readme: ---
  language:
  - uk
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - uk
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: wav2vec2-xls-r-300m-uk-with-lm
    results:
    - task: 
        name: Automatic Speech Recognition 
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: uk
      metrics:
         - name: Test WER
           type: wer
           value: 26.47
         - name: Test CER
           type: cer
           value: 2.90
  ---
  
  
  # Ukrainian STT model (with Language Model)
  
  🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
  
  ⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
  
  - Have a look on an updated 300m model: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm
  - Have a look on a better model with more parameters: https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-lm
  
  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 - UK dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3015
  - Wer: 0.3377
  - Cer: 0.0708
  
  The above results present evaluation without the language model.
  
  ## Model description
  
  On 100 test example the model shows the following results:
  
  Without LM:
  
  - WER: 0.2647
  - CER: 0.0469
  
  With LM:
  
  - WER: 0.1568
  - CER: 0.0289
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 20
  - total_train_batch_size: 160
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 3.0255        | 7.93  | 500  | 2.5514          | 0.9921 | 0.9047 |
  | 1.3809        | 15.86 | 1000 | 0.4065          | 0.5361 | 0.1201 |
  | 1.2355        | 23.8  | 1500 | 0.3474          | 0.4618 | 0.1033 |
  | 1.1956        | 31.74 | 2000 | 0.3617          | 0.4580 | 0.1005 |
  | 1.1416        | 39.67 | 2500 | 0.3182          | 0.4074 | 0.0891 |
  | 1.0996        | 47.61 | 3000 | 0.3166          | 0.3985 | 0.0875 |
  | 1.0427        | 55.55 | 3500 | 0.3116          | 0.3835 | 0.0828 |
  | 0.9961        | 63.49 | 4000 | 0.3137          | 0.3757 | 0.0807 |
  | 0.9575        | 71.42 | 4500 | 0.2992          | 0.3632 | 0.0771 |
  | 0.9154        | 79.36 | 5000 | 0.3015          | 0.3502 | 0.0740 |
  | 0.8994        | 87.3  | 5500 | 0.3004          | 0.3425 | 0.0723 |
  | 0.871         | 95.24 | 6000 | 0.3016          | 0.3394 | 0.0713 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.18.1.dev0
  - Tokenizers 0.11.0
  