Whisper Medium Hi - Aditya Agrawal
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0 dataset.
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2945340432158418383223693624588738123559693482299075088767878449688292160397327779966295692450325070170031945807812908771881611572255401942922812303597144053805349165872996110766935565946816006053119311086960734516644260779498911850068592403100913453684334767056261910363295677456051671938422478104563288264146944
- total_train_batch_size: 2945340432158418383223693624588738123559693482299075088767878449688292160397327779966295692450325070170031945807812908771881611572255401942922812303597144053805349165872996110766935565946816006053119311086960734516644260779498911850068592403100913453684334767056261910363295677456051671938422478104563288264146944
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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