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# data-augmentation-whitenoise-timit-1155

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5458
- Wer: 0.3324

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5204        | 0.8   | 500   | 1.6948          | 0.9531 |
| 0.8435        | 1.6   | 1000  | 0.5367          | 0.5113 |
| 0.4449        | 2.4   | 1500  | 0.4612          | 0.4528 |
| 0.3182        | 3.21  | 2000  | 0.4314          | 0.4156 |
| 0.2328        | 4.01  | 2500  | 0.4250          | 0.4031 |
| 0.1897        | 4.81  | 3000  | 0.4630          | 0.4023 |
| 0.1628        | 5.61  | 3500  | 0.4445          | 0.3922 |
| 0.1472        | 6.41  | 4000  | 0.4452          | 0.3793 |
| 0.1293        | 7.21  | 4500  | 0.4715          | 0.3847 |
| 0.1176        | 8.01  | 5000  | 0.4267          | 0.3757 |
| 0.1023        | 8.81  | 5500  | 0.4494          | 0.3821 |
| 0.092         | 9.62  | 6000  | 0.4501          | 0.3704 |
| 0.0926        | 10.42 | 6500  | 0.4722          | 0.3643 |
| 0.0784        | 11.22 | 7000  | 0.5033          | 0.3765 |
| 0.077         | 12.02 | 7500  | 0.5165          | 0.3684 |
| 0.0704        | 12.82 | 8000  | 0.5138          | 0.3646 |
| 0.0599        | 13.62 | 8500  | 0.5664          | 0.3674 |
| 0.0582        | 14.42 | 9000  | 0.5188          | 0.3575 |
| 0.0526        | 15.22 | 9500  | 0.5605          | 0.3621 |
| 0.0512        | 16.03 | 10000 | 0.5400          | 0.3585 |
| 0.0468        | 16.83 | 10500 | 0.5471          | 0.3603 |
| 0.0445        | 17.63 | 11000 | 0.5168          | 0.3555 |
| 0.0411        | 18.43 | 11500 | 0.5772          | 0.3542 |
| 0.0394        | 19.23 | 12000 | 0.5079          | 0.3567 |
| 0.0354        | 20.03 | 12500 | 0.5427          | 0.3613 |
| 0.0325        | 20.83 | 13000 | 0.5532          | 0.3572 |
| 0.0318        | 21.63 | 13500 | 0.5223          | 0.3514 |
| 0.0269        | 22.44 | 14000 | 0.6002          | 0.3460 |
| 0.028         | 23.24 | 14500 | 0.5591          | 0.3432 |
| 0.0254        | 24.04 | 15000 | 0.5837          | 0.3432 |
| 0.0235        | 24.84 | 15500 | 0.5571          | 0.3397 |
| 0.0223        | 25.64 | 16000 | 0.5470          | 0.3383 |
| 0.0193        | 26.44 | 16500 | 0.5611          | 0.3367 |
| 0.0227        | 27.24 | 17000 | 0.5405          | 0.3342 |
| 0.0183        | 28.04 | 17500 | 0.5205          | 0.3330 |
| 0.017         | 28.85 | 18000 | 0.5512          | 0.3330 |
| 0.0167        | 29.65 | 18500 | 0.5458          | 0.3324 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "data-augmentation-whitenoise-timit-1155", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

data-augmentation-whitenoise-timit-1155
=======================================


This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5458
* Wer: 0.3324


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1