readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - evanarlian/common_voice_11_0_id_filtered
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-113m-id
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: evanarlian/common_voice_11_0_id_filtered
        type: evanarlian/common_voice_11_0_id_filtered
      metrics:
      - name: Wer
        type: wer
        value: 0.4274974633336408
  ---
  
  <!-- 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-xls-r-113m-id
  
  This model is a fine-tuned version of [evanarlian/distil-wav2vec2-xls-r-113m-id](https://huggingface.co/evanarlian/distil-wav2vec2-xls-r-113m-id) on the evanarlian/common_voice_11_0_id_filtered dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4804
  - Wer: 0.4275
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0002
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 3
  - total_train_batch_size: 24
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.3
  - num_epochs: 30.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.2694        | 0.92  | 1000  | 2.9168          | 1.0000 |
  | 2.2449        | 1.84  | 2000  | 1.5711          | 0.9901 |
  | 1.2118        | 2.75  | 3000  | 1.0133          | 0.9261 |
  | 0.971         | 3.67  | 4000  | 0.8860          | 0.8743 |
  | 0.8472        | 4.59  | 5000  | 0.7562          | 0.8180 |
  | 0.7436        | 5.51  | 6000  | 0.6800          | 0.7505 |
  | 0.6603        | 6.43  | 7000  | 0.6275          | 0.7023 |
  | 0.5961        | 7.35  | 8000  | 0.5913          | 0.6589 |
  | 0.5458        | 8.26  | 9000  | 0.5605          | 0.6358 |
  | 0.5113        | 9.18  | 10000 | 0.5346          | 0.6039 |
  | 0.463         | 10.1  | 11000 | 0.5052          | 0.5689 |
  | 0.4326        | 11.02 | 12000 | 0.4880          | 0.5497 |
  | 0.3981        | 11.94 | 13000 | 0.4778          | 0.5357 |
  | 0.3602        | 12.86 | 14000 | 0.4656          | 0.5198 |
  | 0.3501        | 13.77 | 15000 | 0.4510          | 0.5085 |
  | 0.3199        | 14.69 | 16000 | 0.4617          | 0.5010 |
  | 0.3058        | 15.61 | 17000 | 0.4385          | 0.4880 |
  | 0.2844        | 16.53 | 18000 | 0.4638          | 0.4930 |
  | 0.2729        | 17.45 | 19000 | 0.4594          | 0.4783 |
  | 0.2648        | 18.37 | 20000 | 0.4521          | 0.4703 |
  | 0.2515        | 19.28 | 21000 | 0.4727          | 0.4627 |
  | 0.2428        | 20.2  | 22000 | 0.4566          | 0.4587 |
  | 0.2343        | 21.12 | 23000 | 0.4554          | 0.4545 |
  | 0.2228        | 22.04 | 24000 | 0.4670          | 0.4506 |
  | 0.2135        | 22.96 | 25000 | 0.4458          | 0.4446 |
  | 0.2067        | 23.88 | 26000 | 0.4571          | 0.4402 |
  | 0.2065        | 24.79 | 27000 | 0.4680          | 0.4359 |
  | 0.1968        | 25.71 | 28000 | 0.4702          | 0.4346 |
  | 0.1914        | 26.63 | 29000 | 0.4687          | 0.4320 |
  | 0.182         | 27.55 | 30000 | 0.4807          | 0.4332 |
  | 0.1771        | 28.47 | 31000 | 0.4824          | 0.4308 |
  | 0.1728        | 29.38 | 32000 | 0.4804          | 0.4275 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.13.1
  