---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Traditional Chinese
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 zh-TW
      type: mozilla-foundation/common_voice_11_0
      config: zh-TW
      split: test
      args: zh-TW
    metrics:
    - name: Wer
      type: wer
      value: 32.04202832343535
---

<!-- 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 Traditional Chinese

This model is a fine-tuned version of [kimbochen/whisper-small-zh-tw](https://huggingface.co/kimbochen/whisper-small-zh-tw) on the mozilla-foundation/common_voice_11_0 zh-TW dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4334
- Wer: 32.0420

## 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: 64
- 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: 1200
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0066        | 2.05  | 400  | 0.3743          | 32.9100 |
| 0.0084        | 5.03  | 800  | 0.3787          | 33.4171 |
| 0.0098        | 8.01  | 1200 | 0.3979          | 33.2481 |
| 0.0019        | 10.06 | 1600 | 0.4084          | 32.3116 |
| 0.0008        | 13.04 | 2000 | 0.4334          | 32.0420 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2