---
base_model: biodatlab/whisper-th-medium-combined
datasets:
- common_voice_17_0
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-finetune-th
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: th
      split: None
      args: th
    metrics:
    - type: wer
      value: 15.045342636924866
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whisper-finetune-th

This model is a fine-tuned version of [biodatlab/whisper-th-medium-combined](https://huggingface.co./biodatlab/whisper-th-medium-combined) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1015
- Wer: 15.0453
- Cer: 3.8830

## 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     | Cer    |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|
| 0.2829        | 0.4873 | 1000 | 0.1345          | 20.0856 | 5.3644 |
| 0.1548        | 0.9747 | 2000 | 0.1161          | 17.6348 | 4.5783 |
| 0.1775        | 1.4620 | 3000 | 0.1074          | 15.9448 | 4.1193 |
| 0.1477        | 1.9493 | 4000 | 0.1015          | 15.0453 | 3.8830 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1