readme: ---
  language:
  - pa-IN
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - pa-IN
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-xls-r-300m-pa-IN-r5
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: pa-IN
      metrics:
      - name: Test WER
        type: wer
        value: 0.4186593492747942
      - name: Test CER
        type: cer
        value: 0.13301322550753938
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: pa-IN
      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. -->
  
  # 
  
  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 - PA-IN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8881
  - Wer: 0.4175
  
  ### Evaluation Commands
  
  
  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
  
  python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5 --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  Punjabi 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: 32
  - 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: 2000
  - num_epochs: 200.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 10.695        | 18.52  | 500  | 3.5681          | 1.0    |
  | 3.2718        | 37.04  | 1000 | 2.3081          | 0.9643 |
  | 0.8727        | 55.56  | 1500 | 0.7227          | 0.5147 |
  | 0.3349        | 74.07  | 2000 | 0.7498          | 0.4959 |
  | 0.2134        | 92.59  | 2500 | 0.7779          | 0.4720 |
  | 0.1445        | 111.11 | 3000 | 0.8120          | 0.4594 |
  | 0.1057        | 129.63 | 3500 | 0.8225          | 0.4610 |
  | 0.0826        | 148.15 | 4000 | 0.8307          | 0.4351 |
  | 0.0639        | 166.67 | 4500 | 0.8967          | 0.4316 |
  | 0.0528        | 185.19 | 5000 | 0.8875          | 0.4238 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  