| | --- |
| | tags: |
| | - automatic-speech-recognition |
| | - abdusahmbzuai/arabic_speech_massive_300hrs |
| | - generated_from_trainer |
| | model-index: |
| | - name: aradia-ctc-data2vec-ft |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # aradia-ctc-data2vec-ft |
| |
|
| | This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 3.0464 |
| | - Wer: 1.0 |
| | |
| | ## 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: 0.0003 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 64 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 30.0 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:-----:|:----:|:---------------:|:---:| |
| | | No log | 0.43 | 100 | 3.3600 | 1.0 | |
| | | No log | 0.87 | 200 | 3.0887 | 1.0 | |
| | | No log | 1.3 | 300 | 3.0779 | 1.0 | |
| | | No log | 1.74 | 400 | 3.0551 | 1.0 | |
| | | 4.8553 | 2.17 | 500 | 3.0526 | 1.0 | |
| | | 4.8553 | 2.61 | 600 | 3.0560 | 1.0 | |
| | | 4.8553 | 3.04 | 700 | 3.1251 | 1.0 | |
| | | 4.8553 | 3.48 | 800 | 3.0870 | 1.0 | |
| | | 4.8553 | 3.91 | 900 | 3.0822 | 1.0 | |
| | | 3.1133 | 4.35 | 1000 | 3.0484 | 1.0 | |
| | | 3.1133 | 4.78 | 1100 | 3.0558 | 1.0 | |
| | | 3.1133 | 5.22 | 1200 | 3.1019 | 1.0 | |
| | | 3.1133 | 5.65 | 1300 | 3.0914 | 1.0 | |
| | | 3.1133 | 6.09 | 1400 | 3.0691 | 1.0 | |
| | | 3.109 | 6.52 | 1500 | 3.0589 | 1.0 | |
| | | 3.109 | 6.95 | 1600 | 3.0508 | 1.0 | |
| | | 3.109 | 7.39 | 1700 | 3.0540 | 1.0 | |
| | | 3.109 | 7.82 | 1800 | 3.0546 | 1.0 | |
| | | 3.109 | 8.26 | 1900 | 3.0524 | 1.0 | |
| | | 3.1106 | 8.69 | 2000 | 3.0569 | 1.0 | |
| | | 3.1106 | 9.13 | 2100 | 3.0622 | 1.0 | |
| | | 3.1106 | 9.56 | 2200 | 3.0518 | 1.0 | |
| | | 3.1106 | 10.0 | 2300 | 3.0749 | 1.0 | |
| | | 3.1106 | 10.43 | 2400 | 3.0698 | 1.0 | |
| | | 3.1058 | 10.87 | 2500 | 3.0665 | 1.0 | |
| | | 3.1058 | 11.3 | 2600 | 3.0555 | 1.0 | |
| | | 3.1058 | 11.74 | 2700 | 3.0589 | 1.0 | |
| | | 3.1058 | 12.17 | 2800 | 3.0611 | 1.0 | |
| | | 3.1058 | 12.61 | 2900 | 3.0561 | 1.0 | |
| | | 3.1071 | 13.04 | 3000 | 3.0480 | 1.0 | |
| | | 3.1071 | 13.48 | 3100 | 3.0492 | 1.0 | |
| | | 3.1071 | 13.91 | 3200 | 3.0574 | 1.0 | |
| | | 3.1071 | 14.35 | 3300 | 3.0538 | 1.0 | |
| | | 3.1071 | 14.78 | 3400 | 3.0505 | 1.0 | |
| | | 3.1061 | 15.22 | 3500 | 3.0600 | 1.0 | |
| | | 3.1061 | 15.65 | 3600 | 3.0596 | 1.0 | |
| | | 3.1061 | 16.09 | 3700 | 3.0623 | 1.0 | |
| | | 3.1061 | 16.52 | 3800 | 3.0800 | 1.0 | |
| | | 3.1061 | 16.95 | 3900 | 3.0583 | 1.0 | |
| | | 3.1036 | 17.39 | 4000 | 3.0534 | 1.0 | |
| | | 3.1036 | 17.82 | 4100 | 3.0563 | 1.0 | |
| | | 3.1036 | 18.26 | 4200 | 3.0481 | 1.0 | |
| | | 3.1036 | 18.69 | 4300 | 3.0477 | 1.0 | |
| | | 3.1036 | 19.13 | 4400 | 3.0505 | 1.0 | |
| | | 3.1086 | 19.56 | 4500 | 3.0485 | 1.0 | |
| | | 3.1086 | 20.0 | 4600 | 3.0481 | 1.0 | |
| | | 3.1086 | 20.43 | 4700 | 3.0615 | 1.0 | |
| | | 3.1086 | 20.87 | 4800 | 3.0658 | 1.0 | |
| | | 3.1086 | 21.3 | 4900 | 3.0505 | 1.0 | |
| | | 3.1028 | 21.74 | 5000 | 3.0492 | 1.0 | |
| | | 3.1028 | 22.17 | 5100 | 3.0485 | 1.0 | |
| | | 3.1028 | 22.61 | 5200 | 3.0483 | 1.0 | |
| | | 3.1028 | 23.04 | 5300 | 3.0479 | 1.0 | |
| | | 3.1028 | 23.48 | 5400 | 3.0509 | 1.0 | |
| | | 3.1087 | 23.91 | 5500 | 3.0530 | 1.0 | |
| | | 3.1087 | 24.35 | 5600 | 3.0486 | 1.0 | |
| | | 3.1087 | 24.78 | 5700 | 3.0514 | 1.0 | |
| | | 3.1087 | 25.22 | 5800 | 3.0505 | 1.0 | |
| | | 3.1087 | 25.65 | 5900 | 3.0508 | 1.0 | |
| | | 3.1043 | 26.09 | 6000 | 3.0501 | 1.0 | |
| | | 3.1043 | 26.52 | 6100 | 3.0467 | 1.0 | |
| | | 3.1043 | 26.95 | 6200 | 3.0466 | 1.0 | |
| | | 3.1043 | 27.39 | 6300 | 3.0465 | 1.0 | |
| | | 3.1043 | 27.82 | 6400 | 3.0465 | 1.0 | |
| | | 3.1175 | 28.26 | 6500 | 3.0466 | 1.0 | |
| | | 3.1175 | 28.69 | 6600 | 3.0466 | 1.0 | |
| | | 3.1175 | 29.13 | 6700 | 3.0465 | 1.0 | |
| | | 3.1175 | 29.56 | 6800 | 3.0465 | 1.0 | |
| | | 3.1175 | 30.0 | 6900 | 3.0464 | 1.0 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.18.0.dev0 |
| | - Pytorch 1.10.2+cu113 |
| | - Datasets 1.18.4 |
| | - Tokenizers 0.11.6 |
| | |
