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whisper-uz-medium – AI Model by AbdulxoliqMirzaev | AlphaNeural AI
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whisper-uz-medium
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
uz
openai/whisper-medium
finetune
apache-2.0
endpoints_compatible
us
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Whisper Medium UZ - AbdulxoliqMirzaev
This model is a fine-tuned version of
openai/whisper-medium
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0640
Wer: 6.8956
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: Use OptimizerNames.ADAMW_TORCH_FUSED 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
0.2112
0.16
500
0.2028
19.4923
0.1119
0.32
1000
0.1140
11.7691
0.0792
0.48
1500
0.0757
7.8866
0.0689
0.64
2000
0.0640
6.8956
Framework versions
Transformers 4.57.1
Pytorch 2.9.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1
