--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy - f1 - recall - precision model-index: - name: wav2vec2-base-finetuned-common_voice results: [] --- # wav2vec2-base-finetuned-common_voice This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 - F1: 1.0 - Recall: 1.0 - Precision: 1.0 - Mcc: 1.0 - Auc: 1.0 ## 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: 3e-05 - train_batch_size: 8 - 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_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:| | 0.3184 | 1.0 | 200 | 0.2012 | 0.97 | 0.9693 | 0.97 | 0.9721 | 0.9633 | 0.9965 | | 0.0889 | 2.0 | 400 | 0.0144 | 0.995 | 0.9950 | 0.9950 | 0.9950 | 0.9938 | 1.0000 | | 0.1011 | 3.0 | 600 | 0.0482 | 0.9925 | 0.9925 | 0.9925 | 0.9928 | 0.9907 | 0.9987 | | 0.0007 | 4.0 | 800 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0005 | 5.0 | 1000 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0003 | 6.0 | 1200 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0004 | 7.0 | 1400 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0003 | 8.0 | 1600 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0003 | 9.0 | 1800 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0002 | 10.0 | 2000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1