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whisper-mediaspeech-cv-tr-v2 – AI Model by zeynepgulhan | AlphaNeural AI
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whisper-mediaspeech-cv-tr-v2
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transformers
pytorch
tensorboard
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-medium
finetune
apache-2.0
endpoints_compatible
us
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openai/whisper-medium
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: 0.1711
Wer: 10.1446
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-06
train_batch_size: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
training_steps: 5000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.111
0.2
1000
0.1786
11.0063
0.0961
1.16
2000
0.1719
10.5906
0.0732
2.12
3000
0.1743
10.3268
0.0742
3.08
4000
0.1715
10.2262
0.0692
4.03
5000
0.1711
10.1446
Framework versions
Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2
