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---
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-finetuned-nan-tw-v3-torbo-20-epoch
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: nan-tw
split: test
args: nan-tw
metrics:
- name: Wer
type: wer
value: 100.0
---
<!-- 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-finetuned-nan-tw-v3-torbo-20-epoch
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2611
- Wer: 100.0
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.2238 | 1.0616 | 500 | 0.2174 | 100.0 |
| 0.1871 | 2.1233 | 1000 | 0.2099 | 100.0 |
| 0.1606 | 3.1849 | 1500 | 0.2050 | 100.0 |
| 0.1315 | 4.2465 | 2000 | 0.2134 | 100.0 |
| 0.1087 | 5.3082 | 2500 | 0.2247 | 100.0 |
| 0.0969 | 6.3698 | 3000 | 0.2269 | 100.0 |
| 0.0851 | 7.4315 | 3500 | 0.2381 | 100.0 |
| 0.0793 | 8.4931 | 4000 | 0.2412 | 100.0 |
| 0.0737 | 9.5547 | 4500 | 0.2406 | 100.0 |
| 0.07 | 10.6164 | 5000 | 0.2452 | 99.9569 |
| 0.0669 | 11.6780 | 5500 | 0.2435 | 100.0 |
| 0.0623 | 12.7396 | 6000 | 0.2447 | 100.0 |
| 0.0605 | 13.8013 | 6500 | 0.2490 | 100.0 |
| 0.0563 | 14.8629 | 7000 | 0.2524 | 99.9569 |
| 0.0525 | 15.9245 | 7500 | 0.2536 | 100.0 |
| 0.0485 | 16.9862 | 8000 | 0.2554 | 100.0 |
| 0.0435 | 18.0468 | 8500 | 0.2591 | 100.0 |
| 0.0386 | 19.1084 | 9000 | 0.2611 | 100.0 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
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