metadata

library_name: transformers
language:
  - en
license: apache-2.0
base_model: openai/whisper-base
tags:
  - generated_from_trainer
datasets:
  - Hani89/medical_asr_recording_dataset
metrics:
  - wer
model-index:
  - name: Whisper Base - Shantanu
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: 'medical-speech-transcription-and-intent '
          type: Hani89/medical_asr_recording_dataset
          args: 'config: en, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 5.945355191256831

Whisper Base - Shantanu

This model is a fine-tuned version of openai/whisper-base on the medical-speech-transcription-and-intent dataset. It achieves the following results on the evaluation set:

Loss: 0.1194
Wer: 5.9454

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0544	3.0030	1000	0.1275	7.1403
0.007	6.0060	2000	0.1147	6.4044
0.0007	9.0090	3000	0.1183	5.9381
0.0004	12.0120	4000	0.1194	5.9454

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3