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whisper-medium-finetuned – AI Model by shane062 | AlphaNeural AI
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shane062
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whisper-medium-finetuned
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
audiofolder
openai/whisper-medium
finetune
apache-2.0
model-index
endpoints_compatible
us
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whisper-medium-finetuned
This model is a fine-tuned version of
openai/whisper-medium
on the audiofolder dataset. It achieves the following results on the evaluation set:
Loss: 0.6522
Wer Ortho: 59.4595
Wer: 59.4595
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: 10
training_steps: 100
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer Ortho
Wer
0.4001
16.6667
50
0.7105
67.5676
67.5676
0.0001
33.3333
100
0.6522
59.4595
59.4595
Framework versions
Transformers 4.41.1
Pytorch 2.3.0+cpu
Datasets 2.19.1
Tokenizers 0.19.1
