--- library_name: transformers language: - ar license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper base AR - BH results: [] --- # Whisper base AR - BH This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset. It achieves the following results on the evaluation set: - Loss: 0.0120 - Wer: 11.5780 - Cer: 3.7937 ## 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - training_steps: 18600 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:------:|:-----:|:-------:|:---------------:|:-------:| | 0.0292 | 0.0704 | 200 | 10.4216 | 0.0261 | 33.1910 | | 0.0197 | 0.1408 | 400 | 8.7672 | 0.0199 | 25.3217 | | 0.0169 | 0.2112 | 600 | 8.0395 | 0.0169 | 22.2440 | | 0.0144 | 0.2817 | 800 | 6.0399 | 0.0161 | 18.5408 | | 0.011 | 0.3521 | 1000 | 5.5138 | 0.0148 | 17.3477 | | 0.0092 | 0.4225 | 1200 | 5.0008 | 0.0136 | 16.7994 | | 0.0087 | 0.4929 | 1400 | 4.7966 | 0.0134 | 14.8045 | | 0.0078 | 0.5633 | 1600 | 4.5948 | 0.0128 | 14.1680 | | 0.0056 | 0.6337 | 1800 | 4.7204 | 0.0112 | 13.6886 | | 0.0059 | 0.7042 | 2000 | 4.2984 | 0.0117 | 13.2643 | | 0.0052 | 0.7746 | 2200 | 4.0641 | 0.0111 | 12.5892 | | 0.0041 | 0.8450 | 2400 | 3.8254 | 0.0109 | 11.9527 | | 0.0043 | 0.9154 | 2600 | 3.8392 | 0.0105 | 11.8177 | | 0.004 | 0.9858 | 2800 | 3.7978 | 0.0105 | 11.7406 | | 0.0039 | 1.0563 | 3000 | 3.9949 | 0.0114 | 12.3219 | | 0.0038 | 1.1267 | 3200 | 3.9334 | 0.0111 | 11.8535 | | 0.0031 | 1.1972 | 3400 | 3.9939 | 0.0111 | 11.9803 | | 0.0034 | 1.2676 | 3600 | 3.9068 | 0.0109 | 12.0960 | | 0.0027 | 1.3380 | 3800 | 3.8959 | 0.0110 | 12.0519 | | 0.0035 | 1.4084 | 4000 | 3.9113 | 0.0109 | 12.0464 | | 0.0033 | 1.4788 | 4200 | 3.8424 | 0.0116 | 11.8287 | | 0.003 | 1.5492 | 4400 | 4.0442 | 0.0115 | 12.4432 | | 0.0033 | 1.6197 | 4600 | 3.8276 | 0.0113 | 11.9197 | | 0.0028 | 1.6901 | 4800 | 3.8978 | 0.0113 | 11.9445 | | 0.003 | 1.7605 | 5000 | 3.8286 | 0.0114 | 11.8590 | | 0.0029 | 1.8309 | 5200 | 3.8360 | 0.0112 | 11.8838 | | 0.0025 | 1.9013 | 5400 | 3.7738 | 0.0112 | 11.7902 | | 0.0026 | 1.9717 | 5600 | 3.7863 | 0.0112 | 11.7406 | | 0.0027 | 2.0422 | 5800 | 3.6681 | 0.0115 | 11.6689 | | 0.0021 | 2.1127 | 6000 | 3.8388 | 0.0118 | 11.7543 | | 0.0018 | 2.1831 | 6200 | 3.8116 | 0.0114 | 11.5918 | | 0.0021 | 2.2535 | 6400 | 3.7619 | 0.0114 | 11.5835 | | 0.0024 | 2.3239 | 6600 | 3.7517 | 0.0113 | 11.5532 | | 0.0022 | 2.3943 | 6800 | 3.7296 | 0.0114 | 11.5091 | | 0.0017 | 2.4647 | 7000 | 3.7357 | 0.0114 | 11.5670 | | 0.0023 | 2.5352 | 7200 | 3.7283 | 0.0114 | 11.5477 | | 0.0022 | 2.6056 | 7400 | 3.8404 | 0.0114 | 11.9059 | | 0.0025 | 2.6760 | 7600 | 3.8555 | 0.0114 | 11.8205 | | 0.0023 | 2.7464 | 7800 | 3.7853 | 0.0113 | 11.6496 | | 0.0018 | 2.8168 | 8000 | 3.7498 | 0.0114 | 11.6303 | | 0.0017 | 2.8872 | 8200 | 3.7559 | 0.0114 | 11.6193 | | 0.0021 | 2.9577 | 8400 | 3.7693 | 0.0114 | 11.5642 | | 0.0026 | 3.0282 | 8600 | 3.7658 | 0.0116 | 11.5091 | | 0.0022 | 3.0986 | 8800 | 3.8430 | 0.0117 | 11.6028 | | 0.0012 | 3.1690 | 9000 | 3.7030 | 0.0114 | 11.5642 | | 0.0019 | 3.2394 | 9200 | 3.8065 | 0.0116 | 11.6882 | | 0.0017 | 3.3098 | 9400 | 3.6713 | 0.0114 | 11.4402 | | 0.0013 | 3.3802 | 9600 | 3.7238 | 0.0115 | 11.5504 | | 0.0014 | 3.4507 | 9800 | 3.7078 | 0.0115 | 11.4623 | | 0.0018 | 3.5211 | 10000 | 3.7427 | 0.0115 | 11.5091 | | 0.0018 | 3.5915 | 10200 | 3.8664 | 0.0117 | 11.7902 | | 0.0016 | 3.6619 | 10400 | 3.7568 | 0.0116 | 11.6303 | | 0.0016 | 3.7323 | 10600 | 3.7693 | 0.0116 | 11.6111 | | 0.0014 | 3.8027 | 10800 | 3.7520 | 0.0116 | 11.4402 | | 0.0015 | 3.8732 | 11000 | 3.7587 | 0.0116 | 11.4678 | | 0.0017 | 3.9436 | 11200 | 3.8017 | 0.0116 | 11.5587 | | 0.002 | 4.0141 | 11400 | 3.7575 | 0.0116 | 11.4843 | | 0.0014 | 4.0845 | 11600 | 3.7808 | 0.0117 | 11.5670 | | 0.0015 | 4.1549 | 11800 | 3.7123 | 0.0117 | 11.5064 | | 0.0014 | 4.2253 | 12000 | 3.7456 | 0.0117 | 11.5697 | | 0.0014 | 4.2957 | 12200 | 3.7924 | 0.0117 | 11.6607 | | 0.0017 | 4.3662 | 12400 | 3.7933 | 0.0117 | 11.6579 | | 0.0013 | 4.4366 | 12600 | 3.8084 | 0.0117 | 11.6717 | | 0.0021 | 4.5070 | 12800 | 3.9065 | 0.0118 | 11.9086 | | 0.0014 | 4.5774 | 13000 | 3.9045 | 0.0118 | 11.7846 | | 0.0015 | 4.6478 | 13200 | 3.7975 | 0.0119 | 11.6469 | | 0.0012 | 4.7182 | 13400 | 3.7414 | 0.0118 | 11.5697 | | 0.0014 | 4.7887 | 13600 | 3.7917 | 0.0118 | 11.5311 | | 0.0016 | 4.8591 | 13800 | 3.7719 | 0.0118 | 11.4953 | | 0.0015 | 4.9295 | 14000 | 3.7857 | 0.0118 | 11.5201 | | 0.0015 | 4.9999 | 14200 | 3.7834 | 0.0118 | 11.5119 | | 0.0014 | 5.0704 | 14400 | 3.7517 | 0.0119 | 11.4512 | | 0.0009 | 5.1408 | 14600 | 3.6793 | 0.0118 | 11.4154 | | 0.0012 | 5.2112 | 14800 | 3.7369 | 0.0118 | 11.3989 | | 0.0015 | 5.2817 | 15000 | 3.7087 | 0.0119 | 11.4402 | | 0.0015 | 5.3521 | 15200 | 3.7049 | 0.0119 | 11.4843 | | 0.001 | 5.4225 | 15400 | 3.7187 | 0.0119 | 11.4788 | | 0.0013 | 5.4929 | 15600 | 3.7225 | 0.0119 | 11.4650 | | 0.0015 | 5.5633 | 15800 | 3.7289 | 0.0119 | 11.4926 | | 0.0014 | 5.6337 | 16000 | 3.7277 | 0.0120 | 11.4650 | | 0.0015 | 5.7042 | 16200 | 3.7491 | 0.0119 | 11.5449 | | 0.0013 | 5.7746 | 16400 | 3.7616 | 0.0119 | 11.5229 | | 0.0013 | 5.8450 | 16600 | 3.7430 | 0.0119 | 11.5256 | | 0.0014 | 5.9154 | 16800 | 3.7591 | 0.0119 | 11.5064 | | 0.0011 | 5.9858 | 17000 | 3.7655 | 0.0119 | 11.5201 | | 0.0014 | 6.0563 | 17200 | 0.0119 | 11.6441 | 3.8190 | | 0.0014 | 6.1267 | 17400 | 0.0119 | 11.5587 | 3.7376 | | 0.0013 | 6.1972 | 17600 | 0.0119 | 11.6111 | 3.8094 | | 0.0011 | 6.2676 | 17800 | 0.0120 | 11.6827 | 3.8142 | | 0.0014 | 6.3380 | 18000 | 0.0120 | 11.5339 | 3.7004 | | 0.0014 | 6.4084 | 18200 | 0.0120 | 11.5449 | 3.7430 | | 0.0012 | 6.4788 | 18400 | 0.0120 | 11.5670 | 3.7635 | | 0.0012 | 6.5492 | 18600 | 0.0120 | 11.5780 | 3.7937 | ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0