README.md

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: 6.286946013912929

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: 0.1269
• Wer: 6.2869

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: 1100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.2361	0.2825	100	1.0425	10.4870
0.6631	0.5650	200	0.6451	9.4908
0.419	0.8475	300	0.3854	8.5535
0.1538	1.1299	400	0.1895	7.2635
0.1234	1.4124	500	0.1644	6.8454
0.1134	1.6949	600	0.1470	6.6201
0.1071	1.9774	700	0.1358	6.0289
0.0721	2.2599	800	0.1329	6.1302
0.0693	2.5424	900	0.1299	6.3065
0.0635	2.8249	1000	0.1275	6.5025
0.0441	3.1073	1100	0.1269	6.2869

Framework versions

• Transformers 4.43.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1