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whisper-small-atc – AI Model by nbaker | AlphaNeural AI
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whisper-small-atc
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
generated_from_trainer
endpoints_compatible
us
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whisper-small-atc
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3749
Wer: 18.7892
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: 3200
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.1809
1.42
1000
0.3831
24.2040
0.0976
2.83
2000
0.3497
19.3049
0.0165
4.25
3000
0.3749
18.7892
Framework versions
Transformers 4.31.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.12.1.dev0
Tokenizers 0.13.3
