from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import librosa

# Load model and processor
model = Wav2Vec2ForCTC.from_pretrained("misterkissi/w2v-bert-2.0-oromo-colab-CV1.0")
processor = Wav2Vec2Processor.from_pretrained("misterkissi/w2v-bert-2.0-oromo-colab-CV1.0")

# Load and preprocess audio
audio, rate = librosa.load("audio.wav", sr=16000)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")

# Perform inference
with torch.no_grad():
    logits = model(**inputs).logits
pred_ids = torch.argmax(logits, dim=-1)

# Decode prediction
transcription = processor.batch_decode(pred_ids)[0]
print(transcription)

Model Details

Base model: facebook/w2v-bert-2.0

Fine-tuned on Oromo language data

WER: 0.3745

CER: 0.0687

model-index:

name: w2v-bert-2.0-oromo-colab-CV1.0 results:
- task: type: Automatic Speech Generation metrics:
  - name: WER value: 0.3745

DarliAI/kissi-w2v-bert-2.0-oromo

Wav2Vec2-BERT Model for Oromo ASR

Evaluation

Usage

Model Details

DarliAI/
kissi-w2v-bert-2.0-oromo