readme: ---
  language:
  - sv-SE
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  - cer
  model-index:
  - name: wav2vec2-xls-r-300m-swedish
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice sv-SE
        args: sv-SE
      metrics:
      - type: wer
        value: 24.73
        name: Test WER
        args:
        - learning_rate: 7.5e-05
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 1000
        - num_epochs: 50
        - mixed_precision_training: Native AMP
      - type: cer
        value: 7.58
        name: Test CER
        args:
        - learning_rate: 7.5e-05
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 1000
        - num_epochs: 50
        - mixed_precision_training: Native AMP
  ---
  
  <!-- 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-large-xls-r-300m-Swedish
  
  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: 0.3641
  - Wer: 0.2473
  - Cer: 0.0758
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - train_batch_size: 64
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 6.1097        | 5.49  | 500  | 3.1422          | 1.0    | 1.0    |
  | 2.985         | 10.98 | 1000 | 1.7357          | 0.9876 | 0.4125 |
  | 1.0363        | 16.48 | 1500 | 0.4773          | 0.3510 | 0.1047 |
  | 0.6111        | 21.97 | 2000 | 0.3937          | 0.2998 | 0.0910 |
  | 0.4942        | 27.47 | 2500 | 0.3779          | 0.2776 | 0.0844 |
  | 0.4421        | 32.96 | 3000 | 0.3745          | 0.2630 | 0.0807 |
  | 0.4018        | 38.46 | 3500 | 0.3685          | 0.2553 | 0.0781 |
  | 0.3759        | 43.95 | 4000 | 0.3618          | 0.2488 | 0.0761 |
  | 0.3646        | 49.45 | 4500 | 0.3641          | 0.2473 | 0.0758 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  