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S-Sethisak
/
xlsr

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Model card Files
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 Metrics Community
xlsr
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---
library_name: transformers
base_model: S-Sethisak/xlsr-khmer-fleur-ex02
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: xlsr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: km_kh
      split: None
      args: km_kh
    metrics:
    - name: Wer
      type: wer
      value: 0.6776300222422034
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlsr

This model is a fine-tuned version of [S-Sethisak/xlsr-khmer-fleur-ex02](https://huggingface.co/S-Sethisak/xlsr-khmer-fleur-ex02) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8011
- Wer: 0.6776

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6.25e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.1893        | 0.1434 | 400  | 1.3998          | 1.0    |
| 1.7694        | 0.2867 | 800  | 0.9742          | 0.9704 |
| 1.7196        | 0.4301 | 1200 | 0.8980          | 0.7788 |
| 1.8691        | 0.5735 | 1600 | 0.8685          | 0.7422 |
| 1.8432        | 0.7168 | 2000 | 0.8528          | 0.7295 |
| 1.8607        | 0.8602 | 2400 | 0.8395          | 0.7231 |
| 1.7744        | 1.0036 | 2800 | 0.8338          | 0.7122 |
| 1.6846        | 1.1470 | 3200 | 0.8259          | 0.7024 |
| 1.7989        | 1.2903 | 3600 | 0.8297          | 0.6974 |
| 1.5462        | 1.4337 | 4000 | 0.8212          | 0.6938 |
| 1.6145        | 1.5771 | 4400 | 0.8214          | 0.6908 |
| 1.4987        | 1.7204 | 4800 | 0.8172          | 0.6854 |
| 1.5861        | 1.8638 | 5200 | 0.8185          | 0.6835 |
| 1.6129        | 2.0072 | 5600 | 0.8144          | 0.6810 |
| 1.6523        | 2.1505 | 6000 | 0.8170          | 0.6788 |
| 1.5069        | 2.2939 | 6400 | 0.8116          | 0.6793 |
| 1.5815        | 2.4373 | 6800 | 0.8113          | 0.6780 |
| 1.4807        | 2.5806 | 7200 | 0.8069          | 0.6768 |
| 1.6869        | 2.7240 | 7600 | 0.8024          | 0.6777 |
| 1.712         | 2.8674 | 8000 | 0.8011          | 0.6776 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1