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whisper-base – AI Model by meghtedari | AlphaNeural AI
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meghtedari
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whisper-base
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transformers
pytorch
whisper
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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meghtedariatc
This model is a fine-tuned version of
openai/whisper-small
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.1820
Wer: 53.4928
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
0.0002
76.92
1000
1.1820
53.4928
0.0002
153.85
2000
1.1820
53.4928
0.0002
230.77
3000
1.1820
53.4928
0.0002
307.69
4000
1.1820
53.4928
Framework versions
Transformers 4.28.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2
