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wav2vec2-xls-r-1b-uk

Automatic Speech Recognition Transformers PyTorch TensorBoard
Ukrainian wav2vec2 generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_8_0 robust-speech-event Eval Results Inference Endpoints
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metadata
language:
  - uk
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-1b-hy
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice uk
          args: uk
        metrics:
          - type: wer
            value: 10.406342913776015
            name: WER LM
          - type: cer
            value: 2.0387492208601703
            name: CER LM
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 40.57
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 28.95

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/UK/COMPOSED_DATASET/ - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1092
  • Wer: 0.1752
  • Cer: 0.0323

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 12000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.7005 1.61 500 0.4082 0.5584 0.1164
1.1555 3.22 1000 0.2020 0.2953 0.0557
1.0927 4.82 1500 0.1708 0.2584 0.0480
1.0707 6.43 2000 0.1563 0.2405 0.0450
1.0728 8.04 2500 0.1620 0.2442 0.0463
1.0268 9.65 3000 0.1588 0.2378 0.0458
1.0328 11.25 3500 0.1466 0.2352 0.0442
1.0249 12.86 4000 0.1552 0.2341 0.0449
1.016 14.47 4500 0.1602 0.2435 0.0473
1.0164 16.08 5000 0.1491 0.2337 0.0444
0.9935 17.68 5500 0.1539 0.2373 0.0458
0.9626 19.29 6000 0.1458 0.2305 0.0434
0.9505 20.9 6500 0.1368 0.2157 0.0407
0.9389 22.51 7000 0.1437 0.2231 0.0426
0.9129 24.12 7500 0.1313 0.2076 0.0394
0.9118 25.72 8000 0.1292 0.2040 0.0384
0.8848 27.33 8500 0.1299 0.2028 0.0384
0.8667 28.94 9000 0.1228 0.1945 0.0367
0.8641 30.55 9500 0.1223 0.1939 0.0364
0.8516 32.15 10000 0.1184 0.1876 0.0349
0.8379 33.76 10500 0.1137 0.1821 0.0338
0.8235 35.37 11000 0.1127 0.1779 0.0331
0.8112 36.98 11500 0.1103 0.1766 0.0327
0.8069 38.59 12000 0.1092 0.1752 0.0323

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2
  • Datasets 1.18.4.dev0
  • Tokenizers 0.11.0