readme: ---
  language:
  - hy
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - hy
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-1b-hy-cv
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice hy-AM
        args: hy-AM
      metrics:
      - type: wer
        value: 10.811865729898516
        name: WER LM
      - type: cer
        value: 2.2205361659079412
        name: CER LM
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: hy
      metrics:
      - name: Test WER
        type: wer
        value: 18.219363037089988
      - name: Test CER
        type: cer
        value: 7.075988867335752
  ---
  
  <!-- 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/HY/NOIZY_STUDENT_4/ - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1693
  - Wer: 0.2373
  - Cer: 0.0429
  
  ## 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: 842
  - 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: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 1.255         | 7.24  | 500  | 0.2978          | 0.4294 | 0.0758 |
  | 1.0058        | 14.49 | 1000 | 0.1883          | 0.2838 | 0.0483 |
  | 0.9371        | 21.73 | 1500 | 0.1813          | 0.2627 | 0.0457 |
  | 0.8999        | 28.98 | 2000 | 0.1693          | 0.2373 | 0.0429 |
  | 0.8814        | 36.23 | 2500 | 0.1760          | 0.2420 | 0.0435 |
  | 0.8364        | 43.47 | 3000 | 0.1765          | 0.2416 | 0.0419 |
  | 0.8019        | 50.72 | 3500 | 0.1758          | 0.2311 | 0.0398 |
  | 0.7665        | 57.96 | 4000 | 0.1745          | 0.2240 | 0.0399 |
  | 0.7376        | 65.22 | 4500 | 0.1717          | 0.2190 | 0.0385 |
  | 0.716         | 72.46 | 5000 | 0.1700          | 0.2147 | 0.0382 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2
  - Datasets 1.18.4.dev0
  - Tokenizers 0.11.0
  