readme: ---
  language:
  - he
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - he
  - hf-asr-leaderboard
  - robust-speech-event
  model-index:
  - name: wav2vec2-xls-r-300m-hebrew
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Custom Dataset
        type: custom
        args: he
      metrics:
      - name: Test WER
        type: wer
        value: 23.18
  ---
  
  <!-- 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. -->
  
  # wav2vec2-xls-r-300m-hebrew
  
  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 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.
  
  Small dataset:
  
  | split  |size(gb) | n_samples | duration(hrs)|   |
  |---|---|---|---|---|
  |train|4.19| 20306  | 28  |   |
  |dev  |1.05|  5076 |  7 |   |
  
  Large dataset: 
  
  | split  |size(gb) | n_samples | duration(hrs)|   |
  |---|---|---|---|---|
  |train|12.3| 90777  | 69  |   |
  |dev  |2.39|  20246 |  14* |   |
  (*weakly labeled data wasn't used in validation set)
  
  After firts training it achieves:
  
  on small dataset
  - Loss: 0.5438
  - WER: 0.1773
  
  on large dataset
  - WER: 0.3811
  
  after second training:
  on small dataset
  - WER: 0.1697
  
  on large dataset
  - Loss: 0.4502
  - WER: 0.2318
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  
  #### First training
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | No log        | 3.15  | 1000  | 0.5203          | 0.4333 |
  | 1.4284        | 6.31  | 2000  | 0.4816          | 0.3951 |
  | 1.4284        | 9.46  | 3000  | 0.4315          | 0.3546 |
  | 1.283         | 12.62 | 4000  | 0.4278          | 0.3404 |
  | 1.283         | 15.77 | 5000  | 0.4090          | 0.3054 |
  | 1.1777        | 18.93 | 6000  | 0.3893          | 0.3006 |
  | 1.1777        | 22.08 | 7000  | 0.3968          | 0.2857 |
  | 1.0994        | 25.24 | 8000  | 0.3892          | 0.2751 |
  | 1.0994        | 28.39 | 9000  | 0.4061          | 0.2690 |
  | 1.0323        | 31.54 | 10000 | 0.4114          | 0.2507 |
  | 1.0323        | 34.7  | 11000 | 0.4021          | 0.2508 |
  | 0.9623        | 37.85 | 12000 | 0.4032          | 0.2378 |
  | 0.9623        | 41.01 | 13000 | 0.4148          | 0.2374 |
  | 0.9077        | 44.16 | 14000 | 0.4350          | 0.2323 |
  | 0.9077        | 47.32 | 15000 | 0.4515          | 0.2246 |
  | 0.8573        | 50.47 | 16000 | 0.4474          | 0.2180 |
  | 0.8573        | 53.63 | 17000 | 0.4649          | 0.2171 |
  | 0.8083        | 56.78 | 18000 | 0.4455          | 0.2102 |
  | 0.8083        | 59.94 | 19000 | 0.4587          | 0.2092 |
  | 0.769         | 63.09 | 20000 | 0.4794          | 0.2012 |
  | 0.769         | 66.25 | 21000 | 0.4845          | 0.2007 |
  | 0.7308        | 69.4  | 22000 | 0.4937          | 0.2008 |
  | 0.7308        | 72.55 | 23000 | 0.4920          | 0.1895 |
  | 0.6927        | 75.71 | 24000 | 0.5179          | 0.1911 |
  | 0.6927        | 78.86 | 25000 | 0.5202          | 0.1877 |
  | 0.6622        | 82.02 | 26000 | 0.5266          | 0.1840 |
  | 0.6622        | 85.17 | 27000 | 0.5351          | 0.1854 |
  | 0.6315        | 88.33 | 28000 | 0.5373          | 0.1811 |
  | 0.6315        | 91.48 | 29000 | 0.5331          | 0.1792 |
  | 0.6075        | 94.64 | 30000 | 0.5390          | 0.1779 |
  | 0.6075        | 97.79 | 31000 | 0.5459          | 0.1773 |
  
  #### Second training
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 60.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:---