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---
library_name: peft
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-lora
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
---
<!-- 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-large-v3-turbo-common_voice_19_0-zh-TW-lora
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 |