---
language:
- th
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large V3 Th - Chee Li
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: th
      split: None
      args: 'config: th split: test'
    metrics:
    - name: Wer
      type: wer
      value: 1243.2612539099687
---

<!-- 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 Large V3 Th - Chee Li

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0898
- Wer: 1243.2613

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer       |
|:-------------:|:------:|:----:|:---------------:|:---------:|
| 0.1662        | 0.3740 | 1000 | 0.1388          | 865.8439  |
| 0.1298        | 0.7479 | 2000 | 0.1118          | 998.3068  |
| 0.062         | 1.1219 | 3000 | 0.0996          | 1036.6313 |
| 0.0609        | 1.4959 | 4000 | 0.0898          | 1243.2613 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1