---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-chinese-tw-minnan
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: nan-tw
      split: test
      args: nan-tw
    metrics:
    - name: Wer
      type: wer
      value: 96.9298245614035
---

<!-- 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-small-chinese-tw-minnan

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1626
- Wer: 96.9298
- Cer: 30.1452

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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      | Cer     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------:|
| 0.106         | 3.6364  | 1000 | 0.8017          | 110.0877 | 37.4445 |
| 0.0094        | 7.2727  | 2000 | 0.9844          | 101.9424 | 37.1209 |
| 0.0007        | 10.9091 | 3000 | 1.0714          | 95.8020  | 31.5374 |
| 0.0002        | 14.5455 | 4000 | 1.1310          | 96.0526  | 29.5809 |
| 0.0001        | 18.1818 | 5000 | 1.1626          | 96.9298  | 30.1452 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3