---
library_name: transformers
language:
- zh
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- wft
- whisper
- automatic-speech-recognition
- audio
- speech
- generated_from_trainer
datasets:
- JacobLinCool/common_voice_19_0_zh-TW
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-common_voice_19_0-zh-TW
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: JacobLinCool/common_voice_19_0_zh-TW
      type: JacobLinCool/common_voice_19_0_zh-TW
    metrics:
    - type: wer
      value: 32.55535607420706
      name: Wer
---

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# whisper-large-v3-turbo-common_voice_19_0-zh-TW

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the JacobLinCool/common_voice_19_0_zh-TW dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1786
- Wer: 32.5554
- Cer: 8.6009
- Decode Runtime: 90.9833
- Wer Runtime: 0.1257
- Cer Runtime: 0.1534

## Model description

This is an open-source Traditional Chinese (Taiwan) automatic speech recognition (ASR) model.

## Intended uses & limitations

This model is designed to be a prompt-free ASR model for Traditional Chinese. Due to its inherited language identification (LID) system from Whisper, which supports other Chinese language variants under the same language token (`zh`), we expect that performance may degrade when transcribing Simplified Chinese.

The model is free to use under the MIT license.

## Training and evaluation data

This model was trained on the [Common Voice Corpus 19.0 Chinese (Taiwan) Subset](https://huggingface.co/datasets/JacobLinCool/common_voice_19_0_zh-TW), containing about 50k training examples (44 hours) and 5k test examples (5 hours). This dataset is four times larger than the combination of training and validation set (`train+validation`) of [mozilla-foundation/common_voice_16_1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1), which includes about 12k examples.

## Training procedure

[Tensorboard](https://huggingface.co/JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW-lora/tensorboard)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 50
- training_steps: 5000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     | Cer     | Decode Runtime | Wer Runtime | Cer Runtime |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:--------------:|:-----------:|:-----------:|
| No log        | 0      | 0    | 2.7208          | 76.5011 | 20.4851 | 89.4916        | 0.1213      | 0.1639      |
| 1.1832        | 0.1    | 500  | 0.1939          | 39.9561 | 10.8721 | 90.0926        | 0.1222      | 0.1555      |
| 1.5179        | 0.2    | 1000 | 0.1774          | 37.6621 | 9.9322  | 89.8657        | 0.1225      | 0.1545      |
| 0.6179        | 0.3    | 1500 | 0.1796          | 36.2657 | 9.8325  | 90.2480        | 0.1198      | 0.1573      |
| 0.3626        | 1.0912 | 2000 | 0.1846          | 36.2258 | 9.7801  | 90.3306        | 0.1196      | 0.1539      |
| 0.1311        | 1.1912 | 2500 | 0.1776          | 34.8095 | 9.3214  | 90.3124        | 0.1286      | 0.1610      |
| 0.1263        | 1.2912 | 3000 | 0.1763          | 36.1261 | 9.3563  | 90.4271        | 0.1330      | 0.1650      |
| 0.2194        | 2.0825 | 3500 | 0.1891          | 34.6898 | 9.3114  | 91.1932        | 0.1320      | 0.1643      |
| 0.1127        | 2.1825 | 4000 | 0.1838          | 34.0714 | 9.1095  | 90.2416        | 0.1196      | 0.1529      |
| 0.3792        | 2.2824 | 4500 | 0.1786          | 33.1339 | 8.7679  | 90.9144        | 0.1310      | 0.1550      |
| 0.0606        | 3.0737 | 5000 | 0.1786          | 32.5554 | 8.6009  | 90.9833        | 0.1257      | 0.1534      |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.4.0
- Datasets 3.0.2
- Tokenizers 0.20.1