readme: ---
  language:
  - ky
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M Kyrgiz CV8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: ky
      metrics:
      - name: Test WER (with LM)
        type: wer
        value: 19.01
      - name: Test CER (with LM)
        type: cer
        value: 5.38
      - name: Test WER (no LM)
        type: wer
        value: 31.28
      - name: Test CER (no LM)
        type: cer
        value: 7.66
  ---
  
  <!-- 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. -->
  
  # XLS-R-300M Kyrgiz CV8
  
  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_8_0 - KY dataset.
  It achieves the following results on the validation set:
  - Loss: 0.5497
  - Wer: 0.2945
  - Cer: 0.0791
  
  ## Model description
  
  For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
  
  The model vocabulary consists of the cyrillic alphabet with punctuation removed.
  
  The kenlm language model is built using the text of the train and invalidated corpus splits.
  
  ## Intended uses & limitations
  
  This model is expected to be of some utility for low-fidelity use cases such as:
  - Draft video captions
  - Indexing of recorded broadcasts
  
  The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.
  
  ## Training and evaluation data
  
  The combination of `train`, `dev` and `other` of common voice official splits were used as training data. The half of the official `test` split was used as validation data, as and the full `test` set was used for final evaluation.
  
  ## Training procedure
  
  The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Kyrgiz CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 500 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 8100 steps (300 epochs).
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 300.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
  | 3.1079        | 18.51  | 500  | 2.6795          | 0.9996 | 0.9825 |
  | 0.8506        | 37.04  | 1000 | 0.4323          | 0.3718 | 0.0961 |
  | 0.6821        | 55.55  | 1500 | 0.4105          | 0.3311 | 0.0878 |
  | 0.6091        | 74.07  | 2000 | 0.4281          | 0.3168 | 0.0851 |
  | 0.5429        | 92.58  | 2500 | 0.4525          | 0.3147 | 0.0842 |
  | 0.5063        | 111.11 | 3000 | 0.4619          | 0.3144 | 0.0839 |
  | 0.4661        | 129.62 | 3500 | 0.4660          | 0.3039 | 0.0818 |
  | 0.4353        | 148.15 | 4000 | 0.4695          | 0.3083 | 0.0820 |
  | 0.4048        | 166.65 | 4500 | 0.4909          | 0.3085 | 0.0824 |
  | 0.3852        | 185.18 | 5000 | 0.5074          | 0.3048 | 0.0812 |
  | 0.3567        | 203.69 | 5500 | 0.5111          | 0.3012 | 0.0810 |
  | 0.3451        | 222.22 | 6000 | 0.5225          | 0.2982 | 0.0804 |
  | 0.325         | 240.73 | 6500 | 0.5270          | 0.2955 | 0.0796 |
  | 0.3089        | 259.25 | 7000 | 0.5381          | 0.2929 | 0.0793 |
  | 0.2941        | 277.76 | 7500 | 0.5565          | 0.2923 | 0.0794 |
  | 0.2945        | 296.29 | 8000 | 0.5495          | 0.2951 | 0.0789 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  