
![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        | 68.51 | 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

{"language": ["cak"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-kaqchikel-with-bloom", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer cak license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #cak #license-mit #endpoints_compatible #region-us 

!sil-ai logo


wav2vec2-large-xls-r-300m-kaqchikel-with-bloom
==============================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on a collection of audio from Deditos videos in Kaqchikel provided by Viña Studios and Kaqchikel audio from audiobooks on Bloom Library.
It achieves the following results on the evaluation set:


* Loss: 0.6700
* Cer: 0.0854
* Wer: 0.3069


Model description
-----------------


* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library and Viña Studios


This model is a baseline model finetuned from 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.


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


### 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



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.2.1
* Tokenizers 0.10.3



### 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 ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.2.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #cak #license-mit #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.2.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #cak #license-mit #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.2.1
* Tokenizers 0.10.3