--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: wav2vec2-base-mk results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: fleurs type: fleurs config: mk_mk split: test args: mk_mk metrics: - name: Wer type: wer value: 0.14327357528057136 --- # wav2vec2-base-mk This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.1589 - Wer: 0.1433 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.609 | 2.33 | 400 | 0.3751 | 0.4184 | | 0.232 | 4.65 | 800 | 0.1694 | 0.1960 | | 0.0773 | 6.98 | 1200 | 0.1630 | 0.1598 | | 0.0407 | 9.3 | 1600 | 0.1589 | 0.1433 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0