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whisper-large-swiss – AI Model by jempf | AlphaNeural AI
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whisper-large-swiss
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
endpoints_compatible
us
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whisper-large-swiss
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.5228
eval_wer: 30.8267
eval_runtime: 4076.0945
eval_samples_per_second: 0.817
eval_steps_per_second: 0.204
epoch: 0.0886
step: 500
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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
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: 4000
mixed_precision_training: Native AMP
Framework versions
Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0
