
# wav2vec2-bloom-speech-stk

![logo for Bloom Library](https://bloom-vist.s3.amazonaws.com/bloom_logo.png) ![sil-ai logo](https://s3.amazonaws.com/moonup/production/uploads/1661440873726-6108057a823007eaf0c7bd10.png) 

## 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/)

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [SIL-AI/bloom-speech](https://huggingface.co/datasets/sil-ai/bloom-speech) - STK (Aramba) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8616
- Wer: 0.5312
- Cer: 0.1640

Users should refer to the original model for tutorials on using a trained model for inference.

## Intended uses & limitations

Users of this model must abide by the [SIL RAIL-M License](https://huggingface.co/spaces/sil-ai/model-license).

This model is created as a proof of concept 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: 16
- eval_batch_size: 8
- 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: 250
- num_epochs: 1000.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| No log        | 41.67  | 250  | 2.9562          | 1.1641 | 1.0317 |
| 4.9676        | 83.33  | 500  | 1.3412          | 0.6641 | 0.1928 |
| 4.9676        | 125.0  | 750  | 1.5549          | 0.625  | 0.1856 |
| 0.1326        | 166.67 | 1000 | 1.4986          | 0.6094 | 0.1856 |
| 0.1326        | 208.33 | 1250 | 1.5501          | 0.5938 | 0.1554 |
| 0.0597        | 250.0  | 1500 | 1.8616          | 0.5312 | 0.1640 |
| 0.0597        | 291.67 | 1750 | 1.7009          | 0.5312 | 0.1496 |
| 0.0352        | 333.33 | 2000 | 1.9785          | 0.5781 | 0.1770 |
| 0.0352        | 375.0  | 2250 | 1.7492          | 0.625  | 0.1871 |


### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.2.2
- Tokenizers 0.12.1

{"language": ["stk"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL International AI & NLP RAIL-M license further specifying rights and usage.\n\nThe SIL RAIL-M License specifies: \n\n1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content. Particularly, you cannot use the model use with the intent or effect of harming or enabling discrimination against Indigenous People.\n2. SIL claims no rights on outputs you generate for non-commercial use, you are free to use them and are accountable for their use, which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the model non-commercially including as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the SIL International AI & NLP RAIL-M to all your users (please read the license entirely and carefully). Please read the full license here: https://huggingface.co/spaces/sil-ai/model-license\n\nBy clicking on \"Access repository\" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.\n\nIf you would like to ask about commercial uses of this model, please [email us](mailto:sj@derivation.co).\n  ", "extra_gated_fields": {"I have read the License and agree with its terms": "checkbox"}, "model-index": [{"name": "wav2vec2-bloom-speech-stk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Bloom Speech stk", "type": "sil-ai/bloom-speech", "args": "stk"}, "metrics": [{"type": "wer", "value": 53.12, "name": "Test WER"}, {"type": "cer", "value": 16.4, "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition sil-ai/bloom-speech generated_from_trainer stk dataset:bloom_speech license:other model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #stk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us 

wav2vec2-bloom-speech-stk
=========================


!logo for Bloom Library !sil-ai logo


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


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


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - STK (Aramba) dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8616
* Wer: 0.5312
* Cer: 0.1640


Users should refer to the original model for tutorials on using a trained model for inference.


Intended uses & limitations
---------------------------


Users of this model must abide by the SIL RAIL-M License.


This model is created as a proof of concept 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: 16
* eval\_batch\_size: 8
* 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: 250
* num\_epochs: 1000.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* 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: 250
* num\_epochs: 1000.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #stk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* 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: 250
* num\_epochs: 1000.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #stk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* 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: 250
* num\_epochs: 1000.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 2.2.2
* Tokenizers 0.12.1