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whisper-medium-gronings – AI Model by lucdekeijzer | AlphaNeural AI | AlphaNeural AI
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whisper-medium-gronings
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-medium
finetune
apache-2.0
endpoints_compatible
us
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whisper-medium-gronings
This model is a fine-tuned version of
openai/whisper-medium
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.5036
Wer Ortho: 19.3896
Wer: 19.0964
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: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 2000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer Ortho
Wer
0.0289
4.6948
500
0.4230
18.2584
18.0982
0.0122
9.3897
1000
0.4559
17.6796
17.4678
0.0076
14.0845
1500
0.4976
18.3899
18.1770
0.0097
18.7793
2000
0.5036
19.3896
19.0964
Framework versions
Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3
