readme: ---
  language:
  - mn
  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-mn
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: mn
      metrics:
      - name: Test WER using LM
        type: wer
        value: 31.3919
      - name: Test CER using LM
        type: cer
        value: 10.2565
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: mn
      metrics:
      - name: Test WER
        type: wer
        value: 65.26
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: mn
      metrics:
      - name: Test WER
        type: wer
        value: 63.09
  ---
  
  <!-- 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 - MN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5502
  - Wer: 0.4042
  
  ## Training and evaluation data
  
  Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_mn.ipynb"
  
  
  Test WER without LM
  wer = 58.2171 %
  cer = 16.0670 %
  
  Test WER using 
  wer = 31.3919 %
  cer = 10.2565 %
  
  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-mn --dataset mozilla-foundation/common_voice_8_0 --config mn --split test
  
  # running without LM
  python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-mn --dataset mozilla-foundation/common_voice_8_0 --config mn --split test --greedy
  ```
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - 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: 500
  - num_epochs: 40.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 6.35  | 400  | 0.9380          | 0.7902 |
  | 3.2674        | 12.7  | 800  | 0.5794          | 0.5309 |
  | 0.7531        | 19.05 | 1200 | 0.5749          | 0.4815 |
  | 0.5382        | 25.4  | 1600 | 0.5530          | 0.4447 |
  | 0.4293        | 31.75 | 2000 | 0.5709          | 0.4237 |
  | 0.4293        | 38.1  | 2400 | 0.5476          | 0.4059 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  