readme: ---
  license: apache-2.0
  language: it
  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-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: 17.17
      - name: Test CER
        type: cer
        value: 4.27
      - name: Test WER (+LM)
        type: wer
        value: 12.07
      - name: Test CER (+LM)
        type: cer
        value: 3.52
    - 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: 24.29
      - name: Test CER
        type: cer
        value: 8.1
      - name: Test WER (+LM)
        type: wer
        value: 17.36
      - name: Test CER (+LM)
        type: cer
        value: 7.94
    - 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: 33.66
  ---
  
  <!-- 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-robust
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Italian splits of the following datasets:
  - Mozilla Foundation Common Voice V7 dataset
  - [LibriSpeech multilingual](http://www.openslr.org/94)
  - [TED multilingual](https://www.openslr.org/100/)
  - [Voxforge](http://www.voxforge.org/it/Downloads)
  - [M-AILABS Speech Dataset](https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/) 
  - [EuroParl-ST](https://www.mllp.upv.es/europarl-st/)
  - [EMOVO](http://voice.fub.it/activities/corpora/emovo/index.html) 
  - [MSPKA](http://www.mspkacorpus.it/) 
  
  ## 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: 32
  - 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: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | No log        | 0.06  | 400   | 0.7508          | 0.7354 |
  | 2.3127        | 0.11  | 800   | 0.5888          | 0.5882 |
  | 0.7256        | 0.17  | 1200  | 0.5121          | 0.5247 |
  | 0.6692        | 0.22  | 1600  | 0.4774          | 0.5028 |
  | 0.6384        | 0.28  | 2000  | 0.4832          | 0.4885 |
  | 0.6384        | 0.33  | 2400  | 0.4410          | 0.4581 |
  | 0.6199        | 0.39  | 2800  | 0.4160          | 0.4331 |
  | 0.5972        | 0.44  | 3200  | 0.4136          | 0.4275 |
  | 0.6048        | 0.5   | 3600  | 0.4362          | 0.4538 |
  | 0.5627        | 0.55  | 4000  | 0.4313          | 0.4469 |
  | 0.5627        | 0.61  | 4400  | 0.4425          | 0.4579 |
  | 0.5855        | 0.66  | 4800  | 0.3859          | 0.4133 |
  | 0.5702        | 0.72  | 5200  | 0.3974          | 0.4097 |
  | 0.55          | 0.77  | 5600  | 0.3931          | 0.4134 |
  | 0.5624        | 0.83  | 6000  | 0.3900          | 0.4126 |
  | 0.5624        | 0.88  | 6400  | 0.3622          | 0.3899 |
  | 0.5615        | 0.94  | 6800  | 0.3755          | 0.4067 |
  | 0.5472        | 0.99  | 7200  | 0.3980          | 0.4284 |
  | 0.5663        | 1.05  | 7600  | 0.3553          | 0.3782 |
  | 0.5189        | 1.1   | 8000  | 0.3538          | 0.3726 |
  | 0.5189        | 1.16  | 8400  | 0.3425          | 0.3624 |
  | 0.518         | 1.21  | 8800  | 0.3431          | 0.3651 |
  | 0.5399        | 1.27  | 9200  | 0.3442          | 0.3573 |
  | 0.5303        | 1.32  | 9600  | 0.3241          | 0.3404 |
  | 0.5043        | 1.38  | 10000 | 0.3175          | 0.3378 |
  | 0.5043        | 1.43  | 10400 | 0.3265          | 0.3501 |
  | 0.4968        | 1.49  | 10800 | 0.3539          | 0.3703 |
  | 0.5102        | 1.54  | 11200 | 0.3323          | 0.3506 |
  | 0.5008        | 1.6   | 11600 | 0.3188          | 0.3433 |
  | 0.4996        | 1.65  | 12000 | 0.3162          | 0.3388 |
  | 0.4996        | 1.71  | 12400 | 0.3353          | 0.3552 |
  | 0.5007        | 1.76  | 12800 | 0.3152          | 0.3317 |
  | 0.4956        | 1.82  | 13200 | 0.3207          | 0.3430 |
  | 0.5205        | 1.87  