openai/whisper-large-v2, all the parameters updated for 5 epochs
This model is a fine-tuned version of openai/whisper-large-v2 on the 2 hour dataset of SPGIspeech(custom dataset) 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 120
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.0
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Model tree for Jungwonchang/whisper_large-v2-Full-SPGIspeech-xs
Base model
openai/whisper-large-v2Evaluation results
- WER on Test set for spgispeechtest set self-reported6.850
- CER on Test set for spgispeechtest set self-reported2.020