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whisper-base-ar-restaurant – AI Model by ReejaNoor | AlphaNeural AI
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whisper-base-ar-restaurant
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transformers
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-base
finetune
apache-2.0
endpoints_compatible
us
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whisper-base-ar-restaurant
This model is a fine-tuned version of
openai/whisper-base
on the None dataset. It achieves the following results on the evaluation set:
eval_loss: 0.0233
eval_runtime: 13.6186
eval_samples_per_second: 7.343
eval_steps_per_second: 0.514
epoch: 34.4956
step: 1000
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: 2e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP
Framework versions
Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.4.2
Tokenizers 0.22.1
