--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-large-v2-Ko results: [] --- # whisper-large-v2-Ko This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0617 - Wer: 0.0290 ## 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: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 7 - total_train_batch_size: 224 - total_eval_batch_size: 112 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0299 | 10.0 | 1000 | 0.0745 | 0.0447 | | 0.0085 | 20.0 | 2000 | 0.0608 | 0.0353 | | 0.0036 | 30.0 | 3000 | 0.0593 | 0.0302 | | 0.0013 | 40.0 | 4000 | 0.0609 | 0.0282 | | 0.0008 | 50.0 | 5000 | 0.0617 | 0.0290 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.10.1 - Tokenizers 0.13.2