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whisper-large-ver1 – AI Model by unanam | AlphaNeural AI
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unanam
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whisper-large-ver1
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-large-v2
finetune
apache-2.0
endpoints_compatible
us
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whisper-large-ver1
This model is a fine-tuned version of
openai/whisper-large-v2
on an unknown dataset. It achieves the following results on the evaluation set:
Cer: 10.8895
Loss: 0.4810
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: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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
Training results
Training Loss
Epoch
Step
Cer
Validation Loss
0.024
5.6
1000
11.4526
0.3606
0.0038
11.2
2000
10.7559
0.4166
0.0009
16.81
3000
10.8609
0.4669
0.0007
22.45
4000
10.8895
0.4810
Framework versions
Transformers 4.39.0.dev0
Pytorch 2.0.0+cu118
Datasets 2.18.0
Tokenizers 0.15.2
