readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_8_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-1b_cv8italian
    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: 0.13654434668561125
  ---
  
  <!-- 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_cv8italian
  
  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: 0.1935
  - Wer: 0.1365
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 64
  - 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.725         | 0.18  | 400   | 0.3882          | 0.3921 |
  | 0.4309        | 0.36  | 800   | 0.4437          | 0.4091 |
  | 0.4152        | 0.54  | 1200  | 0.3773          | 0.3707 |
  | 0.397         | 0.72  | 1600  | 0.3458          | 0.3481 |
  | 0.386         | 0.9   | 2000  | 0.3343          | 0.3521 |
  | 0.3537        | 1.07  | 2400  | 0.3001          | 0.3087 |
  | 0.3376        | 1.25  | 2800  | 0.2857          | 0.3000 |
  | 0.346         | 1.43  | 3200  | 0.2974          | 0.3090 |
  | 0.3366        | 1.61  | 3600  | 0.2889          | 0.2934 |
  | 0.3302        | 1.79  | 4000  | 0.2715          | 0.2789 |
  | 0.3288        | 1.97  | 4400  | 0.2685          | 0.2855 |
  | 0.3202        | 2.15  | 4800  | 0.3065          | 0.3199 |
  | 0.3232        | 2.33  | 5200  | 0.2639          | 0.2808 |
  | 0.3118        | 2.51  | 5600  | 0.2906          | 0.2942 |
  | 0.312         | 2.69  | 6000  | 0.2593          | 0.2758 |
  | 0.3114        | 2.87  | 6400  | 0.2615          | 0.2819 |
  | 0.3048        | 3.05  | 6800  | 0.2879          | 0.3072 |
  | 0.2847        | 3.22  | 7200  | 0.2950          | 0.3100 |
  | 0.2849        | 3.4   | 7600  | 0.2873          | 0.2955 |
  | 0.2881        | 3.58  | 8000  | 0.2580          | 0.2736 |
  | 0.2951        | 3.76  | 8400  | 0.2743          | 0.2897 |
  | 0.2939        | 3.94  | 8800  | 0.2786          | 0.2974 |
  | 0.2771        | 4.12  | 9200  | 0.2598          | 0.2709 |
  | 0.2725        | 4.3   | 9600  | 0.2494          | 0.2544 |
  | 0.2606        | 4.48  | 10000 | 0.2360          | 0.2474 |
  | 0.2588        | 4.66  | 10400 | 0.2352          | 0.2486 |
  | 0.2665        | 4.84  | 10800 | 0.2397          | 0.2463 |
  | 0.2669        | 5.02  | 11200 | 0.2382          | 0.2471 |
  | 0.2465        | 5.19  | 11600 | 0.2363          | 0.2491 |
  | 0.2568        | 5.37  | 12000 | 0.2457          | 0.2542 |
  | 0.2525        | 5.55  | 12400 | 0.2372          | 0.2429 |
  | 0.2529        | 5.73  | 12800 | 0.2495          | 0.2656 |
  | 0.2512        | 5.91  | 13200 | 0.2339          | 0.2490 |
  | 0.2535        | 6.09  | 13600 | 0.2577          | 0.2701 |
  | 0.2353        | 6.27  | 14000 | 0.2462          | 0.2564 |
  | 0.2426        | 6.45  | 14400 | 0.2441          | 0.2537 |
  | 0.2361        | 6.63  | 14800 | 0.2407          | 0.2551 |
  | 0.2444        | 6.81  | 15200 | 0.2333          | 0.2470 |
  | 0.24          | 6.99  | 15600 | 0.2307          | 0.2480 |
  | 0.2269        | 7.17  | 16000 | 0.2328          | 0.2389 |
  | 0.2327        | 7.34  | 16400 | 0.2316          | 0.2437 |
  | 0.2321        | 7.52  | 16800 | 0.2217          | 0.2362 |
  | 0.225         | 7.7   | 17200 | 0.2244          | 0.2405 |
  | 0.2244        | 7.88  | 17600 | 0.2368          | 0.2444 |
  | 0.2194        | 8.06  | 18000 | 0.2250          | 0.2318 |
  | 0.2114        | 8.24  | 18400 | 0.2250          | 0.2345 |
  | 0.2185        | 8.42  | 18800 | 0.2198          | 0.2306 |
  | 0.2161        | 8.6   | 19200 | 0.2235          | 0.2319 |
  | 0.2131        | 8.78  | 19600 | 0.2190          | 0.2310 |
  | 0.218         | 8.96  | 20000 | 0.2229          | 0.2291 |
  | 0.2145        | 9.14  | 20400 | 0.2371          | 0.2362 |
  | 0.2102        | 9.31  | 20800 | 0.2321          | 0.2354 |
  | 0.2104        | 9.49  | 21200 | 0.2296          | 0.2322 |
  | 0.2159        | 9.67  | 21600 | 0.2230          | 0.2345 |
  | 0.217         | 9.85  | 22000 | 0.2227          | 0.2331