|
--- |
|
language: |
|
- ko |
|
license: apache-2.0 |
|
tags: |
|
- hf-asr-leaderboard |
|
- generated_from_trainer |
|
datasets: |
|
- unanam/mdrama |
|
base_model: openai/whisper-small |
|
model-index: |
|
- name: whisper-small-ver2 |
|
results: [] |
|
--- |
|
|
|
<!-- 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-ver2 |
|
|
|
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the 사극 드라마 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6016 |
|
- Cer: 15.2224 |
|
|
|
## 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: 20 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- training_steps: 4000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Cer | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:| |
|
| 0.1387 | 3.5 | 500 | 0.4537 | 16.3581 | |
|
| 0.0218 | 6.99 | 1000 | 0.5024 | 16.0241 | |
|
| 0.0041 | 10.49 | 1500 | 0.5310 | 15.2224 | |
|
| 0.0028 | 13.99 | 2000 | 0.5554 | 18.7918 | |
|
| 0.0009 | 17.48 | 2500 | 0.5743 | 14.9265 | |
|
| 0.0012 | 20.98 | 3000 | 0.5883 | 15.2510 | |
|
| 0.0006 | 24.48 | 3500 | 0.5982 | 15.2128 | |
|
| 0.0006 | 27.97 | 4000 | 0.6016 | 15.2224 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.0.dev0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.17.1 |
|
- Tokenizers 0.15.1 |
|
|
