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whisper-medium-111 – AI Model by hts98 | AlphaNeural AI
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whisper-medium-111
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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whisper-medium-111
This model is a fine-tuned version of
openai/whisper-medium
on the None dataset. It achieves the following results on the evaluation set:
eval_loss: 0.2345
eval_wer: 100.1832
eval_runtime: 218.2253
eval_samples_per_second: 2.145
eval_steps_per_second: 0.215
epoch: 2.2
step: 400
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: 10
eval_batch_size: 10
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: 4000
mixed_precision_training: Native AMP
Framework versions
Transformers 4.27.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.7.0
Tokenizers 0.12.1
