readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-xls-r-300m-arabic_speech_commands_10s
    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. -->
  
  # wav2vec2-xls-r-300m-arabic_speech_commands_10s
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2029
  - Accuracy: 0.9625
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 80
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.6904        | 1.0   | 25   | 3.6889          | 0.0292   |
  | 3.6892        | 2.0   | 50   | 3.6876          | 0.025    |
  | 3.6902        | 3.0   | 75   | 3.6707          | 0.0792   |
  | 3.6133        | 4.0   | 100  | 3.5175          | 0.0917   |
  | 3.4989        | 5.0   | 125  | 3.3043          | 0.0917   |
  | 3.1971        | 6.0   | 150  | 3.1385          | 0.125    |
  | 3.1348        | 7.0   | 175  | 2.8781          | 0.2      |
  | 2.8271        | 8.0   | 200  | 2.6197          | 0.2417   |
  | 2.7187        | 9.0   | 225  | 2.4002          | 0.2417   |
  | 2.3602        | 10.0  | 250  | 2.1351          | 0.4167   |
  | 2.1853        | 11.0  | 275  | 1.9951          | 0.4042   |
  | 2.0971        | 12.0  | 300  | 2.0668          | 0.3667   |
  | 1.8549        | 13.0  | 325  | 1.7583          | 0.4792   |
  | 1.6929        | 14.0  | 350  | 1.5585          | 0.5542   |
  | 1.4449        | 15.0  | 375  | 1.5602          | 0.5292   |
  | 1.5174        | 16.0  | 400  | 1.4584          | 0.6      |
  | 1.4283        | 17.0  | 425  | 1.3407          | 0.65     |
  | 1.2341        | 18.0  | 450  | 1.1690          | 0.6708   |
  | 1.2353        | 19.0  | 475  | 1.0808          | 0.7542   |
  | 1.0083        | 20.0  | 500  | 0.8947          | 0.7917   |
  | 0.8907        | 21.0  | 525  | 0.9383          | 0.7958   |
  | 0.9161        | 22.0  | 550  | 0.7948          | 0.7917   |
  | 0.781         | 23.0  | 575  | 0.7135          | 0.8375   |
  | 0.7709        | 24.0  | 600  | 0.8259          | 0.8208   |
  | 0.5534        | 25.0  | 625  | 0.6845          | 0.7958   |
  | 0.6089        | 26.0  | 650  | 0.8161          | 0.7875   |
  | 0.5412        | 27.0  | 675  | 0.5962          | 0.8292   |
  | 0.4843        | 28.0  | 700  | 0.5888          | 0.8792   |
  | 0.5755        | 29.0  | 725  | 0.5389          | 0.8417   |
  | 0.4687        | 30.0  | 750  | 0.5176          | 0.8917   |
  | 0.4191        | 31.0  | 775  | 0.4904          | 0.8542   |
  | 0.4361        | 32.0  | 800  | 0.5360          | 0.8875   |
  | 0.264         | 33.0  | 825  | 0.4501          | 0.8958   |
  | 0.3062        | 34.0  | 850  | 0.5384          | 0.8917   |
  | 0.2992        | 35.0  | 875  | 0.4840          | 0.9167   |
  | 0.3904        | 36.0  | 900  | 0.4934          | 0.8792   |
  | 0.2689        | 37.0  | 925  | 0.3123          | 0.925    |
  | 0.1963        | 38.0  | 950  | 0.4691          | 0.8875   |
  | 0.2402        | 39.0  | 975  | 0.4508          | 0.8792   |
  | 0.1912        | 40.0  | 1000 | 0.3873          | 0.8917   |
  | 0.1512        | 41.0  | 1025 | 0.3153          | 0.9208   |
  | 0.1673        | 42.0  | 1050 | 0.2599          | 0.9417   |
  | 0.1981        | 43.0  | 1075 | 0.5351          | 0.8917   |
  | 0.1977        | 44.0  | 1100 | 0.4595          | 0.9042   |
  | 0.2621        | 45.0  | 1125 | 0.2737          | 0.9375   |
  | 0.1239        | 46.0  | 1150 | 0.1870          | 0.95     |
  | 0.2602        | 47.0  | 1175 | 0.2738          | 0.9167   |
  | 0.0681        | 48.0  | 1200 | 0.2707          | 0.9375   |
  | 0.1257        | 49.0  | 1225 | 0.2281          | 0.95     |
  | 0.1242        | 50.0  | 1250 | 0.2846          | 0.925    |
  | 0.1169        | 51.0  | 1275 | 0.2766          | 0.9167   |
  | 0.1701        | 52.0  | 1300 | 0.1858          | 0.9417   |
  | 0.185         | 53.0  | 1325 | 0.3373          | 0.9292   |
  | 0.0383        | 54.0  | 1350 | 0.3524          | 0.9208   |
  | 0.0808        | 55.0  | 1375 | 0.3378          | 0.925    |
  | 0.1444        | 56.0  | 1400 | 0.2609          | 0.9292   |
  | 0.0798        | 57.0  | 1425 | 0.2635          | 0.9417   |
  | 0.0324        | 58.0  | 1450 | 0.2550          | 0.9375   |
  | 0.0669        | 59.0  | 1475 | 0.2466          | 0.9458   |
  | 0.1389   