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abdusah
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aradia-ctc-distilhubert-ft

Automatic Speech Recognition
Transformers
PyTorch
hubert
abdusahmbzuai/arabic_speech_massive_sm
Generated from Trainer
Inference Endpoints
Model card Files Community
Abdulwahab Sahyoun commited on
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update model card README.md

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: aradia-ctc-distilhubert-ft
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # aradia-ctc-distilhubert-ft
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+
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+ This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.7114
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+ - Wer: 0.8908
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 64
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 30.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | No log | 0.43 | 100 | 4.4129 | 1.0 |
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+ | No log | 0.87 | 200 | 3.5927 | 1.0 |
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+ | No log | 1.3 | 300 | 3.3780 | 1.0 |
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+ | No log | 1.74 | 400 | 3.0830 | 1.0 |
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+ | 5.3551 | 2.17 | 500 | 2.6278 | 0.9999 |
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+ | 5.3551 | 2.61 | 600 | 1.8359 | 1.0000 |
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+ | 5.3551 | 3.04 | 700 | 1.7878 | 0.9914 |
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+ | 5.3551 | 3.48 | 800 | 1.5219 | 0.9875 |
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+ | 5.3551 | 3.91 | 900 | 1.4348 | 0.9879 |
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+ | 1.7199 | 4.35 | 1000 | 1.4354 | 0.9644 |
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+ | 1.7199 | 4.78 | 1100 | 1.5210 | 0.9519 |
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+ | 1.7199 | 5.22 | 1200 | 1.3607 | 0.9475 |
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+ | 1.7199 | 5.65 | 1300 | 1.3839 | 0.9343 |
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+ | 1.7199 | 6.09 | 1400 | 1.2806 | 0.8944 |
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+ | 1.2342 | 6.52 | 1500 | 1.3036 | 0.9011 |
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+ | 1.2342 | 6.95 | 1600 | 1.3704 | 0.9072 |
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+ | 1.2342 | 7.39 | 1700 | 1.2981 | 0.8891 |
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+ | 1.2342 | 7.82 | 1800 | 1.2786 | 0.8733 |
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+ | 1.2342 | 8.26 | 1900 | 1.2897 | 0.8867 |
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+ | 0.9831 | 8.69 | 2000 | 1.4436 | 0.8780 |
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+ | 0.9831 | 9.13 | 2100 | 1.3680 | 0.8873 |
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+ | 0.9831 | 9.56 | 2200 | 1.3471 | 0.8692 |
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+ | 0.9831 | 10.0 | 2300 | 1.3725 | 0.8729 |
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+ | 0.9831 | 10.43 | 2400 | 1.4439 | 0.8771 |
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+ | 0.8071 | 10.87 | 2500 | 1.5114 | 0.8928 |
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+ | 0.8071 | 11.3 | 2600 | 1.6156 | 0.8958 |
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+ | 0.8071 | 11.74 | 2700 | 1.4381 | 0.8749 |
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+ | 0.8071 | 12.17 | 2800 | 1.5088 | 0.8717 |
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+ | 0.8071 | 12.61 | 2900 | 1.5486 | 0.8813 |
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+ | 0.6321 | 13.04 | 3000 | 1.4536 | 0.8884 |
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+ | 0.6321 | 13.48 | 3100 | 1.4679 | 0.8947 |
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+ | 0.6321 | 13.91 | 3200 | 1.5628 | 0.9117 |
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+ | 0.6321 | 14.35 | 3300 | 1.5831 | 0.8716 |
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+ | 0.6321 | 14.78 | 3400 | 1.6733 | 0.8702 |
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+ | 0.4998 | 15.22 | 3500 | 1.8225 | 0.8665 |
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+ | 0.4998 | 15.65 | 3600 | 1.8558 | 0.8732 |
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+ | 0.4998 | 16.09 | 3700 | 1.7513 | 0.8766 |
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+ | 0.4998 | 16.52 | 3800 | 1.8562 | 0.8753 |
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+ | 0.4998 | 16.95 | 3900 | 1.9018 | 0.8704 |
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+ | 0.4421 | 17.39 | 4000 | 1.9341 | 0.8789 |
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+ | 0.4421 | 17.82 | 4100 | 1.9582 | 0.8781 |
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+ | 0.4421 | 18.26 | 4200 | 1.8863 | 0.8821 |
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+ | 0.4421 | 18.69 | 4300 | 1.9366 | 0.8847 |
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+ | 0.4421 | 19.13 | 4400 | 2.1902 | 0.8721 |
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+ | 0.3712 | 19.56 | 4500 | 2.1641 | 0.8670 |
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+ | 0.3712 | 20.0 | 4600 | 2.1639 | 0.8776 |
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+ | 0.3712 | 20.43 | 4700 | 2.2695 | 0.9030 |
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+ | 0.3712 | 20.87 | 4800 | 2.1909 | 0.8937 |
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+ | 0.3712 | 21.3 | 4900 | 2.1606 | 0.8959 |
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+ | 0.3067 | 21.74 | 5000 | 2.1756 | 0.8943 |
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+ | 0.3067 | 22.17 | 5100 | 2.4092 | 0.8773 |
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+ | 0.3067 | 22.61 | 5200 | 2.4991 | 0.8721 |
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+ | 0.3067 | 23.04 | 5300 | 2.3340 | 0.8910 |
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+ | 0.3067 | 23.48 | 5400 | 2.3567 | 0.8946 |
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+ | 0.2764 | 23.91 | 5500 | 2.3215 | 0.8897 |
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+ | 0.2764 | 24.35 | 5600 | 2.4824 | 0.9002 |
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+ | 0.2764 | 24.78 | 5700 | 2.4585 | 0.8963 |
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+ | 0.2764 | 25.22 | 5800 | 2.5804 | 0.8879 |
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+ | 0.2764 | 25.65 | 5900 | 2.5814 | 0.8903 |
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+ | 0.2593 | 26.09 | 6000 | 2.5374 | 0.8868 |
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+ | 0.2593 | 26.52 | 6100 | 2.5346 | 0.8922 |
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+ | 0.2593 | 26.95 | 6200 | 2.5465 | 0.8873 |
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+ | 0.2593 | 27.39 | 6300 | 2.6002 | 0.8919 |
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+ | 0.2593 | 27.82 | 6400 | 2.6102 | 0.8928 |
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+ | 0.227 | 28.26 | 6500 | 2.6925 | 0.8914 |
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+ | 0.227 | 28.69 | 6600 | 2.6981 | 0.8913 |
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+ | 0.227 | 29.13 | 6700 | 2.6872 | 0.8891 |
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+ | 0.227 | 29.56 | 6800 | 2.7015 | 0.8897 |
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+ | 0.227 | 30.0 | 6900 | 2.7114 | 0.8908 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.18.0.dev0
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+ - Pytorch 1.10.2+cu113
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+ - Datasets 1.18.4
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+ - Tokenizers 0.11.6