readme: ---
  language:
  - lt
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - lt
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: sammy786/wav2vec2-xlsr-lithuanian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: lt
      metrics:
      - name: Test WER
        type: wer
        value: 14.67
      - name: Test CER
        type: cer
        value: 2.77
  ---
  # sammy786/wav2vec2-xlsr-lithuanian
  
  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_8_0 - lt dataset.
  It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
  - Loss: 13.1811
  - Wer: 24.2570
  
  ## Model description
  "facebook/wav2vec2-xls-r-1b" was finetuned.
  
  ## Intended uses & limitations
  More information needed
  ## Training and evaluation data
  Training data - 
  Common voice Finnish train.tsv, dev.tsv and other.tsv
  
  ## Training procedure
  For creating the train dataset, all possible datasets were appended and 90-10 split was used. 
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  - learning_rate: 0.000045637994662983496
  - train_batch_size: 8
  - eval_batch_size: 16
  - seed: 13
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine_with_restarts
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 40
  - mixed_precision_training: Native AMP
  
  
  ### Training results
  
  
  | Step  | Training Loss | Validation Loss | Wer      |
  |:-----:|:-------------:|:---------------:|:--------:|
  | 200   | 5.718700      | 2.897032        | 1.000000 |
  | 400   | 1.340000      | 0.309548        | 0.507284 |
  | 600   | 0.799100      | 0.220205        | 0.402098 |
  | 800   | 0.494400      | 0.185093        | 0.352855 |
  | 1000  | 0.370800      | 0.165869        | 0.334207 |
  | 1200  | 0.312500      | 0.159801        | 0.324009 |
  | 1400  | 0.276100      | 0.148066        | 0.321678 |
  | 1600  | 0.250100      | 0.153748        | 0.311626 |
  | 1800  | 0.226400      | 0.147437        | 0.302885 |
  | 2000  | 0.206900      | 0.141176        | 0.296037 |
  | 2200  | 0.189900      | 0.142161        | 0.288170 |
  | 2400  | 0.192100      | 0.138029        | 0.286568 |
  | 2600  | 0.175600      | 0.139496        | 0.283654 |
  | 2800  | 0.156900      | 0.138609        | 0.283217 |
  | 3000  | 0.149400      | 0.140468        | 0.281906 |
  | 3200  | 0.144600      | 0.132472        | 0.278263 |
  | 3400  | 0.144100      | 0.141028        | 0.277535 |
  | 3600  | 0.133000      | 0.134287        | 0.275495 |
  | 3800  | 0.126600      | 0.149136        | 0.277681 |
  | 4000  | 0.123500      | 0.132180        | 0.266463 |
  | 4200  | 0.113000      | 0.137942        | 0.268211 |
  | 4400  | 0.111700      | 0.140038        | 0.272873 |
  | 4600  | 0.108600      | 0.136756        | 0.264132 |
  | 4800  | 0.103600      | 0.137541        | 0.263403 |
  | 5000  | 0.098000      | 0.140435        | 0.264860 |
  | 5200  | 0.095800      | 0.136950        | 0.262383 |
  | 5400  | 0.094000      | 0.128214        | 0.263986 |
  | 5600  | 0.085300      | 0.125024        | 0.259761 |
  | 5800  | 0.078900      | 0.128575        | 0.260198 |
  | 6000  | 0.083300      | 0.135496        | 0.258887 |
  | 6200  | 0.078800      | 0.131706        | 0.259178 |
  | 6400  | 0.073800      | 0.128451        | 0.255390 |
  | 6600  | 0.072600      | 0.131245        | 0.252768 |
  | 6800  | 0.073300      | 0.131525        | 0.249417 |
  | 7000  | 0.069000      | 0.128627        | 0.255536 |
  | 7200  | 0.064400      | 0.127767        | 0.250583 |
  | 7400  | 0.065400      | 0.129557        | 0.247815 |
  | 7600  | 0.061200      | 0.129734        | 0.250146 |
  | 7800  | 0.059100      | 0.135124        | 0.249709 |
  | 8000  | 0.057000      | 0.132850        | 0.249126 |
  | 8200  | 0.056100      | 0.128827        | 0.248252 |
  | 8400  | 0.056400      | 0.130229        | 0.246795 |
  | 8600  | 0.052800      | 0.128939        | 0.245775 |
  | 8800  | 0.051100      | 0.131892        | 0.248543 |
  | 9000  | 0.052900      | 0.132062        | 0.244464 |
  | 9200  | 0.048200      | 0.130988        | 0.244172 |
  | 9400  | 0.047700      | 0.131811        | 0.242570 |
  | 9600  | 0.050000      | 0.133832        | 0.245484 |
  | 9800  | 0.047500      | 0.134340        | 0.243881 |
  | 10000 | 0.048400      | 0.133388        | 0.243590 |
  | 10200 | 0.047800      | 0.132729        | 0.244464 |
  | 10400 | 0.049000      | 0.131695        | 0.245047 |
  | 10600 | 0.044400      | 0.132154        | 0.245484 |
  | 10800 | 0.050100      | 0.131575        | 0.245192 |
  | 11000 | 0.047700      | 0.131211        | 0.245192 |
  | 11200 | 0.046000      | 0.131293        | 0.24