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metadata
language:
  - sv-SE
license: cc0-1.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - sv
  - robust-speech-event
  - model_for_talk
datasets:
  - common_voice
model-index:
  - name: XLS-R-300M - Swedish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_8_0
          type: mozilla-foundation/common_voice_8_0
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 8.72
          - name: Test CER
            type: cer
            value: 3.05
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: speech-recognition-community-v2/dev_data
          type: speech-recognition-community-v2/dev_data
          args: sv
        metrics:
          - name: Test WER
            type: wer
            value: 19.67
          - name: Test CER
            type: cer
            value: 8.94

This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1595
  • Wer: 0.1200

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: 0.00025
  • 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: 0.25
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.0418 5.49 500 3.0176 1.0
1.1819 10.98 1000 0.2562 0.2168
1.0032 16.48 1500 0.1746 0.1546
0.9077 21.97 2000 0.1600 0.1339
0.8687 27.47 2500 0.1647 0.1378
0.8081 32.96 3000 0.1608 0.1353
0.7923 38.46 3500 0.1534 0.1277
0.7349 43.95 4000 0.1546 0.1303
0.7199 49.45 4500 0.1617 0.1277
0.7028 54.94 5000 0.1572 0.1287
0.6912 60.44 5500 0.1560 0.1249
0.6492 65.93 6000 0.1542 0.1260
0.6407 71.43 6500 0.1605 0.1240
0.6222 76.92 7000 0.1577 0.1219
0.6039 82.42 7500 0.1645 0.1249
0.5928 87.91 8000 0.1590 0.1214
0.6022 93.4 8500 0.1597 0.1213
0.5814 98.9 9000 0.1599 0.1199

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0