Medical-Whisper-Large-v3 – AI Model by jessicalzhou | AlphaNeural AI | AlphaNeural AI

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jessicalzhou/
Medical-Whisper-Large-v3

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generated_from_trainer

automatic-speech-recognition

Na0s/Primock_med

openai/whisper-large-v3

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DALL-E-2024-10-05-20-47-54-A-doctor-in-a-modern-clinical-setting-carefully-listening-to-a-patient-s

med-whisper-large-final

This model is a fine-tuned version of openai/whisper-large-v3 on the primock_data dataset.

Model description

Fine tuned version of whisper-large-v3 through transfer learning on Doctor/Patient consultations

Intended uses & limitations

Medical transcription

Training and evaluation data

Na0s/Medical_Augmented_data

Training procedure

Exhaustive transfer learning

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 6
eval_batch_size: 6
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 500
mixed_precision_training: Native AMP

Performance Overview:

\| Model Name	WER	CER	Number of Parameters
Whisper Tiny	0.46	0.27	39M
Whisper Base	0.42	0.26	74M
Whisper Small	0.39	0.26	244M
Whisper Medium	0.37	0.23	769M
Whisper Large v3	0.33	0.18	1.55B
Whisper Medical	0.19	0.10	1.55B

Performance of foundation Whispers vs Medical Whisper on the Validation set.

Model Name	WER	CER	Number of Parameters
Whisper Medical	0.24	0.13	1.55B

Table: Performance of Whisper Medical on the Test set.

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1