metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- deepinfinityai/30_report_sentences_dataset
metrics:
- wer
model-index:
- name: Whisper_Large_30_sent_Model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: 11 Sentences
type: deepinfinityai/30_report_sentences_dataset
metrics:
- name: Wer
type: wer
value: 169.6969696969697
Whisper_Large_30_sent_Model
This model is a fine-tuned version of openai/whisper-large on the 11 Sentences dataset. It achieves the following results on the evaluation set:
- Loss: 0.8472
- Wer: 169.6970
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.3891 | 8.3333 | 50 | 1.2466 | 21.2121 |
| 0.0553 | 16.6667 | 100 | 0.1580 | 18.1818 |
| 0.0002 | 25.0 | 150 | 0.1879 | 157.5758 |
| 0.0002 | 33.3333 | 200 | 0.2462 | 87.8788 |
| 0.0001 | 41.6667 | 250 | 0.3595 | 200.0 |
| 0.0001 | 50.0 | 300 | 0.5265 | 190.9091 |
| 0.0001 | 58.3333 | 350 | 0.6597 | 184.8485 |
| 0.0001 | 66.6667 | 400 | 0.7327 | 175.7576 |
| 0.0001 | 75.0 | 450 | 0.8169 | 172.7273 |
| 0.0001 | 83.3333 | 500 | 0.8472 | 169.6970 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0