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whisper-medium-2-F – AI Model by nicolarsen | AlphaNeural AI
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whisper-medium-2-F
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
whisper-event
generated_from_trainer
common_voice_14_0
openai/whisper-medium
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper da-nst
This model is a fine-tuned version of
openai/whisper-medium
on the common_voice_14_0 dataset. It achieves the following results on the evaluation set:
Loss: 0.7234
Wer: 35.3094
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: 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: 5000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0133
4.04
1000
0.6362
48.9279
0.0025
9.04
2000
0.6635
37.4731
0.0001
14.03
3000
0.6959
34.1296
0.0001
19.03
4000
0.7166
35.1821
0.0
24.03
5000
0.7234
35.3094
Framework versions
Transformers 4.37.2
Pytorch 2.2.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1
