metadata
language:
- hu
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large-v2 Hu - cleaned
results: []
Whisper Large-v2 Hu - cleaned
This model is a fine-tuned version of openai/whisper-large-v2 on the Common Voice 16.1 hu cleaned dataset. It achieves the following results on the evaluation set:
- Loss: 0.0393
- Wer Ortho: 4.1403
- Wer: 3.5518
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: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 600
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0716 | 0.34 | 100 | 0.0690 | 6.5849 | 5.9493 |
| 0.0539 | 0.69 | 200 | 0.0520 | 4.9650 | 4.4825 |
| 0.0381 | 1.03 | 300 | 0.0457 | 4.4900 | 4.0385 |
| 0.0235 | 1.37 | 400 | 0.0423 | 4.2854 | 3.7458 |
| 0.0221 | 1.72 | 500 | 0.0386 | 3.9786 | 3.5518 |
| 0.0158 | 2.06 | 600 | 0.0393 | 4.1403 | 3.6768 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1