readme: ---
  language:
  - ug
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  - ug
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M Uyghur CV8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: ug
      metrics:
      - name: Test WER
        type: wer
        value: 30.5
      - name: Test CER
        type: cer
        value: 5.8
  ---
  
  <!-- 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 Uyghur 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 - UG dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2026
  - Wer: 0.3248
  
  ## 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 alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), with punctuation removed.
  
  ## 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` and `dev` of common voice official splits were used as training data. The official `test` split was used as validation data as well as for final evaluation.
  
  ## Training procedure
  
  The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 9400 steps (100 epochs).
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - 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: 2000
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.3036        | 5.32  | 500  | 3.2628          | 1.0    |
  | 2.9734        | 10.63 | 1000 | 2.5677          | 0.9980 |
  | 1.3466        | 15.95 | 1500 | 0.4455          | 0.6306 |
  | 1.2424        | 21.28 | 2000 | 0.3603          | 0.5301 |
  | 1.1655        | 26.59 | 2500 | 0.3165          | 0.4740 |
  | 1.1026        | 31.91 | 3000 | 0.2930          | 0.4400 |
  | 1.0655        | 37.23 | 3500 | 0.2675          | 0.4159 |
  | 1.0239        | 42.55 | 4000 | 0.2580          | 0.3913 |
  | 0.9938        | 47.87 | 4500 | 0.2373          | 0.3698 |
  | 0.9655        | 53.19 | 5000 | 0.2379          | 0.3675 |
  | 0.9374        | 58.51 | 5500 | 0.2486          | 0.3795 |
  | 0.9065        | 63.83 | 6000 | 0.2243          | 0.3405 |
  | 0.888         | 69.15 | 6500 | 0.2157          | 0.3277 |
  | 0.8646        | 74.47 | 7000 | 0.2103          | 0.3288 |
  | 0.8602        | 79.78 | 7500 | 0.2088          | 0.3238 |
  | 0.8442        | 85.11 | 8000 | 0.2045          | 0.3266 |
  | 0.8335        | 90.42 | 8500 | 0.2038          | 0.3241 |
  | 0.8288        | 95.74 | 9000 | 0.2024          | 0.3280 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  