--- license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - kul-speech-lab/CGN metrics: - wer model-index: - name: Whisper Small CGN results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: kul-speech-lab/CGN type: kul-speech-lab/CGN config: null split: test metrics: - name: Wer type: wer value: 15.197170132057957 --- # Whisper Small CGN This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the kul-speech-lab/CGN dataset. It achieves the following results on the evaluation set: - Loss: 0.3386 - Wer: 15.1972 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 15000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1967 | 1.01 | 1000 | 0.4085 | 21.8459 | | 0.1355 | 2.03 | 2000 | 0.3752 | 18.6212 | | 0.2952 | 3.04 | 3000 | 0.3535 | 18.5841 | | 0.1876 | 4.05 | 4000 | 0.3464 | 17.5097 | | 0.1037 | 6.01 | 5000 | 0.3396 | 16.7360 | | 0.0473 | 7.02 | 6000 | 0.3526 | 16.4131 | | 0.1605 | 8.04 | 7000 | 0.3284 | 16.4012 | | 0.0537 | 9.05 | 8000 | 0.3386 | 15.9454 | | 0.0928 | 11.01 | 9000 | 0.3315 | 15.9568 | | 0.0144 | 12.02 | 10000 | 0.3532 | 15.5387 | | 0.0267 | 13.04 | 11000 | 0.3261 | 15.7577 | | 0.0936 | 14.05 | 12000 | 0.3155 | 15.3380 | | 0.0825 | 16.01 | 13000 | 0.3198 | 15.2653 | | 0.0498 | 17.02 | 14000 | 0.3386 | 15.1972 | | 0.0338 | 18.03 | 15000 | 0.3413 | 15.1972 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2 Whisper small model finetuned on Flemish part of Corpus Gesproken Nederlands (CGN).