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whisper-medium-ur-v2 – AI Model by abdullah090809 | AlphaNeural AI
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whisper-medium-ur-v2
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transformers
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
ur
fsicoli/common_voice_19_0
openai/whisper-medium
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper Medium Ur - Your Name
This model is a fine-tuned version of
openai/whisper-medium
on the Common Voice 19.0 dataset. It achieves the following results on the evaluation set:
Loss: 0.3564
Wer: 27.7201
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: 3e-06
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: 150
training_steps: 1500
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.3965
0.6557
500
0.3952
30.0288
0.3086
1.3108
1000
0.3665
27.9635
0.2877
1.9666
1500
0.3564
27.7201
Framework versions
Transformers 4.49.0
Pytorch 2.5.1+cu121
Datasets 3.4.1
Tokenizers 0.21.0
