readme: ---
  language:
  - it
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-1b - 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: 32.74
      - name: Test CER
        type: cer
        value: 7.83
      - name: Test WER (+LM)
        type: wer
        value: 19.55
      - name: Test CER (+LM)
        type: cer
        value: 5.59
    - 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: 43.23
      - name: Test CER
        type: cer
        value: 13.37
      - name: Test WER (+LM)
        type: wer
        value: 27.51
      - name: Test CER (+LM)
        type: cer
        value: 10.69
    - 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: 51.12
  ---
  
  <!-- 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-1b-italian-robust
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the Common Voice 7 & Libri Speech datasets.
  It achieves the following results on the evaluation set:
  - Loss: 0.2428
  - Wer: 0.2960
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - 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: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | No log        | 0.07  | 400   | 1.0053          | 0.8058 |
  | 1.5087        | 0.13  | 800   | 0.9127          | 0.8104 |
  | 0.9552        | 0.2   | 1200  | 1.0360          | 0.8836 |
  | 0.9555        | 0.27  | 1600  | 0.9980          | 0.8577 |
  | 1.0259        | 0.34  | 2000  | 1.0103          | 0.8842 |
  | 1.0259        | 0.4   | 2400  | 0.9119          | 0.8466 |
  | 1.0365        | 0.47  | 2800  | 0.9000          | 0.8281 |
  | 1.0069        | 0.54  | 3200  | 0.7976          | 0.7875 |
  | 0.9688        | 0.61  | 3600  | 0.8126          | 0.8051 |
  | 0.9638        | 0.67  | 4000  | 0.7921          | 0.7903 |
  | 0.9638        | 0.74  | 4400  | 0.7703          | 0.7783 |
  | 0.9327        | 0.81  | 4800  | 0.7253          | 0.7463 |
  | 0.8992        | 0.88  | 5200  | 0.6841          | 0.7171 |
  | 0.8693        | 0.94  | 5600  | 0.6867          | 0.7250 |
  | 0.8433        | 1.01  | 6000  | 0.7077          | 0.7302 |
  | 0.8433        | 1.08  | 6400  | 0.6685          | 0.7091 |
  | 0.8499        | 1.14  | 6800  | 0.6355          | 0.6825 |
  | 0.8159        | 1.21  | 7200  | 0.6283          | 0.6800 |
  | 0.8001        | 1.28  | 7600  | 0.6288          | 0.6743 |
  | 0.7883        | 1.35  | 8000  | 0.5995          | 0.6633 |
  | 0.7883        | 1.41  | 8400  | 0.6195          | 0.6726 |
  | 0.7863        | 1.48  | 8800  | 0.6039          | 0.6588 |
  | 0.7713        | 1.55  | 9200  | 0.5842          | 0.6490 |
  | 0.7572        | 1.62  | 9600  | 0.5975          | 0.6533 |
  | 0.7442        | 1.68  | 10000 | 0.5508          | 0.6233 |
  | 0.7442        | 1.75  | 10400 | 0.5521          | 0.6209 |
  | 0.7296        | 1.82  | 10800 | 0.5760          | 0.6245 |
  | 0.7205        | 1.89  | 11200 | 0.5593          | 0.6144 |
  | 0.7106        | 1.95  | 11600 | 0.5672          | 0.6220 |
  | 0.7146        | 2.02  | 12000 | 0.5134          | 0.5911 |
  | 0.7146        | 2.09  | 12400 | 0.5069          | 0.5811 |
  | 0.6944        | 2.15  | 12800 | 0.5022          | 0.5962 |
  | 0.6817        | 2.22  | 13200 | 0.4989          | 0.5813 |
  | 0.6721        | 2.29  | 13600 | 0.4941          | 0.5742 |
  | 0.6774        | 2.36  | 14000 | 0.4775          | 0.5676 |
  | 0.6774        | 2.42  | 14400 | 0.4694          | 0.5525 |
  | 0.6621        | 2.49  | 14800 | 0.4720          | 0.5514 |
  | 0.6599        | 2.56  | 15200 | 0.4714          | 0.5553 |
  | 0.6591        | 2.63  | 15600 | 0.4578          | 0.5397 |
  | 0.645         | 2.69  | 160