metadata

language:
  - en
license: apache-2.0
base_model: openai/whisper-base.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: 8.218579234972678

English Whisper Model

This model is a fine-tuned version of openai/whisper-base.en on the Medical dataset. It achieves the following results on the evaluation set:

Loss: 0.1241
Wer: 8.2186

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: 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: 3000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0573	3.0030	1000	0.1333	8.6703
0.0073	6.0060	2000	0.1185	8.2914
0.0009	9.0090	3000	0.1241	8.2186

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1