readme: ---
  language:
  - eu
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  - et
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: xls-r-eus
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: eu
      metrics:
      - name: Test WER
        type: wer
        value: 0.17871523648578164
      - name: Test CER
        type: cer
        value: 0.032624506085144
  ---
  
  <!-- 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 MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - EU dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2278
  - Wer: 0.1787
  
  ## 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: 72
  - eval_batch_size: 72
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 144
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.2548        | 4.24  | 500   | 0.2470          | 0.3663 |
  | 0.1435        | 8.47  | 1000  | 0.2000          | 0.2791 |
  | 0.1158        | 12.71 | 1500  | 0.2030          | 0.2652 |
  | 0.1094        | 16.95 | 2000  | 0.2096          | 0.2605 |
  | 0.1004        | 21.19 | 2500  | 0.2150          | 0.2477 |
  | 0.0945        | 25.42 | 3000  | 0.2072          | 0.2369 |
  | 0.0844        | 29.66 | 3500  | 0.1981          | 0.2328 |
  | 0.0877        | 33.89 | 4000  | 0.2041          | 0.2425 |
  | 0.0741        | 38.14 | 4500  | 0.2353          | 0.2421 |
  | 0.0676        | 42.37 | 5000  | 0.2092          | 0.2213 |
  | 0.0623        | 46.61 | 5500  | 0.2217          | 0.2250 |
  | 0.0574        | 50.84 | 6000  | 0.2152          | 0.2179 |
  | 0.0583        | 55.08 | 6500  | 0.2207          | 0.2186 |
  | 0.0488        | 59.32 | 7000  | 0.2225          | 0.2159 |
  | 0.0456        | 63.56 | 7500  | 0.2293          | 0.2031 |
  | 0.041         | 67.79 | 8000  | 0.2277          | 0.2013 |
  | 0.0379        | 72.03 | 8500  | 0.2287          | 0.1991 |
  | 0.0381        | 76.27 | 9000  | 0.2233          | 0.1954 |
  | 0.0308        | 80.51 | 9500  | 0.2195          | 0.1835 |
  | 0.0291        | 84.74 | 10000 | 0.2266          | 0.1825 |
  | 0.0266        | 88.98 | 10500 | 0.2285          | 0.1801 |
  | 0.0266        | 93.22 | 11000 | 0.2292          | 0.1801 |
  | 0.0262        | 97.46 | 11500 | 0.2278          | 0.1788 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.18.4.dev0
  - Tokenizers 0.11.0
  