metadata
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_v8
results: []
finetune_v8
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: 0.4224
- Wer: 102.2241
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 | 0.5239 | 19.1617 |
| No log | 20.0 | 20 | 0.4346 | 18.0496 |
| No log | 30.0 | 30 | 0.4050 | 17.1942 |
| No log | 40.0 | 40 | 0.4204 | 18.4773 |
| 0.0997 | 50.0 | 50 | 0.4294 | 20.6159 |
| 0.0997 | 60.0 | 60 | 0.4282 | 19.6749 |
| 0.0997 | 70.0 | 70 | 0.4246 | 23.9521 |
| 0.0997 | 80.0 | 80 | 0.4224 | 102.2241 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1