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whisper_base_en – AI Model by Eyesiga | AlphaNeural AI
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whisper_base_en
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
whisper-event
generated_from_trainer
en
tericlabs
openai/whisper-base.en
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper base english
This model is a fine-tuned version of
openai/whisper-base.en
on the Sunbird dataset. It achieves the following results on the evaluation set:
Loss: 0.2710
Wer: 7.7095
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: 16
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: 1000
training_steps: 4000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.395
3.33
1000
0.1988
7.4860
0.0295
6.67
2000
0.2389
7.3743
0.0026
10.0
3000
0.2645
7.5978
0.0011
13.33
4000
0.2710
7.7095
Framework versions
Transformers 4.39.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2
