readme: ---
  license: mit
  tags:
  - generated_from_trainer
  language:
  - cak
  model-index:
  - name: wav2vec2-large-xls-r-300m-kaqchikel-with-bloom
    results: []
  ---
  
  ![sil-ai logo](https://s3.amazonaws.com/moonup/production/uploads/1661440873726-6108057a823007eaf0c7bd10.png) 
  
  # wav2vec2-large-xls-r-300m-kaqchikel-with-bloom
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a collection of audio from [Deditos](deditos.org) videos in Kaqchikel provided by [Viña Studios](www.vinyastudios.org) and Kaqchikel audio from audiobooks on [Bloom Library](bloomlibrary.org).
  It achieves the following results on the evaluation set:
  - Loss: 0.6700
  - Cer: 0.0854
  - Wer: 0.3069
  
  ## Model description
  
  - **Homepage:** [SIL AI](https://ai.sil.org/)
  - **Point of Contact:** [SIL AI email](mailto:idx_aqua@sil.org)
  - **Source Data:** [Bloom Library](https://bloomlibrary.org/) and [Viña Studios](https://www.vinyastudios.org)
  
  This model is a baseline model finetuned from [XLS-R 300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m). Users should refer to the original model for tutorials on using a trained model for inference.
  
  ## Intended uses & limitations
  
  Users of this model should abide by the [UN Declarations on the Rights of Indigenous Peoples](https://www.un.org/development/desa/indigenouspeoples/declaration-on-the-rights-of-indigenous-peoples.html).
  
  This model is released under the MIT license and no guarantees are made regarding the performance of the model is specific situations.
  
  ## Training and evaluation data
  
  Training, Validation, and Test datasets were generated from the same corpus, ensuring that no duplicate files were used.
  
  ## Training procedure
  
  Standard finetuning of XLS-R was used based on the examples in the [Hugging Face Transformers Github](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition)
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - 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: 200
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Cer    | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 11.1557       | 1.84  | 100  | 4.2251          | 1.0    | 1.0    |
  | 3.7231        | 3.7   | 200  | 3.5794          | 1.0    | 1.0    |
  | 3.3076        | 5.55  | 300  | 3.4362          | 1.0    | 1.0    |
  | 3.2495        | 7.4   | 400  | 3.2553          | 1.0    | 1.0    |
  | 3.2076        | 9.26  | 500  | 3.2932          | 1.0    | 1.0    |
  | 3.1304        | 11.11 | 600  | 3.1100          | 1.0    | 1.0    |
  | 2.899         | 12.95 | 700  | 2.4021          | 0.8477 | 1.0    |
  | 2.2875        | 14.81 | 800  | 1.5473          | 0.4790 | 0.9984 |
  | 1.7605        | 16.66 | 900  | 1.1034          | 0.3061 | 0.9192 |
  | 1.3802        | 18.51 | 1000 | 0.9422          | 0.2386 | 0.8530 |
  | 1.0989        | 20.37 | 1100 | 0.7429          | 0.1667 | 0.6042 |
  | 0.857         | 22.22 | 1200 | 0.7490          | 0.1499 | 0.5751 |
  | 0.6899        | 24.07 | 1300 | 0.6376          | 0.1286 | 0.4798 |
  | 0.5927        | 25.92 | 1400 | 0.6887          | 0.1232 | 0.4443 |
  | 0.4699        | 27.77 | 1500 | 0.6341          | 0.1184 | 0.4378 |
  | 0.4029        | 29.62 | 1600 | 0.6341          | 0.1103 | 0.4216 |
  | 0.3492        | 31.48 | 1700 | 0.6709          | 0.1121 | 0.4120 |
  | 0.3019        | 33.33 | 1800 | 0.7665          | 0.1097 | 0.4136 |
  | 0.2681        | 35.18 | 1900 | 0.6671          | 0.1085 | 0.4120 |
  | 0.2491        | 37.04 | 2000 | 0.7049          | 0.1010 | 0.3748 |
  | 0.2108        | 38.88 | 2100 | 0.6699          | 0.1064 | 0.3974 |
  | 0.2146        | 40.73 | 2200 | 0.7037          | 0.1046 | 0.3780 |
  | 0.1854        | 42.59 | 2300 | 0.6970          | 0.1055 | 0.4006 |
  | 0.1693        | 44.44 | 2400 | 0.6593          | 0.0980 | 0.3764 |
  | 0.1628        | 46.29 | 2500 | 0.7162          | 0.0998 | 0.3764 |
  | 0.156         | 48.15 | 2600 | 0.6445          | 0.0998 | 0.3829 |
  | 0.1439        | 49.99 | 2700 | 0.6437          | 0.1004 | 0.3845 |
  | 0.1292        | 51.84 | 2800 | 0.6471          | 0.0944 | 0.3457 |
  | 0.1287        | 53.7  | 2900 | 0.6411          | 0.0923 | 0.3538 |
  | 0.1186        | 55.55 | 3000 | 0.6754          | 0.0992 | 0.3813 |
  | 0.1175        | 57.4  | 3100 | 0.6741          | 0.0953 | 0.3538 |
  | 0.1082        | 59.26 | 3200 | 0.6949          | 0.0977 | 0.3619 |
  | 0.105         | 61.11 | 3300 | 0.6919          | 0.0983 | 0.3683 |
  | 0.1048        | 62.95 | 3400 | 0.6802          | 0.0950 | 0.3425 |
  | 0.092         | 64.81 | 3500 | 0.6830          | 0.0962 | 0.3263 |
  | 0.0904        | 66.66 | 3600 | 0.6993          | 0.0971 | 0.3554 |
  | 0.0914        | 68.5