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xls-r-et-V-3

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Estonian
wav2vec2
mozilla-foundation/common_voice_8_0
robust-speech-event
Generated from Trainer
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Metrics Community
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metadata
language:
  - et
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - et
  - robust-speech-event
  - generated_from_trainer
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: XLS-R-1B - Estonian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: et
        metrics:
          - name: Test WER
            type: wer
            value: 52.47
          - name: Test CER
            type: cer
            value: 12.59
      - 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: 61.02
          - name: Test CER
            type: cer
            value: 21.08
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: et
        metrics:
          - name: Test WER
            type: wer
            value: 59.23
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: et
        metrics:
          - name: Test WER
            type: wer
            value: 69.08

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ET dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8824
  • Wer: 0.5246

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: 7e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 25000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.0296 2.79 500 0.8106 0.8029
0.9339 5.59 1000 0.7419 0.7932
0.8925 8.38 1500 0.7137 0.7706
0.8484 11.17 2000 0.7020 0.7677
0.7521 13.97 2500 0.7043 0.7375
0.719 16.76 3000 0.6617 0.7428
0.656 19.55 3500 0.6388 0.7202
0.6085 22.35 4000 0.6211 0.6960
0.5598 25.14 4500 0.6132 0.6644
0.4969 27.93 5000 0.6065 0.6521
0.4638 30.73 5500 0.6978 0.6577
0.4385 33.52 6000 0.5994 0.6565
0.396 36.31 6500 0.6170 0.6258
0.3861 39.11 7000 0.6486 0.6217
0.3602 41.9 7500 0.6508 0.6115
0.3251 44.69 8000 0.7022 0.6253
0.3197 47.49 8500 0.7706 0.6215
0.3013 50.28 9000 0.6419 0.5999
0.2813 53.07 9500 0.6908 0.5959
0.286 55.87 10000 0.7151 0.5916
0.2645 58.66 10500 0.7181 0.5860
0.2535 61.45 11000 0.7877 0.5979
0.247 64.25 11500 0.8199 0.6129
0.2412 67.04 12000 0.7679 0.5884
0.2404 69.83 12500 0.7266 0.5816
0.2293 72.63 13000 0.7928 0.5795
0.2176 75.42 13500 0.7916 0.5846
0.2143 78.21 14000 0.7954 0.5765
0.2185 81.01 14500 0.8317 0.5907
0.2057 83.8 15000 0.8016 0.5851
0.1895 86.59 15500 0.8080 0.5679
0.1883 89.39 16000 0.8103 0.5712
0.1802 92.18 16500 0.8383 0.5644
0.1826 94.97 17000 0.8799 0.5657
0.1717 97.77 17500 0.8620 0.5709
0.1701 100.56 18000 0.8717 0.5662
0.1623 103.35 18500 0.8534 0.5594
0.158 106.15 19000 0.8595 0.5546
0.1508 108.94 19500 0.8574 0.5545
0.142 111.73 20000 0.8671 0.5537
0.1395 114.53 20500 0.8436 0.5525
0.1373 117.32 21000 0.8808 0.5482
0.1338 120.11 21500 0.9024 0.5418
0.1278 122.91 22000 0.9143 0.5409
0.1207 125.7 22500 0.8917 0.5358
0.1203 128.49 23000 0.9041 0.5341
0.1083 131.28 23500 0.8884 0.5341
0.1147 134.08 24000 0.8910 0.5255
0.1129 136.87 24500 0.8826 0.5241
0.1029 139.66 25000 0.8824 0.5246

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0