---
library_name: transformers
license: mit
base_model: Vira21/Whisper-Base-KhmerV2
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: whisper-base-khmer-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: km_kh
      split: test
      args: km_kh
    metrics:
    - name: Wer
      type: wer
      value: 0.609560191987462
---

<!-- 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. -->

# whisper-base-khmer-v2

This model is a fine-tuned version of [Vira21/Whisper-Base-KhmerV2](https://huggingface.co/Vira21/Whisper-Base-KhmerV2) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2003
- Wer: 0.6096

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.1065        | 0.3171 | 50   | 0.2027          | 0.6121 |
| 0.0973        | 0.6342 | 100  | 0.2014          | 0.6138 |
| 0.0933        | 0.9512 | 150  | 0.2003          | 0.6096 |
| 0.0816        | 1.2727 | 200  | 0.2020          | 0.6125 |
| 0.0767        | 1.5898 | 250  | 0.2025          | 0.6131 |
| 0.0782        | 1.9069 | 300  | 0.2027          | 0.6096 |
| 0.0728        | 2.2283 | 350  | 0.2044          | 0.6097 |
| 0.0692        | 2.5454 | 400  | 0.2043          | 0.6131 |
| 0.0685        | 2.8625 | 450  | 0.2043          | 0.6125 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0