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xls-r-300m-sv-cv8

Automatic Speech Recognition Transformers PyTorch TensorBoard
wav2vec2 mozilla-foundation/common_voice_8_0 generated_from_trainer robust-speech-event hf-asr-leaderboard Eval Results Inference Endpoints
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metadata
language:
  - sv-SE
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: XLS-R-300M - Swedish - CV8
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 17.1
          - name: Test CER
            type: cer
            value: 5.7
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: sv
        metrics:
          - name: Test WER
            type: wer
            value: 26.92
          - name: Test CER
            type: cer
            value: 12.53

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:

Without LM:

  • Wer: 0.2465
  • Cer: 0.0717

With LM:

  • Wer: 0.1710
  • Cer: 0.0569

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.3224 1.37 500 3.2676 1.0
2.9319 2.74 1000 2.9287 1.0000
2.1173 4.11 1500 1.1478 0.8788
1.6973 5.48 2000 0.6749 0.6547
1.5865 6.85 2500 0.5500 0.5634
1.5094 8.22 3000 0.4840 0.5430
1.4644 9.59 3500 0.4844 0.4142
1.4061 10.96 4000 0.4356 0.3808
1.3584 12.33 4500 0.4192 0.3698
1.3438 13.7 5000 0.3980 0.3584
1.3332 15.07 5500 0.3896 0.3572
1.3025 16.44 6000 0.3835 0.3487
1.2979 17.81 6500 0.3781 0.3417
1.2736 19.18 7000 0.3734 0.3270
1.2415 20.55 7500 0.3637 0.3316
1.2255 21.92 8000 0.3546 0.3147
1.2193 23.29 8500 0.3524 0.3196
1.2104 24.66 9000 0.3403 0.3097
1.1965 26.03 9500 0.3508 0.3093
1.1976 27.4 10000 0.3419 0.3071
1.182 28.77 10500 0.3364 0.2963
1.158 30.14 11000 0.3338 0.2932
1.1414 31.51 11500 0.3376 0.2940
1.1402 32.88 12000 0.3370 0.2891
1.1213 34.25 12500 0.3201 0.2874
1.1207 35.62 13000 0.3261 0.2826
1.1074 36.98 13500 0.3117 0.2786
1.0818 38.36 14000 0.3194 0.2776
1.0889 39.73 14500 0.3188 0.2738
1.0672 41.1 15000 0.3196 0.2773
1.0838 42.47 15500 0.3130 0.2739
1.0553 43.83 16000 0.3165 0.2704
1.0786 45.21 16500 0.3108 0.2706
1.0546 46.57 17000 0.3102 0.2677
1.0425 47.94 17500 0.3115 0.2679
1.0398 49.31 18000 0.3131 0.2666

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu113
  • Datasets 1.18.1.dev0
  • Tokenizers 0.10.3