metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny.en
tags:
- generated_from_trainer
datasets:
- Dev372/Medical_STT_Dataset_1.1
metrics:
- wer
model-index:
- name: English Whisper Model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Medical
type: Dev372/Medical_STT_Dataset_1.1
args: 'split: test'
metrics:
- name: Wer
type: wer
value: 6.554753584375714
English Whisper Model
This model is a fine-tuned version of openai/whisper-tiny.en on the Medical dataset. It achieves the following results on the evaluation set:
- Loss: 0.1509
- Wer: 6.5548
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: 18
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.3263 | 0.2825 | 100 | 1.1474 | 12.0219 |
| 0.8292 | 0.5650 | 200 | 0.8086 | 9.9840 |
| 0.5971 | 0.8475 | 300 | 0.5736 | 9.0597 |
| 0.2888 | 1.1299 | 400 | 0.3038 | 8.2465 |
| 0.172 | 1.4124 | 500 | 0.2112 | 7.5835 |
| 0.1499 | 1.6949 | 600 | 0.1839 | 7.0773 |
| 0.1347 | 1.9774 | 700 | 0.1693 | 6.6691 |
| 0.0977 | 2.2599 | 800 | 0.1650 | 6.7834 |
| 0.0966 | 2.5424 | 900 | 0.1578 | 7.0381 |
| 0.0877 | 2.8249 | 1000 | 0.1542 | 6.6462 |
| 0.0587 | 3.1073 | 1100 | 0.1539 | 6.5090 |
| 0.0642 | 3.3898 | 1200 | 0.1531 | 6.5646 |
| 0.0597 | 3.6723 | 1300 | 0.1518 | 6.5090 |
| 0.0754 | 3.9548 | 1400 | 0.1511 | 6.5254 |
| 0.0506 | 4.2373 | 1500 | 0.1509 | 6.5548 |
Framework versions
- Transformers 4.43.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1