README.md

metadata

language:
  - en
license: apache-2.0
base_model: openai/whisper-small.en
tags:
  - generated_from_trainer
datasets:
  - Hani89/medical_asr_recording_dataset
metrics:
  - wer
model-index:
  - name: English Whisper Model
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Medical
          type: Hani89/medical_asr_recording_dataset
          args: 'split: test'
        metrics:
          - name: Wer
            type: wer
            value: 7.0236794171220405

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: 0.1122
• Wer: 7.0237

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: 3.5e-05
• train_batch_size: 16
• 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: 2000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0076	3.0030	1000	0.1181	7.3734
0.0003	6.0060	2000	0.1122	7.0237

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1