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: XLS-R-300M - Bashkir
    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: 24.2
      - name: Test CER
        type: cer
        value: 5.08
  ---
  
  <!-- 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
  
  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:
  - Loss: 0.1892
  - Wer: 0.2421
  
  ## 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: 2000
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.4792        | 0.5   | 2000  | 0.4598          | 0.5404 |
  | 1.449         | 1.0   | 4000  | 0.4650          | 0.5610 |
  | 1.3742        | 1.49  | 6000  | 0.4001          | 0.4977 |
  | 1.3375        | 1.99  | 8000  | 0.3916          | 0.4894 |
  | 1.2961        | 2.49  | 10000 | 0.3641          | 0.4569 |
  | 1.2714        | 2.99  | 12000 | 0.3491          | 0.4488 |
  | 1.2399        | 3.48  | 14000 | 0.3151          | 0.3986 |
  | 1.2067        | 3.98  | 16000 | 0.3081          | 0.3923 |
  | 1.1842        | 4.48  | 18000 | 0.2875          | 0.3703 |
  | 1.1644        | 4.98  | 20000 | 0.2840          | 0.3670 |
  | 1.161         | 5.48  | 22000 | 0.2790          | 0.3597 |
  | 1.1303        | 5.97  | 24000 | 0.2552          | 0.3272 |
  | 1.0874        | 6.47  | 26000 | 0.2405          | 0.3142 |
  | 1.0613        | 6.97  | 28000 | 0.2352          | 0.3055 |
  | 1.0498        | 7.47  | 30000 | 0.2249          | 0.2910 |
  | 1.021         | 7.96  | 32000 | 0.2118          | 0.2752 |
  | 1.0002        | 8.46  | 34000 | 0.2046          | 0.2662 |
  | 0.9762        | 8.96  | 36000 | 0.1969          | 0.2530 |
  | 0.9568        | 9.46  | 38000 | 0.1917          | 0.2449 |
  | 0.953         | 9.96  | 40000 | 0.1893          | 0.2425 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  