metadata
library_name: transformers
language:
- fa
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small fa - Mahdi Aspanani
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
args: 'config: fa, split: test'
metrics:
- name: Wer
type: wer
value: 37.00986074930784
Whisper Small fa - Mahdi Aspanani
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3673
- Wer: 37.0099
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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2929 | 0.4055 | 1000 | 0.4922 | 48.5195 |
| 0.2242 | 0.8110 | 2000 | 0.4087 | 41.2283 |
| 0.1258 | 1.2165 | 3000 | 0.3834 | 38.2551 |
| 0.1075 | 1.6221 | 4000 | 0.3673 | 37.0099 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Tokenizers 0.19.1