metadata
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_v4
results: []
finetune_v4
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: 0.2085
- Wer: 14.5161
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: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 | 6.6667 | 10 | 0.2449 | 19.4700 |
| No log | 13.3333 | 20 | 0.1970 | 14.5161 |
| No log | 20.0 | 30 | 0.1805 | 11.6359 |
| No log | 26.6667 | 40 | 0.1826 | 14.4009 |
| 0.0538 | 33.3333 | 50 | 0.1930 | 22.1198 |
| 0.0538 | 40.0 | 60 | 0.1967 | 36.5207 |
| 0.0538 | 46.6667 | 70 | 0.2035 | 35.3687 |
| 0.0538 | 53.3333 | 80 | 0.2085 | 14.5161 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1