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whisper-medium-ur-v2-resumed – AI Model by abdullah090809 | AlphaNeural AI
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whisper-medium-ur-v2-resumed
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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
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:
eval_loss: 0.3571
eval_wer: 25.1658
eval_runtime: 4297.3715
eval_samples_per_second: 1.167
eval_steps_per_second: 0.146
epoch: 1.3108
step: 1000
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: 100
training_steps: 1000
mixed_precision_training: Native AMP
Framework versions
Transformers 4.49.0
Pytorch 2.5.1+cu121
Datasets 3.4.1
Tokenizers 0.21.0
