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---
license: mit
tags:
- generated_from_trainer
language:
- cak
model-index:
- name: wav2vec2-large-xls-r-300m-kaqchikel-with-bloom
results: []
---
# 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 | 68.51 | 3700 | 0.6932 | 0.0995 | 0.3554 |
| 0.0823 | 70.37 | 3800 | 0.6742 | 0.0950 | 0.3409 |
| 0.0799 | 72.22 | 3900 | 0.6852 | 0.0917 | 0.3279 |
| 0.0767 | 74.07 | 4000 | 0.6684 | 0.0929 | 0.3489 |
| 0.0736 | 75.92 | 4100 | 0.6611 | 0.0923 | 0.3393 |
| 0.0708 | 77.77 | 4200 | 0.7123 | 0.0944 | 0.3393 |
| 0.0661 | 79.62 | 4300 | 0.6577 | 0.0899 | 0.3247 |
| 0.0651 | 81.48 | 4400 | 0.6671 | 0.0869 | 0.3150 |
| 0.0607 | 83.33 | 4500 | 0.6980 | 0.0893 | 0.3231 |
| 0.0552 | 85.18 | 4600 | 0.6947 | 0.0884 | 0.3183 |
| 0.0574 | 87.04 | 4700 | 0.6652 | 0.0899 | 0.3183 |
| 0.0503 | 88.88 | 4800 | 0.6798 | 0.0863 | 0.3053 |
| 0.0479 | 90.73 | 4900 | 0.6690 | 0.0884 | 0.3166 |
| 0.0483 | 92.59 | 5000 | 0.6789 | 0.0872 | 0.3069 |
| 0.0437 | 94.44 | 5100 | 0.6758 | 0.0875 | 0.3069 |
| 0.0458 | 96.29 | 5200 | 0.6662 | 0.0884 | 0.3102 |
| 0.0434 | 98.15 | 5300 | 0.6699 | 0.0881 | 0.3069 |
| 0.0449 | 99.99 | 5400 | 0.6700 | 0.0854 | 0.3069 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.2.1
- Tokenizers 0.10.3
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