Fine tuned version of whisper-large-v3 through transfer learning on Doctor/Patient consultations. This version in the ONNX format in fp32 precision. Stay tuned for instructions on how to run this pipeline in OnnxRuntime!

Intended uses & limitations

Medical transcription

Training and evaluation data

Na0s/Primock_med

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 Medical Whisper on the Test set.

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Esperanto/Medical-Whisper-large-kvc-fp32-onnx

Medical_Whisper_large_1.5b

Model description