readme: ---
  license: apache-2.0
  language:
  - es
  tags:
  - es
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-300m-36-tokens-with-lm-es
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice es
        type: common_voice
        args: es
      metrics:
      - name: Test WER
        type: wer
        value: 0.08677014042867702
      - name: Test CER
        type: cer
        value: 0.02810974186831335
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: es
      metrics:
      - name: Test WER
        type: wer
        value: 31.68
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: es
      metrics:
      - name: Test WER
        type: wer
        value: 34.45
  ---
  # Wav2Vec2-xls-r-300m-36-tokens-with-lm-es
  
  <!-- 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. -->
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Wer: 0.0868
  - Cer: 0.0281
  
  This model consists of a Wav2Vec2 model with an additional KenLM 5-gram language model for CTC decoding.  
  The model is trained removing all characters that are not lower-case unaccented letters between `a-z` or the Spanish accented vowels `á`, `é`, `í`, `ó`, `ú` or the dieresis on the vowel `u` - `ü`.  
  
  ## 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: 2
  - total_train_batch_size: 32
  - 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    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 3.6512        | 0.07  | 400    | 0.5734          | 0.4325 |
  | 0.4404        | 0.14  | 800    | 0.3329          | 0.3021 |
  | 0.3465        | 0.22  | 1200   | 0.3067          | 0.2871 |
  | 0.3214        | 0.29  | 1600   | 0.2808          | 0.2694 |
  | 0.319         | 0.36  | 2000   | 0.2755          | 0.2677 |
  | 0.3015        | 0.43  | 2400   | 0.2667          | 0.2437 |
  | 0.3102        | 0.51  | 2800   | 0.2679          | 0.2475 |
  | 0.2955        | 0.58  | 3200   | 0.2591          | 0.2421 |
  | 0.292         | 0.65  | 3600   | 0.2547          | 0.2404 |
  | 0.2961        | 0.72  | 4000   | 0.2824          | 0.2716 |
  | 0.2906        | 0.8   | 4400   | 0.2531          | 0.2321 |
  | 0.2886        | 0.87  | 4800   | 0.2668          | 0.2573 |
  | 0.2934        | 0.94  | 5200   | 0.2608          | 0.2454 |
  | 0.2844        | 1.01  | 5600   | 0.2414          | 0.2233 |
  | 0.2649        | 1.09  | 6000   | 0.2412          | 0.2198 |
  | 0.2587        | 1.16  | 6400   | 0.2432          | 0.2211 |
  | 0.2631        | 1.23  | 6800   | 0.2414          | 0.2225 |
  | 0.2584        | 1.3   | 7200   | 0.2489          | 0.2290 |
  | 0.2588        | 1.37  | 7600   | 0.2341          | 0.2156 |
  | 0.2581        | 1.45  | 8000   | 0.2323          | 0.2155 |
  | 0.2603        | 1.52  | 8400   | 0.2423          | 0.2231 |
  | 0.2527        | 1.59  | 8800   | 0.2381          | 0.2192 |
  | 0.2588        | 1.66  | 9200   | 0.2323          | 0.2176 |
  | 0.2543        | 1.74  | 9600   | 0.2391          | 0.2151 |
  | 0.2528        | 1.81  | 10000  | 0.2295          | 0.2091 |
  | 0.2535        | 1.88  | 10400  | 0.2317          | 0.2099 |
  | 0.2501        | 1.95  | 10800  | 0.2225          | 0.2105 |
  | 0.2441        | 2.03  | 11200  | 0.2356          | 0.2180 |
  | 0.2275        | 2.1   | 11600  | 0.2341          | 0.2115 |
  | 0.2281        | 2.17  | 12000  | 0.2269          | 0.2117 |
  | 0.227         | 2.24  | 12400  | 0.2367          | 0.2125 |
  | 0.2471        | 2.32  | 12800  | 0.2307          | 0.2090 |
  | 0.229         | 2.39  | 13200  | 0.2231          | 0.2005 |
  | 0.2325        | 2.46  | 13600  | 0.2243          | 0.2100 |
  | 0.2314        | 2.53  | 14000  | 0.2252          | 0.2098 |
  | 0.2309        | 2.6   | 14400  | 0.2269          | 0.2089 |
  | 0.2267        | 2.68  | 14800  | 0.2155          | 0.1976 |
  | 0.225         | 2.75  | 15200  | 0.2263          | 0.2067 |
  | 0.2309        | 2.82  