readme: ---
  language:
  - gn
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - gn
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-gn-k1
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: gn
      metrics:
      - name: Test WER
        type: wer
        value: 0.711890243902439
      - name: Test CER
        type: cer
        value: 0.13311897106109324
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: gn
      metrics:
      - name: Test WER
        type: wer
        value: NA
      - name: Test CER
        type: cer
        value: NA
  ---
  
  <!-- 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-gn-k1
  
  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 - GN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9220
  - Wer: 0.6631
  
  ### Evaluation Commands
  
  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
  
  python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-gn-k1 --dataset mozilla-foundation/common_voice_8_0 --config gn --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  NA
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.00018
  - train_batch_size: 16
  - eval_batch_size: 8
  - 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: 600
  - num_epochs: 200
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 15.9402       | 8.32   | 100  | 6.9185          | 1.0    |
  | 4.6367        | 16.64  | 200  | 3.7416          | 1.0    |
  | 3.4337        | 24.96  | 300  | 3.2581          | 1.0    |
  | 3.2307        | 33.32  | 400  | 2.8008          | 1.0    |
  | 1.3182        | 41.64  | 500  | 0.8359          | 0.8171 |
  | 0.409         | 49.96  | 600  | 0.8470          | 0.8323 |
  | 0.2573        | 58.32  | 700  | 0.7823          | 0.7576 |
  | 0.1969        | 66.64  | 800  | 0.8306          | 0.7424 |
  | 0.1469        | 74.96  | 900  | 0.9225          | 0.7713 |
  | 0.1172        | 83.32  | 1000 | 0.7903          | 0.6951 |
  | 0.1017        | 91.64  | 1100 | 0.8519          | 0.6921 |
  | 0.0851        | 99.96  | 1200 | 0.8129          | 0.6646 |
  | 0.071         | 108.32 | 1300 | 0.8614          | 0.7043 |
  | 0.061         | 116.64 | 1400 | 0.8414          | 0.6921 |
  | 0.0552        | 124.96 | 1500 | 0.8649          | 0.6905 |
  | 0.0465        | 133.32 | 1600 | 0.8575          | 0.6646 |
  | 0.0381        | 141.64 | 1700 | 0.8802          | 0.6723 |
  | 0.0338        | 149.96 | 1800 | 0.8731          | 0.6845 |
  | 0.0306        | 158.32 | 1900 | 0.9003          | 0.6585 |
  | 0.0236        | 166.64 | 2000 | 0.9408          | 0.6616 |
  | 0.021         | 174.96 | 2100 | 0.9353          | 0.6723 |
  | 0.0212        | 183.32 | 2200 | 0.9269          | 0.6570 |
  | 0.0191        | 191.64 | 2300 | 0.9277          | 0.6662 |
  | 0.0161        | 199.96 | 2400 | 0.9220          | 0.6631 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  