---
library_name: transformers
language:
- th
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Large v3 Thai Finetuned
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: th
      split: None
      args: 'config: th, split: train'
    metrics:
    - type: wer
      value: 37.14119683781068
      name: Wer
---

<!-- 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 Thai Finetuned

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2345
- Cer: 10.6496
- Wer: 37.1412

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Cer      | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:-------:|
| 0.2027        | 0.4873 | 500   | 0.1805          | 107.2858 | 75.0935 |
| 0.1674        | 0.9747 | 1000  | 0.1508          | 8.7078   | 41.0794 |
| 0.1073        | 1.4620 | 1500  | 0.1506          | 38.7265  | 45.4534 |
| 0.1035        | 1.9493 | 2000  | 0.1372          | 10.7331  | 38.5129 |
| 0.0587        | 2.4366 | 2500  | 0.1438          | 16.8383  | 50.0563 |
| 0.0627        | 2.9240 | 3000  | 0.1397          | 10.6251  | 31.3447 |
| 0.0356        | 3.4113 | 3500  | 0.1497          | 7.8515   | 33.7998 |
| 0.0367        | 3.8986 | 4000  | 0.1456          | 18.7090  | 37.0359 |
| 0.0184        | 4.3860 | 4500  | 0.1606          | 39.3584  | 93.1345 |
| 0.0204        | 4.8733 | 5000  | 0.1596          | 8.4796   | 31.7272 |
| 0.0112        | 5.3606 | 5500  | 0.1730          | 4.8027   | 25.0106 |
| 0.0119        | 5.8480 | 6000  | 0.1697          | 36.5628  | 82.3949 |
| 0.0057        | 6.3353 | 6500  | 0.1800          | 17.5990  | 50.1931 |
| 0.0052        | 6.8226 | 7000  | 0.1789          | 48.1183  | 98.1247 |
| 0.003         | 7.3099 | 7500  | 0.1960          | 15.7676  | 41.7634 |
| 0.0028        | 7.7973 | 8000  | 0.1980          | 15.2090  | 54.8407 |
| 0.001         | 8.2846 | 8500  | 0.2091          | 21.4387  | 68.7365 |
| 0.001         | 8.7719 | 9000  | 0.2175          | 11.7533  | 40.0988 |
| 0.0001        | 9.2593 | 9500  | 0.2327          | 13.1280  | 40.6133 |
| 0.0001        | 9.7466 | 10000 | 0.2345          | 10.6496  | 37.1412 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1