--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: whisper-small-ar2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_17_0 ar type: mozilla-foundation/common_voice_17_0 config: ar split: None args: ar metrics: - name: Wer type: wer value: 0.763668430335097 --- # whisper-small-ar2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_17_0 ar dataset. It achieves the following results on the evaluation set: - Loss: 1.2796 - Wer: 0.7637 ## 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: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 32 - total_eval_batch_size: 32 - 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: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:------:| | 0.3226 | 20.8333 | 250 | 1.0813 | 0.7584 | | 0.0021 | 41.6667 | 500 | 1.2796 | 0.7637 | ### Framework versions - Transformers 4.48.0.dev0 - Pytorch 2.4.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0