SiangLao/xlsr-53-lao-asr

XLSR-53 Lao ASR

Fine-tuned XLSR-53 model for Lao automatic speech recognition, achieving 16.22% CER on test data.

Model Details

This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 using the SiangLao/lao-asr-thesis-dataset.

Training Configuration

• Epochs: 15
• Batch Size: 16
• Learning Rate: 1e-4
• Training Date: June 3, 2025
• Vocabulary Size: 55 Lao characters + special tokens

Performance

Split	CER	Loss
Test	16.22%	0.419
Validation	16.52%	0.487

Usage

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import librosa

# Load model and processor
model = Wav2Vec2ForCTC.from_pretrained("SiangLao/xlsr-53-lao-asr")
processor = Wav2Vec2Processor.from_pretrained("SiangLao/xlsr-53-lao-asr")

# Load audio (must be 16kHz)
audio, sr = librosa.load("audio.wav", sr=16000)

# Process audio
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")

# Generate prediction
with torch.no_grad():
    logits = model(**inputs).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)[0]

# Clean transcription
transcription = transcription.replace("<unk>", " ").strip()

print(transcription)

Citation

@thesis{naovalath2025lao,
  title={Lao Automatic Speech Recognition using Transfer Learning},
  author={Souphaxay Naovalath and Sounmy Chanthavong},
  advisor={Dr. Somsack Inthasone},
  school={National University of Laos, Faculty of Natural Sciences, Computer Science Department},
  year={2025}
}