metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- yashtiwari/PaulMooney-Medical-ASR-Data
metrics:
- wer
model-index:
- name: Whisper Medium Medical
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Medical ASR
type: yashtiwari/PaulMooney-Medical-ASR-Data
metrics:
- name: Wer
type: wer
value: 19.526912865073616
Whisper Medium Medical
This model is a fine-tuned version of openai/whisper-medium on the Medical ASR dataset. It achieves the following results on the evaluation set:
- Loss: 0.0829
- Wer: 19.5269
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: 32
- 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: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3456 | 0.5405 | 100 | 0.2379 | 17.6684 |
| 0.1514 | 1.0811 | 200 | 0.1298 | 15.7615 |
| 0.0846 | 1.6216 | 300 | 0.0976 | 19.7924 |
| 0.0479 | 2.1622 | 400 | 0.0881 | 19.3338 |
| 0.0272 | 2.7027 | 500 | 0.0829 | 19.5269 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0