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whisper-memory-efficient – AI Model by iRaduS | AlphaNeural AI
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whisper-memory-efficient
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
ro
custom
openai/whisper-large-v3
finetune
apache-2.0
endpoints_compatible
us
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Whisper Large v3 RO - finetune
This model is a fine-tuned version of
openai/whisper-large-v3
on the custom dataset.
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 256
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: 10
num_epochs: 8
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.48.0
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.2
