readme: ---
  language: et
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  - cer
  tags:
  - generated_from_trainer
  - mozilla-foundation/common_voice_8_0
  - audio
  - automatic-speech-recognition
  - speech
  - robust-speech-event
  - hf-asr-leaderboard
  model-index:
  - name: XLS-R 1B Wav2Vec2 Estonian by Rasmus Toivanen
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: et
      metrics:
      - name: Test WER
        type: wer
        value: 20.12
      - name: Test CER
        type: cer
        value: 3.82
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: et
      metrics:
      - name: Test WER
        type: wer
        value: 40.77
      - name: Test CER
        type: cer
        value: 12.32
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: et
      metrics:
      - name: Test WER
        type: wer
        value: 41.97
  ---
  <!-- 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-xlsr-et-lm-1B
  
  This model was finetuned with mozilla_foundation/common_voice_8_0 et with train+other+validation splits.
  It achieves the following results on the test set:
  (Loss reported with last eval step at step 2000/2040 during training)
  - Loss: 0.2150 
  - Wer: 0.2012
  
  ## 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: 0.00005
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 1
  - 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  