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whisper-uz – AI Model by jmshd | AlphaNeural AI
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whisper-uz
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
uz
mozilla-foundation/common_voice_17_0
DavronSherbaev/uzbekvoice
jmshd/whisper-uz
finetune
apache-2.0
endpoints_compatible
us
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Whisper base uz - Jamshid Ahmadov
This model is a fine-tuned version of Whisper Base on an Common Voice dataset. It achieves the following results on the evaluation set:
Loss: 0.1652
Wer: 14.0135
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 2000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0346
0.5714
500
0.1719
14.7950
0.0348
1.1429
1000
0.1703
14.2490
0.0327
1.7143
1500
0.1672
14.1848
0.02
2.2857
2000
0.1652
14.0135
Framework versions
Transformers 4.50.3
Pytorch 2.5.1+cu121
Datasets 3.5.0
Tokenizers 0.21.0
