readme: ---
  language:
  - ia
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-ia
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: ia
      metrics:
      - name: Test WER using LM
        type: wer
        value: 8.6074
      - name: Test CER using LM
        type: cer
        value: 2.4147
  ---
  
  <!-- 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. -->
  
  # wav2vec2-large-xls-r-300m-ia
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1452
  - Wer: 0.1253
  
  ## Training Procedure
  
  Training is conducted in Google Colab, the training notebook provided in the repo
  
  ## Training and evaluation data
  
  Language Model Created from texts from processed sentence in train + validation split of dataset (common voice 8.0 for Interlingua)
  Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_ia.ipynb"
  
  Test WER without LM
  wer = 20.1776 %
  cer = 4.7205 %
  
  Test WER using 
  wer = 8.6074 %
  cer = 2.4147 %
  
  evaluation using 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-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test
  
  # running without LM
  python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test --greedy
  ```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 16
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 400
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 7.432         | 1.87  | 400  | 2.9636          | 1.0    |
  | 2.6922        | 3.74  | 800  | 2.2111          | 0.9977 |
  | 1.2581        | 5.61  | 1200 | 0.4864          | 0.4028 |
  | 0.6232        | 7.48  | 1600 | 0.2807          | 0.2413 |
  | 0.4479        | 9.35  | 2000 | 0.2219          | 0.1885 |
  | 0.3654        | 11.21 | 2400 | 0.1886          | 0.1606 |
  | 0.323         | 13.08 | 2800 | 0.1716          | 0.1444 |
  | 0.2935        | 14.95 | 3200 | 0.1687          | 0.1443 |
  | 0.2707        | 16.82 | 3600 | 0.1632          | 0.1382 |
  | 0.2559        | 18.69 | 4000 | 0.1507          | 0.1337 |
  | 0.2433        | 20.56 | 4400 | 0.1572          | 0.1358 |
  | 0.2338        | 22.43 | 4800 | 0.1489          | 0.1305 |
  | 0.2258        | 24.3  | 5200 | 0.1485          | 0.1278 |
  | 0.2218        | 26.17 | 5600 | 0.1470          | 0.1272 |
  | 0.2169        | 28.04 | 6000 | 0.1470          | 0.1270 |
  | 0.2117        | 29.91 | 6400 | 0.1452          | 0.1253 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  