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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>

MedicalWhisperlarge_1.5b

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. 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
• —trainbatchsize: 6
• —evalbatchsize: 6
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 24
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: constantwithwarmup
• —lrschedulerwarmup_steps: 50
• —training_steps: 500
• —mixedprecisiontraining: Native AMP

Performance Overview:

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

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