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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 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
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Dataset used to train patrickvonplaten/wav2vec2-xls-r-phoneme-300m-sv