readme: ---
  language:
  - bas
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - bas
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-bas-v1
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: bas
      metrics:
      - name: Test WER
        type: wer
        value: 0.3566497929130234
      - name: Test CER
        type: cer
        value: 0.1102657634184471
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: bas
      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-bas-v1
  
  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 - BAS dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5997
  - Wer: 0.3870
  
  ### 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-bas-v1 --dataset mozilla-foundation/common_voice_8_0 --config bas --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  Basaa (bas) language isn't available in speech-recognition-community-v2/dev_data
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.000111
  - 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: 500
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 12.7076       | 5.26  | 200  | 3.6361          | 1.0    |
  | 3.1657        | 10.52 | 400  | 3.0101          | 1.0    |
  | 2.3987        | 15.78 | 600  | 0.9125          | 0.6774 |
  | 1.0079        | 21.05 | 800  | 0.6477          | 0.5352 |
  | 0.7392        | 26.31 | 1000 | 0.5432          | 0.4929 |
  | 0.6114        | 31.57 | 1200 | 0.5498          | 0.4639 |
  | 0.5222        | 36.83 | 1400 | 0.5220          | 0.4561 |
  | 0.4648        | 42.1  | 1600 | 0.5586          | 0.4289 |
  | 0.4103        | 47.36 | 1800 | 0.5337          | 0.4082 |
  | 0.3692        | 52.62 | 2000 | 0.5421          | 0.3861 |
  | 0.3403        | 57.88 | 2200 | 0.5549          | 0.4096 |
  | 0.3011        | 63.16 | 2400 | 0.5833          | 0.3925 |
  | 0.2932        | 68.42 | 2600 | 0.5674          | 0.3815 |
  | 0.2696        | 73.68 | 2800 | 0.5734          | 0.3889 |
  | 0.2496        | 78.94 | 3000 | 0.5968          | 0.3985 |
  | 0.2289        | 84.21 | 3200 | 0.5888          | 0.3893 |
  | 0.2091        | 89.47 | 3400 | 0.5849          | 0.3852 |
  | 0.2005        | 94.73 | 3600 | 0.5938          | 0.3875 |
  | 0.1876        | 99.99 | 3800 | 0.5997          | 0.3870 |
  
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.2
  - Tokenizers 0.11.0
  