njoncode
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whisper-small-medical-speech-recognition-14-Sep-2023

Automatic Speech Recognition

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whisper-small-medical-speech-recognition-14-Sep-2023

This model is a fine-tuned version of openai/whisper-small.en on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2226
Wer: 7.3171

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3756	25.0	25	0.3675	26.2195
0.017	50.0	50	0.2226	7.3171

Framework versions

Transformers 4.34.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

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openai/whisper-small.en

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