readme: ---
  language:
  - fi
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - fi
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: sammy786/wav2vec2-xlsr-finnish
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: fi
      metrics:
      - name: Test WER
        type: wer
        value: 13.72
      - name: Test CER
        type: cer
        value: 2.35
  ---
  # sammy786/wav2vec2-xlsr-finnish
  
  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 - fi dataset.
  It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
  - Loss: 8.7555
  - Wer: 23.0231
  
  ## 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, invalidated.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  | 4.253700      | 0.881733        | 0.967007 |
  | 400  | 0.864800      | 0.226977        | 0.420836 |
  | 600  | 0.607000      | 0.157473        | 0.343375 |
  | 800  | 0.380200      | 0.145640        | 0.302672 |
  | 1000 | 0.318400      | 0.128028        | 0.293886 |
  | 1200 | 0.261100      | 0.121414        | 0.289941 |
  | 1400 | 0.232300      | 0.113451        | 0.279182 |
  | 1600 | 0.216600      | 0.113649        | 0.282948 |
  | 1800 | 0.202500      | 0.112375        | 0.276134 |
  | 2000 | 0.190000      | 0.105725        | 0.273803 |
  | 2200 | 0.171000      | 0.109715        | 0.270755 |
  | 2400 | 0.156500      | 0.105042        | 0.264300 |
  | 2600 | 0.155600      | 0.108337        | 0.260714 |
  | 2800 | 0.149100      | 0.112435        | 0.263583 |
  | 3000 | 0.145100      | 0.106193        | 0.261969 |
  | 3200 | 0.131700      | 0.102860        | 0.251210 |
  | 3400 | 0.129100      | 0.096058        | 0.246907 |
  | 3600 | 0.121600      | 0.099932        | 0.246369 |
  | 3800 | 0.112000      | 0.099041        | 0.244397 |
  | 4000 | 0.114100      | 0.101566        | 0.242604 |
  | 4200 | 0.111500      | 0.089498        | 0.239197 |
  | 4400 | 0.099800      | 0.092835        | 0.240990 |
  | 4600 | 0.095300      | 0.093518        | 0.238121 |
  | 4800 | 0.094300      | 0.090783        | 0.240631 |
  | 5000 | 0.089000      | 0.094046        | 0.238479 |
  | 5200 | 0.088000      | 0.089342        | 0.235252 |
  | 5400 | 0.083600      | 0.087770        | 0.234535 |
  | 5600 | 0.083600      | 0.088804        | 0.234355 |
  | 5800 | 0.080300      | 0.090168        | 0.231307 |
  | 6000 | 0.078100      | 0.090163        | 0.230949 |
  | 6200 | 0.075600      | 0.088876        | 0.232383 |
  | 6400 | 0.078700      | 0.087235        | 0.232024 |
  | 6600 | 0.074800      | 0.086825        | 0.231486 |
  | 6800 | 0.076400      | 0.087308        | 0.231845 |
  | 7000 | 0.070700      | 0.087695        | 0.230769 |
  | 7200 | 0.075500      | 0.087555        | 0.230231 |
  
  
  ### Framework versions
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.0+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.10.3
  
  #### Evaluation Commands
  
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id sammy786/wav2vec2-xlsr-finnish --dataset mozilla-foundation/common_voice_8_0 --config fi --split test
  ```