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whisper-fine-banking-dataset – AI Model by SharatChandra | AlphaNeural AI
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SharatChandra
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whisper-fine-banking-dataset
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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-fine-banking-dataset
This model is a fine-tuned version of
openai/whisper-medium
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.6014
Wer: 96.7495
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: 500
training_steps: 4000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0001
32.26
1000
0.5225
96.6399
0.0
64.52
2000
0.5617
96.7495
0.0
96.77
3000
0.5931
96.7495
0.0
129.03
4000
0.6014
96.7495
Framework versions
Transformers 4.36.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0
