metadata
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_v10
results: []
finetune_v10
This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7183
- Wer: 28.6570
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: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 80
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 10.0 | 10 | 0.7852 | 45.9367 |
| No log | 20.0 | 20 | 0.7061 | 24.1232 |
| No log | 30.0 | 30 | 0.6899 | 32.0787 |
| No log | 40.0 | 40 | 0.7144 | 31.9932 |
| 0.1273 | 50.0 | 50 | 0.7314 | 27.6305 |
| 0.1273 | 60.0 | 60 | 0.7285 | 27.5449 |
| 0.1273 | 70.0 | 70 | 0.7554 | 54.1488 |
| 0.1273 | 80.0 | 80 | 0.7183 | 28.6570 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1