readme: ---
  language: fi
  datasets:
  - mozilla-foundation/common_voice_7_0
  metrics:
  - wer
  - cer
  tags:
  - generated_from_trainer
  - mozilla-foundation/common_voice_7_0
  - audio
  - automatic-speech-recognition
  - speech
  - robust-speech-event
  - hf-asr-leaderboard
  model-index:
  - name: XLS-R 1B Wav2Vec2 Finnish by Rasmus Toivanen
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: fi
      metrics:
      - name: Test WER
        type: wer
        value: 10.96
      - name: Test CER
        type: cer
        value: 2.81
  ---
  <!-- 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-fi-train-aug-lm-1B
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1499
  - Wer: 0.1955
  
  ## 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
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.6473        | 0.29  | 400  | 0.2857          | 0.3825 |
  | 0.6039        | 0.58  | 800  | 0.2459          | 0.3476 |
  | 0.4757        | 0.87  | 1200 | 0.2338          | 0.3274 |
  | 0.4473        | 1.15  | 1600 | 0.2246          | 0.3128 |
  | 0.4322        | 1.44  | 2000 | 0.1962          | 0.2805 |
  | 0.3961        | 1.73  | 2400 | 0.2070          | 0.2797 |
  | 0.3642        | 2.02  | 2800 | 0.1790          | 0.2473 |
  | 0.3561        | 2.31  | 3200 | 0.1769          | 0.2375 |
  | 0.282         | 2.6   | 3600 | 0.1672          | 0.2263 |
  | 0.2978        | 2.89  | 4000 | 0.1636          | 0.2192 |
  | 0.2722        | 3.17  | 4400 | 0.1637          | 0.2102 |
  | 0.2924        | 3.46  | 4800 | 0.1506          | 0.2021 |
  | 0.2631        | 3.75  | 5200 | 0.1499          | 0.1955 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  