80 lines
1.9 KiB
Markdown
80 lines
1.9 KiB
Markdown
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---
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language: lo
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license: apache-2.0
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tags:
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- automatic-speech-recognition
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- speech
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- audio
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- lao
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- wav2vec2
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- xlsr
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datasets:
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- SiangLao/lao-asr-thesis-dataset
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metrics:
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- cer
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base_model:
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- facebook/wav2vec2-large-xlsr-53
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library_name: transformers
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---
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# XLSR-53 Lao ASR
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Fine-tuned XLSR-53 model for Lao automatic speech recognition, achieving 16.22% CER on test data.
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## Model Details
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This model is fine-tuned from [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) using the SiangLao/lao-asr-thesis-dataset.
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### Training Configuration
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- **Epochs**: 15
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- **Batch Size**: 16
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- **Learning Rate**: 1e-4
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- **Training Date**: June 3, 2025
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- **Vocabulary Size**: 55 Lao characters + special tokens
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### Performance
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| Split | CER | Loss |
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|-------|-----|------|
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| Test | 16.22% | 0.419 |
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| Validation | 16.52% | 0.487 |
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## Usage
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```python
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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import torch
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import librosa
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# Load model and processor
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model = Wav2Vec2ForCTC.from_pretrained("SiangLao/xlsr-53-lao-asr")
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processor = Wav2Vec2Processor.from_pretrained("SiangLao/xlsr-53-lao-asr")
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# Load audio (must be 16kHz)
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audio, sr = librosa.load("audio.wav", sr=16000)
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# Process audio
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inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
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# Generate prediction
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with torch.no_grad():
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logits = model(**inputs).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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transcription = processor.batch_decode(predicted_ids)[0]
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# Clean transcription
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transcription = transcription.replace("<unk>", " ").strip()
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print(transcription)
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```
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## Citation
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```bibtex
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@thesis{naovalath2025lao,
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title={Lao Automatic Speech Recognition using Transfer Learning},
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author={Souphaxay Naovalath and Sounmy Chanthavong},
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advisor={Dr. Somsack Inthasone},
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school={National University of Laos, Faculty of Natural Sciences, Computer Science Department},
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year={2025}
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}
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```
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