readme: ---
  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 card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/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
  