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Model: SiangLao/xlsr-53-lao-asr
Source: Original Platform

.gitattributes

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README.md

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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}
+}
+```

added_tokens.json

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

config.json

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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,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": false,
+  "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,
+  "final_dropout": 0.0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "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,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_min_space": 1,
+  "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": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 57,
+  "xvector_output_dim": 512
+}

model.safetensors

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+oid sha256:b0e780c6fa0a9d4409d479f379a3d5829a3f32949973c2365234bd6e04909834
+size 1262041180

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0,
+  "processor_class": "Wav2Vec2Processor",
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "1": {
+      "content": "<unk>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "55": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "56": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "processor_class": "Wav2Vec2Processor",
+  "replace_word_delimiter_char": " ",
+  "target_lang": null,
+  "tokenizer_class": "Wav2Vec2CTCTokenizer",
+  "unk_token": "<unk>",
+  "word_delimiter_token": "|"
+}

training_results.json

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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,
+    "gradient_checkpointing": true,
+    "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
+  },
+  "results": {
+    "validation": {
+      "cer": 0.16520911038806443,
+      "wer": 1.0,
+      "loss": 0.48708805441856384
+    },
+    "test": {
+      "cer": 0.16219106723220977,
+      "wer": 0.9958333333333333,
+      "loss": 0.41934534907341003
+    },
+    "dev": {
+      "cer": 0.10665818490245971,
+      "wer": 0.975,
+      "loss": 0.1934221088886261
+    }
+  },
+  "training_metrics": {
+    "final_loss": 1.7461039689566584,
+    "training_time": 12829.0071,
+    "samples_per_second": 4.499,
+    "total_steps": 0
+  }
+}

vocab.json

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+{
+  " ": 2,
+  "<pad>": 0,
+  "<unk>": 1,
+  "ກ": 3,
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+  "່": 49,
+  "້": 50,
+  "໋": 51,
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+  "ໜ": 53,
+  "ໝ": 54
+}