readme: ---
  language:
  - hy
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  - hy
  - hf-asr-leaderboard
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-300m-hy
    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: 13.192818110850899
        name: WER LM
      - type: cer
        value: 2.787051087506323
        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: 22.246048764990867
      - name: Test CER
        type: cer
        value: 7.59406739840239
  ---
  
  <!-- 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-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_3/ - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2293
  - Wer: 0.3333
  - Cer: 0.0602
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 842
  - gradient_accumulation_steps: 2
  - 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: 4000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 3.1471        | 7.02  | 400  | 3.1599          | 1.0    | 1.0    |
  | 1.8691        | 14.04 | 800  | 0.7674          | 0.7361 | 0.1686 |
  | 1.3227        | 21.05 | 1200 | 0.3849          | 0.5336 | 0.1007 |
  | 1.163         | 28.07 | 1600 | 0.3015          | 0.4559 | 0.0823 |
  | 1.0768        | 35.09 | 2000 | 0.2721          | 0.4032 | 0.0728 |
  | 1.0224        | 42.11 | 2400 | 0.2586          | 0.3825 | 0.0691 |
  | 0.9817        | 49.12 | 2800 | 0.2458          | 0.3653 | 0.0653 |
  | 0.941         | 56.14 | 3200 | 0.2306          | 0.3388 | 0.0605 |
  | 0.9235        | 63.16 | 3600 | 0.2315          | 0.3380 | 0.0615 |
  | 0.9141        | 70.18 | 4000 | 0.2293          | 0.3333 | 0.0602 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2
  - Datasets 1.18.4.dev0
  - Tokenizers 0.11.0
  