--- base_model: openai/whisper-base datasets: - Jpep26/AfterProcessing language: - ko library_name: transformers license: apache-2.0 metrics: - wer tags: - hf-asr-leaderboard - generated_from_trainer model-index: - name: Test results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: AfterProcessing type: Jpep26/AfterProcessing args: 'config: ko, split: valid' metrics: - type: wer value: 0.7054380664652568 name: Wer --- # Test This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the AfterProcessing dataset. It achieves the following results on the evaluation set: - Loss: 0.9167 - Cer: 0.8486 - Wer: 0.7054 - Mean: 0.7770 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | Mean | |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:| | 2.9457 | 0.6410 | 50 | 2.6739 | 0.5791 | 0.8056 | 0.6923 | | 1.7821 | 1.2821 | 100 | 1.6827 | 0.4622 | 0.6561 | 0.5592 | | 1.2153 | 1.9231 | 150 | 1.1411 | 0.4216 | 0.6022 | 0.5119 | | 0.8636 | 2.5641 | 200 | 0.9167 | 0.8486 | 0.7054 | 0.7770 | ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1