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whisper-bs-cs-train-noaug-test-noaug – AI Model by LadislavVasina1 | AlphaNeural AI
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whisper-bs-cs-train-noaug-test-noaug
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
common_voice_11_0
openai/whisper-base
finetune
apache-2.0
model-index
endpoints_compatible
us
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whisper-bs-cs-train-noaug-test-noaug
This model is a fine-tuned version of
openai/whisper-base
on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
Loss: 0.3769
Wer: 35.1144
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: 4
total_train_batch_size: 32
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
Validation Loss
Wer
0.3006
1.4440
1000
0.4410
41.9383
0.1741
2.8881
2000
0.3800
36.4792
0.0972
4.3321
3000
0.3750
35.3059
0.079
5.7762
4000
0.3769
35.1144
Framework versions
Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1
