readme: ---
  language:
  - tt
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  - tt
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: sammy786/wav2vec2-xlsr-tatar
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: tt
      metrics:
      - name: Test WER
        type: wer
        value: 16.87
      - name: Test CER
        type: cer
        value: 3.64
  ---
  # sammy786/wav2vec2-xlsr-tatar
  
  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 - tt dataset.
  It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
  - Loss: 7.66
  - Wer: 7.08
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 13
  - gradient_accumulation_steps: 2
  - 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   | 4.849400      | 1.874908        | 0.995232 |
  | 400   | 1.105700      | 0.257292        | 0.367658 |
  | 600   | 0.723000      | 0.181150        | 0.250513 |
  | 800   | 0.660600      | 0.167009        | 0.226078 |
  | 1000  | 0.568000      | 0.135090        | 0.177339 |
  | 1200  | 0.721200      | 0.117469        | 0.166413 |
  | 1400  | 0.416300      | 0.115142        | 0.153765 |
  | 1600  | 0.346000      | 0.105782        | 0.153963 |
  | 1800  | 0.279700      | 0.102452        | 0.146149 |
  | 2000  | 0.273800      | 0.095818        | 0.128468 |
  | 2200  | 0.252900      | 0.102302        | 0.133766 |
  | 2400  | 0.255100      | 0.096592        | 0.121316 |
  | 2600  | 0.229600      | 0.091263        | 0.124561 |
  | 2800  | 0.213900      | 0.097748        | 0.125687 |
  | 3000  | 0.210700      | 0.091244        | 0.125422 |
  | 3200  | 0.202600      | 0.084076        | 0.106284 |
  | 3400  | 0.200900      | 0.093809        | 0.113238 |
  | 3600  | 0.192700      | 0.082918        | 0.108139 |
  | 3800  | 0.182000      | 0.084487        | 0.103371 |
  | 4000  | 0.167700      | 0.091847        | 0.104960 |
  | 4200  | 0.183700      | 0.085223        | 0.103040 |
  | 4400  | 0.174400      | 0.083862        | 0.100589 |
  | 4600  | 0.163100      | 0.086493        | 0.099728 |
  | 4800  | 0.162000      | 0.081734        | 0.097543 |
  | 5000  | 0.153600      | 0.077223        | 0.092974 |
  | 5200  | 0.153700      | 0.086217        | 0.090789 |
  | 5400  | 0.140200      | 0.093256        | 0.100457 |
  | 5600  | 0.142900      | 0.086903        | 0.097742 |
  | 5800  | 0.131400      | 0.083068        | 0.095225 |
  | 6000  | 0.126000      | 0.086642        | 0.091252 |
  | 6200  | 0.135300      | 0.083387        | 0.091186 |
  | 6400  | 0.126100      | 0.076479        | 0.086352 |
  | 6600  | 0.127100      | 0.077868        | 0.086153 |
  | 6800  | 0.118000      | 0.083878        | 0.087676 |
  | 7000  | 0.117600      | 0.085779        | 0.091054 |
  | 7200  | 0.113600      | 0.084197        | 0.084233 |
  | 7400  | 0.112000      | 0.078688        | 0.081319 |
  | 7600  | 0.110200      | 0.082534        | 0.086087 |
  | 7800  | 0.106400      | 0.077245        | 0.080988 |
  | 8000  | 0.102300      | 0.077497        | 0.079332 |
  | 8200  | 0.109500      | 0.079083        | 0.088339 |
  | 8400  | 0.095900      | 0.079721        | 0.077809 |
  | 8600  | 0.094700      | 0.079078        | 0.079730 |
  | 8800  | 0.097400      | 0.078785        | 0.079200 |
  | 9000  | 0.093200      | 0.077445        | 0.077015 |
  | 9200  | 0.088700      | 0.078207        | 0.076617 |
  | 9400  | 0.087200      | 0.078982        | 0.076485 |
  | 9600  | 0.089900      | 0.081209        | 0.076021 |
  | 9800  | 0.081900      | 0.078158        | 0.075757 |
  | 10000 | 0.080200      | 0.078074        | 0.074498 |
  | 10200 | 0.085000      | 0.078830        | 0.073373 |
  | 10400 | 0.080400      | 0.078144        | 0.073373 |
  | 10600 | 0.078200      | 0.077163        | 0.073902 |
  | 10800 | 0.080900      | 0.076394        | 0.072446 |
  | 11000 | 0.080700      | 0.075955        | 0.071585 |
  | 11200 | 0.076800      | 0.077031        | 0.072313 |
  | 11400 