metadata
library_name: transformers
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: whister test
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: ko
split: None
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 34.87394957983193
whister test
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5798
- Wer: 34.8739
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: 16
- eval_batch_size: 8
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0008 | 22.2222 | 1000 | 0.4828 | 33.4989 |
| 0.0003 | 44.4444 | 2000 | 0.5277 | 34.2246 |
| 0.0001 | 66.6667 | 3000 | 0.5551 | 34.6830 |
| 0.0001 | 88.8889 | 4000 | 0.5718 | 34.7976 |
| 0.0001 | 111.1111 | 5000 | 0.5798 | 34.8739 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1