Medical-Whisper-Large-v3 API & Inference Endpoint

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:

Table with columns: | Model Name, WER, CER, Number of Parameters
\| 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

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

Table with columns: Model Name, WER, CER, Number of Parameters
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

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:

Table with columns: | Model Name, WER, CER, Number of Parameters
\| 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

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

Table with columns: Model Name, WER, CER, Number of Parameters
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

Medical-Whisper-Large-v3

README

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Performance Overview:

Framework versions

Explore FriendliAI today

README

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Performance Overview:

Framework versions