---
library_name: transformers
language:
- th
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- fsicoli/common_voice_22_0
metrics:
- wer
model-index:
- name: Whisper Small Th - Testhai
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 22.0
      type: fsicoli/common_voice_22_0
      config: th
      split: test[:2%]
      args: 'config: th, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 100.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Small Th - Testhai

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 22.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9453
- Wer: 100.0

## 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: 0.001
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Use adamw_torch_fused 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   |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 3.3587        | 1.0   | 330  | 3.2810          | 100.0 |
| 2.2188        | 2.0   | 660  | 3.0993          | 100.0 |
| 2.4391        | 3.0   | 990  | 2.9453          | 100.0 |


### Framework versions

- Transformers 5.9.0
- Pytorch 2.12.0+cu132
- Datasets 4.8.5
- Tokenizers 0.22.2