--- library_name: transformers language: - qve license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - cportoca/Quechua_dataset metrics: - wer model-index: - name: Whisper Small Ja-Qve - cportoca results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Quechua_dataset type: cportoca/Quechua_dataset args: 'config: Qve, split: train/test' metrics: - name: Wer type: wer value: 19.76780671477879 --- # Whisper Small Ja-Qve - cportoca This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Quechua_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.2232 - Wer: 19.7678 ## 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: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.2739 | 1.3550 | 1000 | 0.3388 | 31.3461 | | 0.1322 | 2.7100 | 2000 | 0.2377 | 25.1961 | | 0.0275 | 4.0650 | 3000 | 0.2216 | 20.6464 | | 0.0127 | 5.4201 | 4000 | 0.2232 | 19.7678 | ### Framework versions - Transformers 4.46.3 - Pytorch 2.4.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3