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README.md
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model-index:
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- name: wav2vec2-xls-r-300m-
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results: []
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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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# wav2vec2-xls-r-300m-
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This model is a fine-tuned version of [
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- Loss: 0.4502
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## Model description
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### Training hyperparameters
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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: 8
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- he
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- generated_from_trainer
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model-index:
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- name: wav2vec2-xls-r-300m-hebrew
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results: []
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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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# wav2vec2-xls-r-300m-hebrew
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples and it achieves the following results on the evaluation set with the dataset:
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| split |size(gb) | n_samples | duration(hrs)| |
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|---|---|---|---|---|
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|train|4.19| 20306 | 28 | |
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|dev |1.05| 5076 | 7 | |
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- Loss: 0.5438
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- Wer: 0.1773
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Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various samples from different sources, and with an unlabeled dataset that was weakly labeled using a previously trained model.
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on a small dataset from previous step achieves
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- WER: 0.1697
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on a whole dataset
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- Loss: 0.4502
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- WER: 0.2318
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## Model description
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### Training hyperparameters
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#### First training
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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: 8
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- total_eval_batch_size: 16
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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: 1000
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- num_epochs: 100.0
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- mixed_precision_training: Native AMP
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Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|
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| No log | 3.15 | 1000 | 0.5203 | 0.4333 |
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| 1.4284 | 6.31 | 2000 | 0.4816 | 0.3951 |
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| 1.4284 | 9.46 | 3000 | 0.4315 | 0.3546 |
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| 1.283 | 12.62 | 4000 | 0.4278 | 0.3404 |
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| 1.283 | 15.77 | 5000 | 0.4090 | 0.3054 |
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| 1.1777 | 18.93 | 6000 | 0.3893 | 0.3006 |
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| 1.1777 | 22.08 | 7000 | 0.3968 | 0.2857 |
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| 1.0994 | 25.24 | 8000 | 0.3892 | 0.2751 |
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| 1.0994 | 28.39 | 9000 | 0.4061 | 0.2690 |
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| 1.0323 | 31.54 | 10000 | 0.4114 | 0.2507 |
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| 1.0323 | 34.7 | 11000 | 0.4021 | 0.2508 |
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| 0.9623 | 37.85 | 12000 | 0.4032 | 0.2378 |
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| 0.9623 | 41.01 | 13000 | 0.4148 | 0.2374 |
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| 0.9077 | 44.16 | 14000 | 0.4350 | 0.2323 |
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| 0.9077 | 47.32 | 15000 | 0.4515 | 0.2246 |
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| 0.8573 | 50.47 | 16000 | 0.4474 | 0.2180 |
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| 0.8573 | 53.63 | 17000 | 0.4649 | 0.2171 |
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| 0.8083 | 56.78 | 18000 | 0.4455 | 0.2102 |
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| 0.8083 | 59.94 | 19000 | 0.4587 | 0.2092 |
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| 0.769 | 63.09 | 20000 | 0.4794 | 0.2012 |
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| 0.769 | 66.25 | 21000 | 0.4845 | 0.2007 |
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| 0.7308 | 69.4 | 22000 | 0.4937 | 0.2008 |
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| 0.7308 | 72.55 | 23000 | 0.4920 | 0.1895 |
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| 0.6927 | 75.71 | 24000 | 0.5179 | 0.1911 |
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| 0.6927 | 78.86 | 25000 | 0.5202 | 0.1877 |
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| 0.6622 | 82.02 | 26000 | 0.5266 | 0.1840 |
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| 0.6622 | 85.17 | 27000 | 0.5351 | 0.1854 |
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| 0.6315 | 88.33 | 28000 | 0.5373 | 0.1811 |
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| 0.6315 | 91.48 | 29000 | 0.5331 | 0.1792 |
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| 0.6075 | 94.64 | 30000 | 0.5390 | 0.1779 |
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| 0.6075 | 97.79 | 31000 | 0.5459 | 0.1773 |
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#### Second training
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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: 8
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