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whisper_medium_zh – AI Model by zzjo | AlphaNeural AI
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whisper_medium_zh
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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_zh
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: 1.9767
Wer: 96.0
Cer: 12.6058
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: 8
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: 40
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
Cer
No log
0.48
10
2.7665
98.0
13.0762
No log
0.95
20
2.6914
98.0
13.0762
2.4583
1.43
30
2.2921
98.0
12.8881
2.4583
1.9
40
1.9767
96.0
12.6058
Framework versions
Transformers 4.30.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.12.0
Tokenizers 0.12.1