readme: ---
  language:
  - ba
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-bashkir-cv7_opt
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: ba
      metrics:
      - name: Test WER
        type: wer
        value: 0.04440795062008041
      - name: "Test CER"
        type: "cer"
        value: 0.010491234992390509
  ---
  
  <!-- 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-bashkir-cv7_opt
  
  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 - BA dataset.
  It achieves the following results on the evaluation set:
  - Training Loss: 0.268400
  - Validation Loss: 0.088252
  - WER without LM: 0.085588
  - WER with LM: 0.04440795062008041
  - CER with LM: 0.010491234992390509
  
  
  ## Model description
  
  Trained with this [jupiter notebook](https://drive.google.com/file/d/1KohDXZtKBWXVPZYlsLtqfxJGBzKmTtSh/view?usp=sharing)
  
  
  
  ## Intended uses & limitations
  
  In order to reduce the number of characters, the following letters have been replaced or removed:
  
  - 'я' -> 'йа'
  - 'ю' -> 'йу'
  - 'ё' -> 'йо'
  - 'е' -> 'йэ' for first letter
  - 'е' -> 'э' for other cases
  - 'ъ' -> deleted
  - 'ь' -> deleted
  
  Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model.
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 300
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.2
  - Tokenizers 0.10.3
  