readme: ---
  language:
  - gn
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - gn
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: wav2vec2-xls-r-gn-cv7
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: pt
      metrics:
      - name: Validation WER
        type: wer
        value: 73.02
      - name: Validation CER
        type: cer
        value: 17.79
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7.0
        type: mozilla-foundation/common_voice_7_0
        args: gn
      metrics:
      - name: Test WER
        type: wer
        value: 62.65
  ---
  
  <!-- 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-gn-cv7
  
  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:
  - Loss: 1.7197
  - Wer: 0.7434
  
  ## 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: 0.0001
  - 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: 100
  - training_steps: 13000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 3.4669        | 6.24   | 100   | 3.3003          | 1.0    |
  | 3.3214        | 12.48  | 200   | 3.2090          | 1.0    |
  | 3.1619        | 18.73  | 300   | 2.6322          | 1.0    |
  | 1.751         | 24.97  | 400   | 1.4089          | 0.9803 |
  | 0.7997        | 31.24  | 500   | 0.9996          | 0.9211 |
  | 0.4996        | 37.48  | 600   | 0.9879          | 0.8553 |
  | 0.3677        | 43.73  | 700   | 0.9543          | 0.8289 |
  | 0.2851        | 49.97  | 800   | 1.0627          | 0.8487 |
  | 0.2556        | 56.24  | 900   | 1.0933          | 0.8355 |
  | 0.2268        | 62.48  | 1000  | 0.9191          | 0.8026 |
  | 0.1914        | 68.73  | 1100  | 0.9582          | 0.7961 |
  | 0.1749        | 74.97  | 1200  | 1.0502          | 0.8092 |
  | 0.157         | 81.24  | 1300  | 0.9998          | 0.7632 |
  | 0.1505        | 87.48  | 1400  | 1.0076          | 0.7303 |
  | 0.1278        | 93.73  | 1500  | 0.9321          | 0.75   |
  | 0.1078        | 99.97  | 1600  | 1.0383          | 0.7697 |
  | 0.1156        | 106.24 | 1700  | 1.0302          | 0.7763 |
  | 0.1107        | 112.48 | 1800  | 1.0419          | 0.7763 |
  | 0.091         | 118.73 | 1900  | 1.0694          | 0.75   |
  | 0.0829        | 124.97 | 2000  | 1.0257          | 0.7829 |
  | 0.0865        | 131.24 | 2100  | 1.2108          | 0.7368 |
  | 0.0907        | 137.48 | 2200  | 1.0458          | 0.7697 |
  | 0.0897        | 143.73 | 2300  | 1.1504          | 0.7895 |
  | 0.0766        | 149.97 | 2400  | 1.1663          | 0.7237 |
  | 0.0659        | 156.24 | 2500  | 1.1320          | 0.7632 |
  | 0.0699        | 162.48 | 2600  | 1.2586          | 0.7434 |
  | 0.0613        | 168.73 | 2700  | 1.1815          | 0.8158 |
  | 0.0598        | 174.97 | 2800  | 1.3299          | 0.75   |
  | 0.0577        | 181.24 | 2900  | 1.2035          | 0.7171 |
  | 0.0576        | 187.48 | 3000  | 1.2134          | 0.7434 |
  | 0.0518        | 193.73 | 3100  | 1.3406          | 0.7566 |
  | 0.0524        | 199.97 | 3200  | 1.4251          | 0.75   |
  | 0.0467        | 206.24 | 3300  | 1.3533          | 0.7697 |
  | 0.0428        | 212.48 | 3400  | 1.2463          | 0.7368 |
  | 0.0453        | 218.73 | 3500  | 1.4532          | 0.7566 |
  | 0.0473        | 224.97 | 3600  | 1.3152          | 0.7434 |
  | 0.0451        | 231.24 | 3700  | 1.2232          | 0.7368 |
  | 0.0361        | 237.48 | 3800  | 1.2938          | 0.7171 |
  | 0.045         | 243.73 | 3900  | 1.4148          | 0.7434 |
  | 0.0422        | 249.97 | 4000  | 1.3786          | 0.7961 |
  | 0.036         | 256.24 | 4100  | 1.4488          | 0.7697 |
  | 0.0352        | 262.48 | 4200  | 1.2294          | 0.6776 |
  | 0.0326        | 268.73 | 4300  | 1.2796          | 0.6974 |
  | 0.034         | 274.97 | 4400  | 1.3805          | 0.7303 |
  | 0.0305        | 281.24 | 4500  | 1.4994          | 0.7237 |
  | 0.0325        | 287.48 | 4600  | 1.4330          | 0.6908 |
  | 0.0338        | 293.73 | 4700  | 1.3091          | 0.7368 |
  | 0.0306        | 299.97 | 4800  | 1.21