---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Fine-tuned Whisper model for Legislative Yuan of Taiwan
  results: []
---

<!-- 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. -->

# Fine-tuned Whisper model for Legislative Yuan of Taiwan

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0186
- Wer: 71.2474

## 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0223        | 0.1442 | 1000 | 0.0215          | 75.2037 |
| 0.0199        | 0.2883 | 2000 | 0.0206          | 74.3679 |
| 0.0194        | 0.4325 | 3000 | 0.0199          | 73.5279 |
| 0.0177        | 0.5766 | 4000 | 0.0189          | 72.0664 |
| 0.0173        | 0.7208 | 5000 | 0.0186          | 71.2474 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.20.1