metadata
library_name: transformers
language:
- fa
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- vhdm/persian-voice-v1.1
metrics:
- wer
model-index:
- name: vhdm/whisper-v3-turbo-persian-v1.1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: vhdm/persian-voice-v1
type: vhdm/persian-voice-v1.1
args: 'config: fa, split: test'
metrics:
- name: Wer
type: wer
value: 14.065335753176045
vhdm/whisper-v3-turbo-persian-v1.1
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the vhdm/persian-voice-v1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1445
- Wer: 14.0653
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: 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
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.219 | 0.6150 | 1000 | 0.2093 | 22.0750 |
| 0.1191 | 1.2300 | 2000 | 0.1698 | 17.8463 |
| 0.1051 | 1.8450 | 3000 | 0.1485 | 15.7895 |
| 0.0644 | 2.4600 | 4000 | 0.1530 | 16.0375 |
| 0.0289 | 3.0750 | 5000 | 0.1445 | 14.0653 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1