readme: ---
  language:
  - it
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300m - Italian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: it
      metrics:
      - name: Test WER
        type: wer
        value: 19.44
      - name: Test CER
        type: cer
        value: 4.47
      - name: Test WER (+LM)
        type: wer
        value: 14.08
      - name: Test CER (+LM)
        type: cer
        value: 3.67
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: it
      metrics:
      - name: Test WER
        type: wer
        value: 31.01
      - name: Test CER
        type: cer
        value: 9.27
      - name: Test WER (+LM)
        type: wer
        value: 22.09
      - name: Test CER (+LM)
        type: cer
        value: 7.9
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: it
      metrics:
      - name: Test WER
        type: wer
        value: 38.07
  ---
  
  <!-- 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-italian
  
  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_7_0 - IT dataset.
  It achieves the following results on the evaluation set:
  - Loss: inf
  - Wer: 0.1710
  
  ## 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: 64
  - eval_batch_size: 8
  - 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: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | No log        | 0.04  | 100   | inf             | 1.0    |
  | No log        | 0.09  | 200   | inf             | 0.9983 |
  | No log        | 0.13  | 300   | inf             | 0.7672 |
  | No log        | 0.18  | 400   | inf             | 0.6919 |
  | 2.9929        | 0.22  | 500   | inf             | 0.6266 |
  | 2.9929        | 0.26  | 600   | inf             | 0.5513 |
  | 2.9929        | 0.31  | 700   | inf             | 0.5081 |
  | 2.9929        | 0.35  | 800   | inf             | 0.4945 |
  | 2.9929        | 0.39  | 900   | inf             | 0.4720 |
  | 0.5311        | 0.44  | 1000  | inf             | 0.4387 |
  | 0.5311        | 0.48  | 1100  | inf             | 0.4411 |
  | 0.5311        | 0.53  | 1200  | inf             | 0.4429 |
  | 0.5311        | 0.57  | 1300  | inf             | 0.4322 |
  | 0.5311        | 0.61  | 1400  | inf             | 0.4532 |
  | 0.4654        | 0.66  | 1500  | inf             | 0.4492 |
  | 0.4654        | 0.7   | 1600  | inf             | 0.3879 |
  | 0.4654        | 0.75  | 1700  | inf             | 0.3836 |
  | 0.4654        | 0.79  | 1800  | inf             | 0.3743 |
  | 0.4654        | 0.83  | 1900  | inf             | 0.3687 |
  | 0.4254        | 0.88  | 2000  | inf             | 0.3793 |
  | 0.4254        | 0.92  | 2100  | inf             | 0.3766 |
  | 0.4254        | 0.97  | 2200  | inf             | 0.3705 |
  | 0.4254        | 1.01  | 2300  | inf             | 0.3272 |
  | 0.4254        | 1.05  | 2400  | inf             | 0.3185 |
  | 0.3997        | 1.1   | 2500  | inf             | 0.3244 |
  | 0.3997        | 1.14  | 2600  | inf             | 0.3082 |
  | 0.3997        | 1.18  | 2700  | inf             | 0.3040 |
  | 0.3997        | 1.23  | 2800  | inf             | 0.3028 |
  | 0.3997        | 1.27  | 2900  | inf             | 0.3112 |
  | 0.3668        | 1.32  | 3000  | inf             | 0.3110 |
  | 0.3668        | 1.36  | 3100  | inf             | 0.3067 |
  | 0.3668        | 1.4   | 3200  | inf             | 0.2961 |
  | 0.3668        | 1.45  | 3300  | inf             | 0.3081 |
  | 0.3668        | 1.49  | 3400  | inf             | 0.2936 |
  | 0.3645        | 1.54  | 3500  | inf             | 0.3037 |
  | 0.3645        | 1.58  | 3600  | inf             | 0.2974 |
  | 0.3645        | 1.62  | 3700  | inf             | 0.3010 |
  | 0.3645        | 1.67  | 3800  | inf             | 0.2985 |
  | 0.3645        | 1.71  | 3900  | inf           