Amenikhabthani/Tunisian_ASR_System

--- base_model: openai/whisper-small language: - ar license: apache-2.0 metrics: - wer tags: - generated_from_trainer model-index: - name: Tunisian Checkpoint12 results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: custom_tunisian_dataset type: dataset args: 'config: ar, split: test' metrics: - type: wer value: 54.52300785634119 name: Wer - type: cer value: 25.538666370797735 name: Cer --- # Model Card for Model ID ## Model Details ### Model Description # Model Card for Model ID ### Finetuning Whisper on Tunisian custom dataset ## Model Details ### Model Description This model is a fine-tuned version of openai/whisper-small on tunisian custom dataset more than 4h. It achieves the following results on the evaluation set: - Train Loss: 0.0109 - Evaluation Loss: 1.1608097553253174 - Wer: 54.52300785634119 - Cer: 25.538666370797735 -max_audio_length=15 for the preprocessing i used padding+VAD filter - **Developed by:** [Ameni Khabthani] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [ASR system] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model :** [whisper small] #### Training Hyperparameters per_device_train_batch_size=4 gradient_accumulation_steps=8 learning_rate= 5e-5 warmup_steps=100 max_steps=4000 gradient_checkpointing=True fp16=True save_steps=500 eval_steps=500 per_device_eval_batch_size=8 predict_with_generate=True generation_max_length=249 logging_steps=50 weight_decay=0.001 dropout=0.1 optim="adamw_bnb_8bit" seed=42 save_total_limit=5 save_steps=500, eval_steps=500, ### Model repository - **link:** [[(amenIKh/Tunisian_Checkpoint…)]