readme: ---
  language:
  - dv
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - dv
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: sammy786/wav2vec2-xlsr-dhivehi
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: dv
      metrics:
      - name: Test WER
        type: wer
        value: 26.91
      - name: Test CER
        type: cer
        value: 4.02
  ---
  # sammy786/wav2vec2-xlsr-dhivehi
  
  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 - dv dataset.
  It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
  - Loss: 14.86
  - Wer: 29.32
  
  ## 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: 30
  - mixed_precision_training: Native AMP
  
  
  ### Training results
  
  | Step  | Training Loss | Validation Loss | Wer      |
  |-------|---------------|-----------------|----------|
  | 200   | 4.883800      | 3.190218        | 1.000000 |
  | 400   | 1.600100      | 0.497887        | 0.726159 |
  | 600   | 0.928500      | 0.358781        | 0.603892 |
  | 800   | 0.867900      | 0.309132        | 0.570786 |
  | 1000  | 0.743100      | 0.309116        | 0.552954 |
  | 1200  | 0.725100      | 0.266839        | 0.538378 |
  | 1400  | 0.786200      | 0.259797        | 0.535897 |
  | 1600  | 0.655700      | 0.245691        | 0.517290 |
  | 1800  | 0.650500      | 0.246957        | 0.516204 |
  | 2000  | 0.685500      | 0.234808        | 0.516204 |
  | 2200  | 0.487100      | 0.228409        | 0.507753 |
  | 2400  | 0.401300      | 0.221087        | 0.495968 |
  | 2600  | 0.359300      | 0.212476        | 0.489301 |
  | 2800  | 0.347300      | 0.204848        | 0.487750 |
  | 3000  | 0.327000      | 0.203163        | 0.478756 |
  | 3200  | 0.337100      | 0.210235        | 0.487595 |
  | 3400  | 0.308900      | 0.201471        | 0.491316 |
  | 3600  | 0.292600      | 0.192437        | 0.476120 |
  | 3800  | 0.289600      | 0.198398        | 0.468445 |
  | 4000  | 0.290200      | 0.193484        | 0.467204 |
  | 4200  | 0.272600      | 0.193999        | 0.470150 |
  | 4400  | 0.266700      | 0.187384        | 0.460769 |
  | 4600  | 0.253800      | 0.187279        | 0.476663 |
  | 4800  | 0.266400      | 0.197395        | 0.466817 |
  | 5000  | 0.258000      | 0.188920        | 0.456660 |
  | 5200  | 0.237200      | 0.180770        | 0.457358 |
  | 5400  | 0.237900      | 0.178149        | 0.448287 |
  | 5600  | 0.232600      | 0.179827        | 0.461002 |
  | 5800  | 0.228500      | 0.182142        | 0.445185 |
  | 6000  | 0.221000      | 0.173619        | 0.440688 |
  | 6200  | 0.219500      | 0.172291        | 0.442859 |
  | 6400  | 0.219400      | 0.173339        | 0.430609 |
  | 6600  | 0.201900      | 0.177552        | 0.426423 |
  | 6800  | 0.199000      | 0.173157        | 0.429834 |
  | 7000  | 0.200000      | 0.166503        | 0.423709 |
  | 7200  | 0.194600      | 0.171812        | 0.429834 |
  | 7400  | 0.192100      | 0.164989        | 0.420530 |
  | 7600  | 0.185000      | 0.168355        | 0.418825 |
  | 7800  | 0.175100      | 0.168128        | 0.419290 |
  | 8000  | 0.173500      | 0.167959        | 0.424950 |
  | 8200  | 0.172200      | 0.173643        | 0.414793 |
  | 8400  | 0.164200      | 0.167020        | 0.406342 |
  | 8600  | 0.170800      | 0.168050        | 0.405334 |
  | 8800  | 0.157900      | 0.164290        | 0.396573 |
  | 9000  | 0.159900      | 0.163188        | 0.397426 |
  | 9200  | 0.151700      | 0.164370        | 0.390991 |
  | 9400  | 0.146600      | 0.165053        | 0.392852 |
  | 9600  | 0.142200      | 0.164939        | 0.391844 |
  | 9800  | 0.148300      | 0.164422        | 0.385719 |
  | 10000 | 0.136200      | 0.166569        | 0.385951 |
  | 10200 | 0.140700      | 0.161377        | 0.379594 |
  | 10400 | 0.133300      | 0.165194        | 0.378276 |
  | 10600 | 0.131300      | 0.164328        | 0.369205 |
  | 10800 | 0.135500      | 0.160254        | 0.373236 |
  | 11000 | 0.121100      | 0.163522        | 0.372693 |
  
  
  ### Framework versions
  - Transformers 4.16.0.dev0
  - Pytor