metadata
base_model: openai/whisper-small
datasets:
- common_voice_17_0
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-small-tr
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: tr
split: test
args: tr
metrics:
- type: wer
value: 20.088563399472026
name: Wer
whisper-small-tr
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: 0.2316
- Wer: 20.0886
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 |
|---|---|---|---|---|
| 0.2284 | 0.3447 | 1000 | 0.2814 | 23.9819 |
| 0.1906 | 0.6894 | 2000 | 0.2606 | 22.5598 |
| 0.0945 | 1.0341 | 3000 | 0.2472 | 21.1990 |
| 0.0871 | 1.3788 | 4000 | 0.2405 | 20.6744 |
| 0.0823 | 1.7235 | 5000 | 0.2316 | 20.0886 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu124
- Datasets 3.0.0
- Tokenizers 0.19.1