🎙️ Wav2Vec2 XLS-R 300M Karakalpak ASR
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Automatic Speech Recognition (ASR) in the Karakalpak language.
👤 Founder
Quyashbek Allanazarov
🤝 Acknowledgements
- 🎓 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) | 21.21% |
| CER (Character Error Rate) | 4.34% |
Test Details
- Total samples: 504
- Successful transcriptions: 504
- Missing files: 0
- Errors: 0
- Sampling rate: 16 kHz
⚙️ Model Details
- Base model:
facebook/wav2vec2-xls-r-300m - Architecture: Wav2Vec2ForCTC
- Language: Karakalpak
- Task: Speech-to-Text (ASR)
- Framework: PyTorch + Hugging Face Transformers
🧪 Inference Example
import torch
import librosa
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
model_id = "Quyashbek/wav2vec2-xls-r-300m-karakalpak-asr"
processor = Wav2Vec2Processor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)
speech, sr = librosa.load("your_audio.wav", sr=16000)
inputs = processor(speech, sampling_rate=16000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values).logits
pred_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(pred_ids)[0]
print(transcription)