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whisper-medium-test – AI Model by hts98 | AlphaNeural AI
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whisper-medium-test
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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whisper-medium-test
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: 0.3629
Wer: 99.9968
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: 12
eval_batch_size: 12
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 1400
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.2455
1.14
400
0.3382
99.9925
0.1461
2.27
800
0.3414
99.9925
0.0878
3.41
1200
0.3629
99.9968
Framework versions
Transformers 4.27.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.7.0
Tokenizers 0.12.1
