper's best architecture (w2v-BERT-2.0). Matches the paper-published checkpoint within 0.01% macro WER.

This model is part of an independent reproduction of Ethio-ASR (Abdullah et al., 2026) carried out at the University of Florida on the HiPerGator HPC cluster (NVIDIA B200 GPUs). It is fine-tuned from facebook/w2v-bert-2.0 on the WAXAL Ethiopian subset covering Amharic, Tigrinya, Oromo, Sidaama and Wolaytta.

Performance

Word Error Rate on the WAXAL test split (18 810 utterances), with paper-faithful post-processing (Ge'ez homophone collapse + punctuation removal):

Language	WER (%)
Amharic	25.44
Oromo	27.52
Sidaama	30.33
Tigrinya	39.75
Wolaytta	38.29
Macro avg	32.27

Without post-processing the macro WER is 35.76% (raw model output).

Usage

python

from transformers import AutoProcessor, AutoModelForCTC
import torch, librosa

REPO = "boazsew/Ethio-ASR-w2v-bert-2.0-uf"
processor = AutoProcessor.from_pretrained(REPO)
model = AutoModelForCTC.from_pretrained(REPO, torch_dtype=torch.bfloat16).to("cuda").eval()

# Load any 16 kHz audio
audio, sr = librosa.load("your_audio.wav", sr=16000, mono=True)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
key = "input_features" if "input_features" in inputs else "input_values"
in_kwargs = {key: inputs[key].to("cuda", dtype=torch.bfloat16)}
if "attention_mask" in inputs:
    in_kwargs["attention_mask"] = inputs["attention_mask"].to("cuda")

with torch.no_grad():
    logits = model(**in_kwargs).logits
pred_ids = logits.argmax(dim=-1)
text = processor.batch_decode(pred_ids)[0]
print(text)  # starts with [AMH]/[TIR]/[ORM]/[WAL]/[SID] language tag

Training details

Encoder: facebook/w2v-bert-2.0 (600M parameters)
Dataset: badrex/waxalNLP-ethiopic-final (~1106 h, 5 Ethiopian languages)
Max steps: 36 800 (≈7 epochs, effective batch size 32)
Hardware: 1 × NVIDIA B200
Optimizer: AdamW, linear warmup over 10 % of steps, mixed-precision bf16 (except AfriHuBERT which used fp32)
CTC objective with prepended [LANG] token (joint LID + ASR)

Citation

If you use this model please cite both the original paper and the reproduction:

bibtex

@article{abdullah2026ethioasr,
  title={Ethio-ASR: Joint Multilingual Speech Recognition and Language
          Identification for Ethiopian Languages},
  author={Abdullah, Badr M. and others},
  journal={arXiv preprint arXiv:2603.23654},
  year={2026}
}

Full reproduction code, configs and evaluation utilities are at https://github.com/boaztulu/Ethio-ASR under reproduction_uf/.

License

CC-BY-NC-4.0 — inherits from the upstream facebook/w2v-bert-2.0 license.