metadata

library_name: transformers
language:
  - en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - Dev372/Medical_STT_Dataset_1.1
  - OUTCOMESAI/medical_speech_corpus
  - pauleyc/radiology_audio_3_iphone_laptop_666_samples
metrics:
  - wer
model-index:
  - name: Whisper Large
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Medical STT Combined
          type: Dev372/Medical_STT_Dataset_1.1
        metrics:
          - name: Wer
            type: wer
            value: 2.732222934016656

Whisper Large

This model is a fine-tuned version of openai/whisper-large-v3 on the Medical STT Combined dataset. It achieves the following results on the evaluation set:

Loss: 0.0969
Wer Ortho: 4.8761
Wer: 2.7322

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: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.0787	1.1364	500	0.0969	4.8761	2.7322

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3