🎓 New Uzbekistan University — for knowledge, research environment, and academic direction
🏦 Xalq Banki AI Lab — for providing GPU resources and supporting the technical direction of the project

📊 Model Performance

Evaluation was performed on a held-out test set.

Metric	Score
WER (Word Error Rate)	38.59%
CER (Character Error Rate)	9.85%

Test Details

Total samples: 504
Sampling rate: 16 kHz
Task: Transcribe

🧪 Inference Example

python

import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model_id = "Quyashbek/whisper-base-karakalpak"
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)

# speech, sr = librosa.load("audio.wav", sr=16000)
# inputs = processor(speech, sampling_rate=16000, return_tensors="pt")
# generated_ids = model.generate(inputs.input_features)
# transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]