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whisper-finetune – AI Model by hiiamsid | AlphaNeural AI
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whisper-finetune
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transformers
pytorch
whisper
automatic-speech-recognition
hf-asr-leaderboard
generated_from_trainer
en
openai/whisper-base
finetune
apache-2.0
endpoints_compatible
us
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Whisper Base Medical
This model is a fine-tuned version of
openai/whisper-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2595
Wer: 24.0503
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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.3836
1.0
184
0.5763
29.2094
0.2101
2.0
368
0.3948
30.2361
0.1197
3.0
552
0.3029
27.1047
0.0528
4.0
737
0.2583
24.1273
0.0261
4.99
920
0.2595
24.0503
Framework versions
Transformers 4.33.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3
