readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_8_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-1b_cv8_italian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice_8_0
        type: common_voice_8_0
        config: it
        split: train
        args: it
      metrics:
      - name: Wer
        type: wer
        value: 0.22980080878736542
  ---
  
  <!-- 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_cv8_italian
  
  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_8_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1608
  - Wer: 0.2298
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.3409        | 0.36  | 400   | 0.2870          | 0.3814 |
  | 0.4548        | 0.72  | 800   | 0.3603          | 0.4545 |
  | 0.3866        | 1.08  | 1200  | 0.2319          | 0.3287 |
  | 0.3085        | 1.43  | 1600  | 0.2375          | 0.3421 |
  | 0.3163        | 1.79  | 2000  | 0.2462          | 0.3483 |
  | 0.3045        | 2.15  | 2400  | 0.2224          | 0.3239 |
  | 0.284         | 2.51  | 2800  | 0.2631          | 0.3540 |
  | 0.2947        | 2.87  | 3200  | 0.2475          | 0.3428 |
  | 0.2866        | 3.23  | 3600  | 0.2744          | 0.3550 |
  | 0.2742        | 3.58  | 4000  | 0.2781          | 0.3848 |
  | 0.2708        | 3.94  | 4400  | 0.2491          | 0.3451 |
  | 0.259         | 4.3   | 4800  | 0.2526          | 0.3483 |
  | 0.2624        | 4.66  | 5200  | 0.2467          | 0.3433 |
  | 0.2674        | 5.02  | 5600  | 0.2116          | 0.3174 |
  | 0.2326        | 5.38  | 6000  | 0.2120          | 0.3166 |
  | 0.2347        | 5.73  | 6400  | 0.2166          | 0.3188 |
  | 0.2359        | 6.09  | 6800  | 0.2451          | 0.3424 |
  | 0.2218        | 6.45  | 7200  | 0.2333          | 0.3320 |
  | 0.2395        | 6.81  | 7600  | 0.2503          | 0.3431 |
  | 0.2378        | 7.17  | 8000  | 0.2318          | 0.3291 |
  | 0.2318        | 7.53  | 8400  | 0.2235          | 0.3244 |
  | 0.2281        | 7.88  | 8800  | 0.2118          | 0.3100 |
  | 0.2304        | 8.24  | 9200  | 0.2263          | 0.3261 |
  | 0.2281        | 8.6   | 9600  | 0.2336          | 0.3363 |
  | 0.221         | 8.96  | 10000 | 0.2114          | 0.3080 |
  | 0.2042        | 9.32  | 10400 | 0.2120          | 0.3092 |
  | 0.1971        | 9.68  | 10800 | 0.2041          | 0.3000 |
  | 0.1931        | 10.04 | 11200 | 0.1923          | 0.2960 |
  | 0.2068        | 10.39 | 11600 | 0.2042          | 0.3016 |
  | 0.194         | 10.75 | 12000 | 0.2006          | 0.3028 |
  | 0.1884        | 11.11 | 12400 | 0.2059          | 0.3047 |
  | 0.1775        | 11.47 | 12800 | 0.2134          | 0.3065 |
  | 0.185         | 11.83 | 13200 | 0.2022          | 0.2995 |
  | 0.1806        | 12.19 | 13600 | 0.2041          | 0.2957 |
  | 0.1801        | 12.54 | 14000 | 0.2024          | 0.2950 |
  | 0.1894        | 12.9  | 14400 | 0.2000          | 0.2998 |
  | 0.1733        | 13.26 | 14800 | 0.1978          | 0.2902 |
  | 0.1596        | 13.62 | 15200 | 0.1851          | 0.2817 |
  | 0.165         | 13.98 | 15600 | 0.1921          | 0.2860 |
  | 0.1753        | 14.34 | 16000 | 0.1961          | 0.2905 |
  | 0.1665        | 14.69 | 16400 | 0.1928          | 0.2876 |
  | 0.1631        | 15.05 | 16800 | 0.1904          | 0.2855 |
  | 0.1561        | 15.41 | 17200 | 0.1962          | 0.2872 |
  | 0.1665        | 15.77 | 17600 | 0.1868          | 0.2837 |
  | 0.156         | 16.13 | 18000 | 0.1985          | 0.2893 |
  | 0.1584        | 16.49 | 18400 | 0.2013          | 0.2923 |
  | 0.1579        | 16.85 | 18800 | 0.1912          | 0.2826 |
  | 0.1573        | 17.2  | 19200 | 0.1976          | 0.2872 |
  | 0.1552        | 17.56 | 19600 | 0.1851          | 0.2728 |
  | 0.1408        | 17.92 | 20000 | 0.1804          | 0.2774 |
  | 0.1383        | 18.28 | 20400 | 0.1788          | 0.2702 |
  | 0.1325        | 18.64 | 20800 | 0.1720          | 0.2686 |
  | 0.1348        | 19.0  | 21200 | 0.1761          | 0.2674 |
  | 0.1259        | 19.35 | 21600 | 0.1792          | 0.2700 |
  | 0.1277        | 19.71 | 22000 | 0.1760          | 0.