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whisper-finetuned-base – AI Model by faruk786 | AlphaNeural AI
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faruk786
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whisper-finetuned-base
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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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whisper-finetuned-base
This model is a fine-tuned version of
openai/whisper-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0056
Wer: 0.0
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: 0.0001
train_batch_size: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 2000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0
45.4762
500
0.0215
0.0
0.0
90.9524
1000
0.0082
0.0
0.0
136.3810
1500
0.0059
0.0
0.0
181.8571
2000
0.0056
0.0
Framework versions
Transformers 4.53.1
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.2
