readme: ---
  language:
  - sv
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-phoneme-300m-sv
    results: []
  ---
  
  <!-- 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-xls-r-phoneme-300m-sv
  
  **Note**: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: https://github.com/microsoft/UniSpeech/blob/main/UniSpeech/examples/unispeech/data/sv/phonesMatches_reduced.json
  
  One can simply download the file, rename it to `vocab.json` and load a `Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/vocab.json/")`.
  
  This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - SV-SE dataset.
  
  It achieves the following results on the evaluation set:
  - Loss: 0.9707
  - PER: 0.2215
  
  ## 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: 0.0005
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 32
  - total_eval_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: 20.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  See Tensorboard traces
  
  
  ### Framework versions
  
  - Transformers 4.13.0.dev0
  - Pytorch 1.8.1
  - Datasets 1.16.2.dev0
  - Tokenizers 0.10.3
  