--- library_name: transformers license: cc-by-4.0 datasets: - badrex/ethiopian-speech-flat ---
## 🍇 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](https://huggingface.co/datasets/badrex/ethiopian-speech-flat). - **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 ``` python 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 ``` bibtex @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} } ```