metadata
library_name: transformers
license: cc-by-4.0
datasets:
- badrex/ethiopian-speech-flat
Automatic Speech Recognition for Wolaytta 🇪🇹
🍇 Model Description
This is a Automatic Speech Recognition (ASR) model for Wolaytta, one of the official languages of Ethiopia. It is fine‑tuned from Wav2Vec2‑BERT 2.0 using the Ethio speech corpus.
- Developed by: Badr al-Absi
- Model type: Speech Recognition (ASR)
- Languages: Wolaytta
- License: CC-BY-4.0
- Finetuned from: facebook/w2v-bert-2.0
🎧 Direct Use
from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
import torchaudio, torch
processor = Wav2Vec2BertProcessor.from_pretrained("badrex/w2v-bert-2.0-wolaytta-asr")
model = Wav2Vec2BertForCTC.from_pretrained("badrex/w2v-bert-2.0-wolaytta-asr")
audio, sr = torchaudio.load("audio.wav")
inputs = processor(audio.squeeze(), sampling_rate=sr, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
pred_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(pred_ids)[0]
print(transcription)
🔧 Downstream Use
- Voice assistants
- Accessibility tools
- Research baselines
🚫 Out‑of‑Scope Use
- Other languages besides Wolaytta
- High‑stakes deployments without human review
- Noisy audio without further tuning
⚠️ Risks & Limitations
Performance varies with accents, dialects, and recording quality.
📌 Citation
@misc{w2v_bert_ethiopian_asr,
author = {Badr M. Abdullah},
title = {Fine-tuning Wav2Vec2-BERT 2.0 for Ethiopian ASR},
year = {2025},
url = {https://huggingface.co/badrex/w2v-bert-2.0-wolaytta-asr}
}