--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - DTU54DL/common-accent metrics: - wer - precision - recall - f1 model-index: - name: Whisper Base CA results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Accent type: DTU54DL/common-accent metrics: - name: Wer type: wer value: 0.26376410965215386 - name: Precision type: precision value: 0.8083025813102722 - name: Recall type: recall value: 0.8232867121696472 - name: F1 type: f1 value: 0.8149744272232056 --- # Whisper Base CA This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Accent dataset. It achieves the following results on the evaluation set: - Loss: 0.7230 - Wer Ortho: 30.5998 - Wer: 0.2638 - Cer: 0.1320 - Precision: 0.8083 - Recall: 0.8233 - F1: 0.8150 ## 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 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:---------:|:------:|:------:| | 0.1367 | 0.8 | 500 | 0.7230 | 30.5998 | 0.2638 | 0.1320 | 0.8083 | 0.8233 | 0.8150 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0