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whisper-medium-medical – AI Model by Hemgg | AlphaNeural AI | AlphaNeural AI
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Hemgg
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whisper-medium-medical
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-medium
finetune
apache-2.0
endpoints_compatible
us
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whisper-medium-medical
This model is a fine-tuned version of
openai/whisper-medium
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0562
Wer: 10.7169
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: 32
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 500
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.5008
0.5405
100
0.1965
12.0203
0.1034
1.0811
200
0.0870
12.2616
0.0563
1.6216
300
0.0642
8.3514
0.0238
2.1622
400
0.0610
11.6341
0.0129
2.7027
500
0.0562
10.7169
Framework versions
Transformers 4.49.0
Pytorch 2.6.0+cu118
Datasets 3.3.1
Tokenizers 0.21.0
