--- base_model: openai/whisper-large-v3 datasets: - fleurs language: - pl license: apache-2.0 metrics: - wer tags: - hf-asr-leaderboard - generated_from_trainer model-index: - name: Whisper Large V3 pl preprocessed - Chee Li results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Google Fleurs type: fleurs config: pl_pl split: None args: 'config: pl split: test' metrics: - type: wer value: 332.944005496393 name: Wer --- # Whisper Large V3 pl preprocessed - Chee Li This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Google Fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.1369 - Wer: 332.9440 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - 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 | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.006 | 5.0251 | 1000 | 0.1174 | 340.0275 | | 0.0002 | 10.0503 | 2000 | 0.1296 | 200.6596 | | 0.0001 | 15.0754 | 3000 | 0.1343 | 331.7760 | | 0.0001 | 20.1005 | 4000 | 0.1369 | 332.9440 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.3.1+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1