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This model is a fine-tuned version of 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
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Dataset used to train marinone94/xls-r-300m-sv-robust

Evaluation results

  • Test WER on mozilla-foundation/common_voice_9_0
    self-reported
    7.720
  • Test CER on mozilla-foundation/common_voice_9_0
    self-reported
    2.610
  • Test WER on speech-recognition-community-v2/dev_data
    self-reported
    16.230
  • Test CER on speech-recognition-community-v2/dev_data
    self-reported
    8.210
  • Test WER on speech-recognition-community-v2/dev_data
    self-reported
    15.080
  • Test CER on speech-recognition-community-v2/dev_data
    self-reported
    7.510