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whisper-base-finetuned-500 – AI Model by shane062 | AlphaNeural AI | AlphaNeural AI
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whisper-base-finetuned-500
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
audiofolder
openai/whisper-base
finetune
apache-2.0
model-index
endpoints_compatible
us
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whisper-base-finetuned-500
This model is a fine-tuned version of
openai/whisper-base
on the audiofolder dataset. It achieves the following results on the evaluation set:
Loss: nan
Wer Ortho: 100.0
Wer: 108.1081
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: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 500
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer Ortho
Wer
0.0
33.3333
100
nan
100.0
108.1081
0.0
66.6667
200
nan
100.0
108.1081
0.0
100.0
300
nan
100.0
108.1081
0.0
133.3333
400
nan
100.0
108.1081
0.0
166.6667
500
nan
100.0
108.1081
Framework versions
Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1
