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whisper-base-cs – AI Model by LadislavVasina1 | AlphaNeural AI
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whisper-base-cs
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-base
finetune
apache-2.0
endpoints_compatible
us
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whisper-base-cs
This model is a fine-tuned version of
openai/whisper-base
on the CommonVoice11 dataset. It achieves the following results on the evaluation set:
Loss: 0.3785
Wer: 35.4791
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
BASE-CS baseline performance: {'eval_loss': 2.514087438583374, 'eval_wer': 82.45662504144104, 'eval_runtime': 2620.0407, 'eval_samples_per_second': 2.944, 'eval_steps_per_second': 0.368}
Training Loss
Epoch
Step
Validation Loss
Wer
------
0.00
0000
2.5141
82.4566
0.2991
1.44
1000
0.4414
42.2993
0.1776
2.89
2000
0.3818
36.7573
0.0916
4.33
3000
0.3774
35.6080
0.076
5.78
4000
0.3785
35.4791
Framework versions
Transformers 4.36.0
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0
