readme: ---
  license: apache-2.0
  language: tr
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-tr
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice tr
        type: common_voice_8_0
        args: tr
      metrics:
      - name: Test WER
        type: wer
        value: 28.69
  ---
  
  <!-- 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-large-xls-r-300m-tr
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - TR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2224
  - Wer: 0.2869
  
  ## 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: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 6.8222        | 0.64  | 500   | 3.5026          | 1.0    |
  | 3.2136        | 1.28  | 1000  | 3.0593          | 1.0000 |
  | 2.8882        | 1.91  | 1500  | 2.4670          | 0.9939 |
  | 2.3743        | 2.55  | 2000  | 1.1844          | 0.8657 |
  | 1.9456        | 3.19  | 2500  | 0.8228          | 0.7397 |
  | 1.7781        | 3.83  | 3000  | 0.6826          | 0.6753 |
  | 1.6848        | 4.46  | 3500  | 0.5885          | 0.6140 |
  | 1.6228        | 5.1   | 4000  | 0.5274          | 0.5789 |
  | 1.5768        | 5.74  | 4500  | 0.4900          | 0.5519 |
  | 1.5431        | 6.38  | 5000  | 0.4508          | 0.5238 |
  | 1.5019        | 7.02  | 5500  | 0.4248          | 0.5021 |
  | 1.4684        | 7.65  | 6000  | 0.4009          | 0.4827 |
  | 1.4635        | 8.29  | 6500  | 0.3830          | 0.4700 |
  | 1.4291        | 8.93  | 7000  | 0.3707          | 0.4595 |
  | 1.4271        | 9.57  | 7500  | 0.3570          | 0.4514 |
  | 1.3938        | 10.2  | 8000  | 0.3479          | 0.4378 |
  | 1.3914        | 10.84 | 8500  | 0.3396          | 0.4368 |
  | 1.3767        | 11.48 | 9000  | 0.3253          | 0.4262 |
  | 1.3641        | 12.12 | 9500  | 0.3251          | 0.4178 |
  | 1.355         | 12.76 | 10000 | 0.3138          | 0.4136 |
  | 1.336         | 13.39 | 10500 | 0.3121          | 0.4069 |
  | 1.3292        | 14.03 | 11000 | 0.3041          | 0.4014 |
  | 1.3249        | 14.67 | 11500 | 0.3014          | 0.3931 |
  | 1.3156        | 15.31 | 12000 | 0.3014          | 0.3929 |
  | 1.313         | 15.94 | 12500 | 0.2969          | 0.3968 |
  | 1.3068        | 16.58 | 13000 | 0.2965          | 0.3966 |
  | 1.2785        | 17.22 | 13500 | 0.2943          | 0.3850 |
  | 1.2867        | 17.86 | 14000 | 0.2912          | 0.3782 |
  | 1.2714        | 18.49 | 14500 | 0.2819          | 0.3747 |
  | 1.2844        | 19.13 | 15000 | 0.2840          | 0.3740 |
  | 1.2684        | 19.77 | 15500 | 0.2913          | 0.3828 |
  | 1.26          | 20.41 | 16000 | 0.2739          | 0.3674 |
  | 1.2543        | 21.05 | 16500 | 0.2740          | 0.3691 |
  | 1.2532        | 21.68 | 17000 | 0.2709          | 0.3756 |
  | 1.2409        | 22.32 | 17500 | 0.2669          | 0.3593 |
  | 1.2404        | 22.96 | 18000 | 0.2673          | 0.3576 |
  | 1.2347        | 23.6  | 18500 | 0.2678          | 0.3643 |
  | 1.2351        | 24.23 | 19000 | 0.2715          | 0.3650 |
  | 1.2409        | 24.87 | 19500 | 0.2637          | 0.3571 |
  | 1.2152        | 25.51 | 20000 | 0.2785          | 0.3609 |
  | 1.2046        | 26.15 | 20500 | 0.2610          | 0.3508 |
  | 1.2082        | 26.79 | 21000 | 0.2619          | 0.3461 |
  | 1.2109        | 27.42 | 21500 | 0.2597          | 0.3502 |
  | 1.2014        | 28.06 | 22000 | 0.2608          | 0.3468 |
  | 1.1948        | 28.7  | 22500 | 0.2573          | 0.3457 |
  | 1.205         | 29.34 | 23000 | 0.2619          | 0.3464 |
  | 1.2019        | 29.97 | 23500 | 0.2559          | 0.3474 |
  | 1.1917        | 30.61 | 24000 | 0.2601          | 0.3462 |
  | 1.1939        | 31.25 | 24500 | 0.2575          | 0.3387 |
  | 1.1882        | 31.89 | 25000 | 0.2535          | 0.3368 |
  | 1.191         | 32.53 | 25500 | 0.2489          | 0.3365 |
  | 1.1767        | 33.16 | 26000 | 0.2501          | 0.3347 |
  | 1.167         | 33.8  | 26500 | 0.2504          | 0.3347 |
  | 1.1678        | 34.44 | 27000 | 0.2480          | 0.3378 |
  | 1.1803       