readme: ---
  language:
  - hy-AM
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - Armenian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: hy-AM
      metrics:
      - name: Test WER
        type: wer
        value: 101.627
      - name: Test CER
        type: cer
        value: 158.767
  ---
  
  <!-- 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. -->
  
  # wav2vec2-large-xls-r-300m-armenian
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HY-AM dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9669
  - Wer: 0.6942
  
  ## 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: 0.0003
  - 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
  - num_epochs: 200.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 1.7294        | 27.78  | 500  | 0.8540          | 0.9944 |
  | 0.8863        | 55.56  | 1000 | 0.7282          | 0.7312 |
  | 0.5789        | 83.33  | 1500 | 0.8178          | 0.8102 |
  | 0.3899        | 111.11 | 2000 | 0.8034          | 0.7701 |
  | 0.2869        | 138.89 | 2500 | 0.9061          | 0.6999 |
  | 0.1934        | 166.67 | 3000 | 0.9400          | 0.7105 |
  | 0.1551        | 194.44 | 3500 | 0.9667          | 0.6955 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  