readme: ---
  language:
  - el
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-el
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: el
      metrics:
      - name: Test WER using LM
        type: wer
        value: 20.9
      - name: Test CER using LM
        type: cer
        value: 6.0466
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - EL dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3218
  - Wer: 0.3095
  
  ## Training and evaluation data
  
  Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_el.ipynb"
  
  Test WER without LM
  wer = 31.1294 %
  cer = 7.9509 %
  
  Test WER using LM
  wer = 20.7340 %
  cer = 6.0466 %
  
  How to use eval.py
  ```
  huggingface-cli login #login to huggingface for getting auth token to access the common voice v8
  #running with LM
  !python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-el --dataset mozilla-foundation/common_voice_8_0 --config el --split test
  # running without LM
  !python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-el --dataset mozilla-foundation/common_voice_8_0 --config el --split test --greedy
  ```
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 400
  - num_epochs: 80.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 6.3683        | 8.77  | 500  | 3.1280          | 1.0    |
  | 1.9915        | 17.54 | 1000 | 0.6600          | 0.6444 |
  | 0.6565        | 26.32 | 1500 | 0.4208          | 0.4486 |
  | 0.4484        | 35.09 | 2000 | 0.3885          | 0.4006 |
  | 0.3573        | 43.86 | 2500 | 0.3548          | 0.3626 |
  | 0.3063        | 52.63 | 3000 | 0.3375          | 0.3430 |
  | 0.2751        | 61.4  | 3500 | 0.3359          | 0.3241 |
  | 0.2511        | 70.18 | 4000 | 0.3222          | 0.3108 |
  | 0.2361        | 78.95 | 4500 | 0.3205          | 0.3084 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  