readme: ---
  language:
  - sv
  license: cc0-1.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_9_0
  - generated_from_trainer
  - sv
  datasets:
  - mozilla-foundation/common_voice_9_0
  model-index:
  - name: XLS-R-300M - Swedish
    results:
    - task: 
        name: Automatic Speech Recognition 
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_9_0
        type: mozilla-foundation/common_voice_9_0
        split: test
        args: sv-SE
        WER: 
      metrics:
         - name: Test WER
           type: wer
           value: 7.72
         - name: Test CER
           type: cer
           value: 2.61
    - task: 
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: speech-recognition-community-v2/dev_data
        type: speech-recognition-community-v2/dev_data
        split: validation
        args: sv
      metrics:
         - name: Test WER
           type: wer
           value: 16.23
         - name: Test CER
           type: cer
           value: 8.21
    - task: 
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: speech-recognition-community-v2/dev_data
        type: speech-recognition-community-v2/dev_data
        split: test
        args: sv
      metrics:
         - name: Test WER
           type: wer
           value: 15.08
         - name: Test CER
           type: cer
           value: 7.51
  ---
  # 
  
  This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - SV-SE dataset.
  It achieves the following results on the evaluation set ("test" split, without LM):
  - Loss: 0.1318
  - Wer: 0.1121
  
  ## 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: 7.5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.2
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.9099        | 10.42 | 1000 | 2.8369          | 1.0    |
  | 1.0745        | 20.83 | 2000 | 0.1957          | 0.1673 |
  | 0.934         | 31.25 | 3000 | 0.1579          | 0.1389 |
  | 0.8691        | 41.66 | 4000 | 0.1457          | 0.1290 |
  | 0.8328        | 52.08 | 5000 | 0.1435          | 0.1205 |
  | 0.8068        | 62.5  | 6000 | 0.1350          | 0.1191 |
  | 0.7822        | 72.91 | 7000 | 0.1347          | 0.1155 |
  | 0.7769        | 83.33 | 8000 | 0.1321          | 0.1131 |
  | 0.7678        | 93.75 | 9000 | 0.1321          | 0.1115 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 2.2.2
  - Tokenizers 0.11.0
  