---
library_name: transformers
language:
- kh
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- seanghay/khmer_mpwt_speech
metrics:
- wer
model-index:
- name: Whisper Small - KH
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: seanghay/khmer_mpwt_speech
      type: seanghay/khmer_mpwt_speech
      args: 'config: kh, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 58.29787234042553
---

<!-- 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 Small - KH

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the seanghay/khmer_mpwt_speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3627
- Wer: 58.2979

## 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
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 50
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7064        | 1.3966 | 250  | 0.7823          | 106.1170 |
| 0.4618        | 2.7933 | 500  | 0.5052          | 78.0851  |
| 0.1901        | 4.1899 | 750  | 0.4079          | 64.7340  |
| 0.1137        | 5.5866 | 1000 | 0.3627          | 58.2979  |


### Framework versions

- Transformers 5.15.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2