readme: ---
  tags:
  - "es"
  - "robust-speech-event"
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-spanish-small-v3
    results: []
  ---
  
  <!-- 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-large-xls-r-300m-spanish-small-v3
  
  This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3986
  - Wer: 0.1980
  
  ## 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: 0.0004
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 25
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.2372        | 0.26  | 400   | 0.3011          | 0.2660 |
  | 0.3413        | 0.53  | 800   | 0.3559          | 0.3228 |
  | 0.3598        | 0.79  | 1200  | 0.3753          | 0.3400 |
  | 0.3529        | 1.05  | 1600  | 0.3385          | 0.2979 |
  | 0.3133        | 1.32  | 2000  | 0.3559          | 0.3056 |
  | 0.3158        | 1.58  | 2400  | 0.3364          | 0.2994 |
  | 0.3092        | 1.85  | 2800  | 0.3210          | 0.2876 |
  | 0.2919        | 2.11  | 3200  | 0.3460          | 0.3010 |
  | 0.2666        | 2.37  | 3600  | 0.3543          | 0.3036 |
  | 0.2819        | 2.64  | 4000  | 0.3477          | 0.2959 |
  | 0.283         | 2.9   | 4400  | 0.3492          | 0.2968 |
  | 0.2484        | 3.16  | 4800  | 0.3647          | 0.2993 |
  | 0.2371        | 3.43  | 5200  | 0.3601          | 0.2942 |
  | 0.2382        | 3.69  | 5600  | 0.3656          | 0.3019 |
  | 0.2425        | 3.96  | 6000  | 0.3379          | 0.2873 |
  | 0.2092        | 4.22  | 6400  | 0.3385          | 0.2736 |
  | 0.2171        | 4.48  | 6800  | 0.3503          | 0.2889 |
  | 0.2185        | 4.75  | 7200  | 0.3289          | 0.2727 |
  | 0.2236        | 5.01  | 7600  | 0.3447          | 0.2771 |
  | 0.1882        | 5.27  | 8000  | 0.3586          | 0.2860 |
  | 0.1986        | 5.54  | 8400  | 0.3404          | 0.2829 |
  | 0.2055        | 5.8   | 8800  | 0.3561          | 0.2869 |
  | 0.196         | 6.06  | 9200  | 0.3633          | 0.2811 |
  | 0.1748        | 6.33  | 9600  | 0.3703          | 0.2818 |
  | 0.1758        | 6.59  | 10000 | 0.3525          | 0.2816 |
  | 0.1819        | 6.86  | 10400 | 0.3581          | 0.2765 |
  | 0.1715        | 7.12  | 10800 | 0.3480          | 0.2628 |
  | 0.1606        | 7.38  | 11200 | 0.3490          | 0.2703 |
  | 0.1632        | 7.65  | 11600 | 0.3461          | 0.2706 |
  | 0.1638        | 7.91  | 12000 | 0.3458          | 0.2673 |
  | 0.1552        | 8.17  | 12400 | 0.3646          | 0.2732 |
  | 0.154         | 8.44  | 12800 | 0.3706          | 0.2726 |
  | 0.1512        | 8.7   | 13200 | 0.3609          | 0.2683 |
  | 0.149         | 8.97  | 13600 | 0.3610          | 0.2668 |
  | 0.1357        | 9.23  | 14000 | 0.3693          | 0.2740 |
  | 0.1375        | 9.49  | 14400 | 0.3677          | 0.2625 |
  | 0.1391        | 9.76  | 14800 | 0.3795          | 0.2762 |
  | 0.1378        | 10.02 | 15200 | 0.3541          | 0.2592 |
  | 0.1197        | 10.28 | 15600 | 0.3562          | 0.2507 |
  | 0.1259        | 10.55 | 16000 | 0.3612          | 0.2584 |
  | 0.1266        | 10.81 | 16400 | 0.3470          | 0.2527 |
  | 0.1199        | 11.07 | 16800 | 0.3721          | 0.2571 |
  | 0.1157        | 11.34 | 17200 | 0.3734          | 0.2571 |
  | 0.1107        | 11.6  | 17600 | 0.3730          | 0.2589 |
  | 0.1148        | 11.87 | 18000 | 0.3648          | 0.2536 |
  | 0.1095        | 12.13 | 18400 | 0.3746          | 0.2521 |
  | 0.1047        | 12.39 | 18800 | 0.3566          | 0.2530 |
  | 0.1043        | 12.66 | 19200 | 0.3794          | 0.2545 |
  | 0.1066        | 12.92 | 19600 | 0.3548          | 0.2439 |
  | 0.0974        | 13.18 | 20000 | 0.3702          | 0.2461 |
  | 0.0978        | 13.45 | 20400 | 0.3721          | 0.2492 |
  | 0.095         | 13.71 | 20800 | 0.3599          | 0.2467 |
  | 0.0963        | 13.97 | 21200 | 0.3650          | 0.2402 |
  | 0.0902        | 14.24 | 21600 | 0.3689          | 0.2459 |
  | 0.0898        | 14.5  | 22000 | 0.3832          | 0.2452 |
  | 0.0865        | 14.77 | 22400 | 0.3982          | 0.2436 |
  | 0.0911        | 15.03 | 22800 | 0.3785          | 0.2398 |
  | 0.0793        | 15.29 | 23200 | 0.3731          | 0.2396 |
  | 0.0806        | 15.56 | 23600 | 0