readme: ---
  language:
  - uk
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  - uk
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: wav2vec2-xls-r-1b-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: 14.62
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: uk
      metrics:
      - name: Test WER
        type: wer
        value: 48.72
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: uk
      metrics:
      - name: Test WER
        type: wer
        value: 40.66
  ---
  
  # 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
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UK dataset.
  
  It achieves the following results on the evaluation set without the language model:
  
  - Loss: 0.1875
  - Wer: 0.2033
  - Cer: 0.0384
  
  
  ## Model description
  
  On 100 test example the model shows the following results:
  
  Without LM:
  
  - WER: 0.1862
  - CER: 0.0277
  
  With LM:
  
  - WER: 0.1218
  - CER: 0.0190
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 1.2815        | 7.93  | 500  | 0.3536          | 0.4753 | 0.1009 |
  | 1.0869        | 15.86 | 1000 | 0.2317          | 0.3111 | 0.0614 |
  | 0.9984        | 23.8  | 1500 | 0.2022          | 0.2676 | 0.0521 |
  | 0.975         | 31.74 | 2000 | 0.1948          | 0.2469 | 0.0487 |
  | 0.9306        | 39.67 | 2500 | 0.1916          | 0.2377 | 0.0464 |
  | 0.8868        | 47.61 | 3000 | 0.1903          | 0.2257 | 0.0439 |
  | 0.8424        | 55.55 | 3500 | 0.1786          | 0.2206 | 0.0423 |
  | 0.8126        | 63.49 | 4000 | 0.1849          | 0.2160 | 0.0416 |
  | 0.7901        | 71.42 | 4500 | 0.1869          | 0.2138 | 0.0413 |
  | 0.7671        | 79.36 | 5000 | 0.1855          | 0.2075 | 0.0394 |
  | 0.7467        | 87.3  | 5500 | 0.1884          | 0.2049 | 0.0389 |
  | 0.731         | 95.24 | 6000 | 0.1877          | 0.2060 | 0.0387 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.18.1.dev0
  - Tokenizers 0.11.0
  
  #### Evaluation Commands
  
  1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
  
  ```bash
  python eval.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --dataset mozilla-foundation/common_voice_7_0 --config uk --split test
  ```
  
  ### Eval results on Common Voice 7 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | 21.52 | 14.62 |
  