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Cohisper – AI Model by Enpas | AlphaNeural AI
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Cohisper
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-base
finetune
apache-2.0
endpoints_compatible
us
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Cohisper
This model is a fine-tuned version of
openai/whisper-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0307
Wer: 25.1281
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: 2.5e-05
train_batch_size: 16
eval_batch_size: 2
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: 3000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.3033
0.4912
1000
0.0484
37.0363
0.0438
0.9823
2000
0.0341
27.5671
0.0292
1.4735
3000
0.0307
25.1281
Framework versions
Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1
