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Model: SiangLao/xlsr-53-lao-asr
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2026-05-08 11:40:38 +08:00
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---
language: lo
license: apache-2.0
tags:
- automatic-speech-recognition
- speech
- audio
- lao
- wav2vec2
- xlsr
datasets:
- SiangLao/lao-asr-thesis-dataset
metrics:
- cer
base_model:
- facebook/wav2vec2-large-xlsr-53
library_name: transformers
---
# 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](https://huggingface.co/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
```python
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
```bibtex
@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}
}
```

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{
"</s>": 56,
"<s>": 55
}

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{
"activation_dropout": 0.0,
"adapter_attn_dim": null,
"adapter_kernel_size": 3,
"adapter_stride": 2,
"add_adapter": false,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForCTC"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"classifier_proj_size": 256,
"codevector_dim": 768,
"contrastive_logits_temperature": 0.1,
"conv_bias": true,
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512,
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],
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"diversity_loss_weight": 0.1,
"do_stable_layer_norm": true,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.1,
"feat_quantizer_dropout": 0.0,
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"gradient_checkpointing": false,
"hidden_act": "gelu",
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"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"layerdrop": 0.1,
"mask_channel_length": 10,
"mask_channel_min_space": 1,
"mask_channel_other": 0.0,
"mask_channel_prob": 0.0,
"mask_channel_selection": "static",
"mask_feature_length": 10,
"mask_feature_min_masks": 0,
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"mask_time_length": 10,
"mask_time_min_masks": 2,
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"mask_time_other": 0.0,
"mask_time_prob": 0.075,
"mask_time_selection": "static",
"model_type": "wav2vec2",
"num_adapter_layers": 3,
"num_attention_heads": 16,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 24,
"num_negatives": 100,
"output_hidden_size": 1024,
"pad_token_id": 0,
"proj_codevector_dim": 768,
"tdnn_dilation": [
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2,
3,
1,
1
],
"tdnn_dim": [
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1500
],
"tdnn_kernel": [
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],
"torch_dtype": "float32",
"transformers_version": "4.52.4",
"use_weighted_layer_sum": false,
"vocab_size": 57,
"xvector_output_dim": 512
}

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{
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"feature_size": 1,
"padding_side": "right",
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"processor_class": "Wav2Vec2Processor",
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"sampling_rate": 16000
}

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{
"bos_token": "<s>",
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"unk_token": "<unk>"
}

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"word_delimiter_token": "|"
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{
"experiment_info": {
"model_name": "facebook/wav2vec2-large-xlsr-53",
"model_description": "XLSR-53",
"dataset_name": "h3llohihi/lao-asr-thesis-dataset",
"training_date": "2025-06-03 19:11:56",
"vocab_size": 55,
"primary_metric": "cer"
},
"training_config": {
"dataset_name": "h3llohihi/lao-asr-thesis-dataset",
"gradient_accumulation": 1,
"weight_decay": 0.01,
"warmup_steps": 400,
"num_epochs": 15,
"max_steps": -1,
"eval_steps": 250,
"save_steps": 500,
"logging_steps": 50,
"early_stopping_patience": 6,
"dataloader_workers": 8,
"use_fp16": false,
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"label_smoothing": 0.0,
"primary_metric": "cer",
"secondary_metric": "wer",
"logs_dir": "./logs",
"model_name": "facebook/wav2vec2-large-xlsr-53",
"description": "XLSR-53",
"output_dir": "./lao-asr-xlsr-53",
"batch_size": 16,
"learning_rate": 0.0001
},
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"loss": 0.48708805441856384
},
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}
},
"training_metrics": {
"final_loss": 1.7461039689566584,
"training_time": 12829.0071,
"samples_per_second": 4.499,
"total_steps": 0
}
}

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