metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-small.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: 4.889121133936445
English Whisper Model
This model is a fine-tuned version of openai/whisper-small.en on the Medical dataset. It achieves the following results on the evaluation set:
- Loss: 0.0952
- Wer: 4.8891
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: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7956 | 0.2825 | 100 | 0.7275 | 8.0048 |
| 0.5277 | 0.5650 | 200 | 0.5046 | 6.2706 |
| 0.2247 | 0.8475 | 300 | 0.1916 | 6.1988 |
| 0.0883 | 1.1299 | 400 | 0.1251 | 5.5880 |
| 0.0735 | 1.4124 | 500 | 0.1173 | 5.0883 |
| 0.0752 | 1.6949 | 600 | 0.1080 | 4.9120 |
| 0.0689 | 1.9774 | 700 | 0.0975 | 4.5233 |
| 0.0401 | 2.2599 | 800 | 0.0992 | 4.4613 |
| 0.0364 | 2.5424 | 900 | 0.0966 | 4.7291 |
| 0.0345 | 2.8249 | 1000 | 0.0952 | 4.8891 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1