--- language: - de license: apache-2.0 tags: - sbb-asr - generated_from_trainer datasets: - marccgrau/sbbdata_allSNR metrics: - wer model-index: - name: Whisper Small German SBB all SNR - v2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: SBB Dataset 05.01.2023 type: marccgrau/sbbdata_allSNR args: 'config: German, split: train, test, val' metrics: - name: Wer type: wer value: 1.8738110336081166 --- # Whisper Small German SBB all SNR - v2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset. It achieves the following results on the evaluation set: - Loss: 0.7183 - Wer: 1.8738 ## 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-06 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.5636 | 0.71 | 100 | 2.7931 | 1.1541 | | 1.4736 | 1.42 | 200 | 0.8866 | 1.0444 | | 0.8446 | 2.13 | 300 | 0.9127 | 1.5136 | | 0.7396 | 2.84 | 400 | 0.7580 | 1.2644 | | 0.7699 | 3.55 | 500 | 0.7183 | 1.8738 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.12.1