--- language: - ar license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - UBC-NLP/Casablanca - google/fleurs - ymoslem/MediaSpeech - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: Whisper Base ar results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: UBC-NLP/Casablanca config: ar split: test args: ar metrics: - name: Wer type: wer value: 60.7676585850631 --- # Whisper Base ar This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 2.6739 - Wer: 60.7677 ## 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: 64 - 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: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6127 | 0.2 | 1000 | 2.1392 | 51.3435 | | 0.3365 | 0.4 | 2000 | 2.4225 | 48.5384 | | 0.2029 | 0.6 | 3000 | 2.5954 | 50.8035 | | 0.1403 | 0.8 | 4000 | 2.7056 | 53.3236 | | 0.1053 | 1.0 | 5000 | 2.6739 | 60.7677 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1 ## Citation ```bibtex @misc{deepdml/whisper-base-ar-mix-norm, title={Fine-tuned Whisper base ASR model for speech recognition in Arabic}, author={Jimenez, David}, howpublished={\url{https://huggingface.co/deepdml/whisper-base-ar-mix-norm}}, year={2025} } ```