--- language: - eu license: apache-2.0 base_model: openai/whisper-base tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Base Basque results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_13_0 eu type: mozilla-foundation/common_voice_13_0 config: eu split: test args: eu metrics: - name: Wer type: wer value: 25.977155818380655 --- # Whisper Base Basque This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set: - Loss: 0.5520 - Wer: 25.9772 ## 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: 2.5e-05 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0174 | 9.01 | 1000 | 0.4597 | 27.3097 | | 0.0016 | 19.01 | 2000 | 0.5160 | 26.0197 | | 0.0007 | 29.0 | 3000 | 0.5520 | 25.9772 | | 0.0005 | 38.02 | 4000 | 0.5728 | 26.1452 | | 0.0004 | 48.01 | 5000 | 0.5818 | 26.2202 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3