readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-spanish-small
    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
  
  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: 0.3763
  - Wer: 0.1791
  
  ## 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: 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: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.2277        | 0.26  | 400   | 0.2601          | 0.2291 |
  | 0.2932        | 0.53  | 800   | 0.2950          | 0.2670 |
  | 0.3019        | 0.79  | 1200  | 0.3247          | 0.2766 |
  | 0.2987        | 1.05  | 1600  | 0.3031          | 0.2606 |
  | 0.261         | 1.32  | 2000  | 0.2994          | 0.2620 |
  | 0.2651        | 1.58  | 2400  | 0.3134          | 0.2700 |
  | 0.264         | 1.85  | 2800  | 0.3016          | 0.2641 |
  | 0.2475        | 2.11  | 3200  | 0.3135          | 0.2661 |
  | 0.2269        | 2.37  | 3600  | 0.3029          | 0.2562 |
  | 0.2389        | 2.64  | 4000  | 0.3035          | 0.2549 |
  | 0.2319        | 2.9   | 4400  | 0.3022          | 0.2551 |
  | 0.2123        | 3.16  | 4800  | 0.3256          | 0.2638 |
  | 0.2094        | 3.43  | 5200  | 0.3227          | 0.2712 |
  | 0.2121        | 3.69  | 5600  | 0.3085          | 0.2596 |
  | 0.207         | 3.96  | 6000  | 0.3041          | 0.2597 |
  | 0.1809        | 4.22  | 6400  | 0.3122          | 0.2524 |
  | 0.1846        | 4.48  | 6800  | 0.3254          | 0.2579 |
  | 0.1885        | 4.75  | 7200  | 0.2958          | 0.2437 |
  | 0.1923        | 5.01  | 7600  | 0.3136          | 0.2502 |
  | 0.1626        | 5.27  | 8000  | 0.3059          | 0.2488 |
  | 0.1704        | 5.54  | 8400  | 0.3082          | 0.2515 |
  | 0.1674        | 5.8   | 8800  | 0.3196          | 0.2509 |
  | 0.1691        | 6.06  | 9200  | 0.3193          | 0.25   |
  | 0.1499        | 6.33  | 9600  | 0.3529          | 0.2635 |
  | 0.1568        | 6.59  | 10000 | 0.3241          | 0.2481 |
  | 0.1538        | 6.86  | 10400 | 0.3354          | 0.2476 |
  | 0.1503        | 7.12  | 10800 | 0.3180          | 0.2402 |
  | 0.136         | 7.38  | 11200 | 0.3230          | 0.2397 |
  | 0.1413        | 7.65  | 11600 | 0.3178          | 0.2451 |
  | 0.147         | 7.91  | 12000 | 0.3170          | 0.2389 |
  | 0.1341        | 8.17  | 12400 | 0.3380          | 0.2501 |
  | 0.1329        | 8.44  | 12800 | 0.3265          | 0.2414 |
  | 0.1314        | 8.7   | 13200 | 0.3281          | 0.2482 |
  | 0.1312        | 8.97  | 13600 | 0.3259          | 0.2539 |
  | 0.12          | 9.23  | 14000 | 0.3291          | 0.2424 |
  | 0.1193        | 9.49  | 14400 | 0.3302          | 0.2412 |
  | 0.1189        | 9.76  | 14800 | 0.3376          | 0.2407 |
  | 0.1217        | 10.02 | 15200 | 0.3334          | 0.2400 |
  | 0.1118        | 10.28 | 15600 | 0.3359          | 0.2368 |
  | 0.1139        | 10.55 | 16000 | 0.3239          | 0.2335 |
  | 0.1106        | 10.81 | 16400 | 0.3374          | 0.2352 |
  | 0.1081        | 11.07 | 16800 | 0.3585          | 0.2434 |
  | 0.1063        | 11.34 | 17200 | 0.3639          | 0.2472 |
  | 0.1041        | 11.6  | 17600 | 0.3399          | 0.2423 |
  | 0.1062        | 11.87 | 18000 | 0.3410          | 0.2388 |
  | 0.1012        | 12.13 | 18400 | 0.3597          | 0.2413 |
  | 0.0953        | 12.39 | 18800 | 0.3440          | 0.2296 |
  | 0.097         | 12.66 | 19200 | 0.3440          | 0.2269 |
  | 0.0968        | 12.92 | 19600 | 0.3498          | 0.2333 |
  | 0.0902        | 13.18 | 20000 | 0.3471          | 0.2290 |
  | 0.0868        | 13.45 | 20400 | 0.3462          | 0.2266 |
  | 0.0892        | 13.71 | 20800 | 0.3373          | 0.2227 |
  | 0.0902        | 13.97 | 21200 | 0.3377          | 0.2240 |
  | 0.0846        | 14.24 | 21600 | 0.3484          | 0.2237 |
  | 0.0839        | 14.5  | 22000 | 0.3706          | 0.2260 |
  | 0.0834        | 14.77 | 22400 | 0.3430          | 0.2268 |
  | 0.0841        | 15.03 | 22800 | 0.3489          | 0.2259 |
  | 0.076         | 15.29 | 23200 | 0.3626          | 0.2281 |
  | 0.0771        | 15.56 | 23600 | 0.3624          | 0.2268 |
  | 0.0773   