
# Wav2Vec2 Adult/Child Indonesian Speech Classifier

Wav2Vec2 Adult/Child Indonesian Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a fine-tuned version of [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on a private adult/child Indonesian speech classification dataset.

This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.

## Model

| Model                         | #params | Arch.       | Training/Validation data (text)                      |
| ----------------------------- | ------- | ----------- | ---------------------------------------------------- |
| `wav2vec2-adult-child-id-cls` | 91M     | wav2vec 2.0 | Adult/Child Indonesian Speech Classification Dataset |

## Evaluation Results

The model achieves the following results on evaluation:

| Dataset                                      | Loss   | Accuracy | F1     |
| -------------------------------------------- | ------ | -------- | ------ |
| Adult/Child Indonesian Speech Classification | 0.2603 | 92.22%   | 0.9202 |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

-   `learning_rate`: 3e-05
-   `train_batch_size`: 32
-   `eval_batch_size`: 32
-   `seed`: 42
-   `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
-   `lr_scheduler_type`: linear
-   `lr_scheduler_warmup_ratio`: 0.1
-   `gradient_accumulation_steps`: 1
-   `num_epochs`: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |   F1   |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
|    0.2415     |  1.0  | 305  |     0.2951      |  0.8804  | 0.8695 |
|     0.202     |  2.0  | 610  |     0.2392      |  0.9124  | 0.9081 |
|    0.2161     |  3.0  | 915  |     0.2508      |  0.9199  | 0.9161 |
|    0.1348     |  4.0  | 1220 |     0.2748      |  0.9153  | 0.9126 |
|     0.162     |  5.0  | 1525 |     0.2603      |  0.9222  | 0.9202 |

## Disclaimer

Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.

## Authors

Wav2Vec2 Adult/Child Indonesian Speech Classifier was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/). All computation and development are done on Kaggle.

## Framework versions

-   Transformers 4.18.0
-   Pytorch 1.11.0+cu102
-   Datasets 2.2.0
-   Tokenizers 0.12.1

{"language": "id", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-adult-child-id-cls", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer id arxiv:2006.11477 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #id #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us 

Wav2Vec2 Adult/Child Indonesian Speech Classifier
=================================================


Wav2Vec2 Adult/Child Indonesian Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a fine-tuned version of wav2vec2-base on a private adult/child Indonesian speech classification dataset.


This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.


Model
-----



Evaluation Results
------------------


The model achieves the following results on evaluation:



Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* 'train\_batch\_size': 32
* 'eval\_batch\_size': 32
* 'seed': 42
* 'optimizer': Adam with 'betas=(0.9,0.999)' and 'epsilon=1e-08'
* 'lr\_scheduler\_type': linear
* 'lr\_scheduler\_warmup\_ratio': 0.1
* 'gradient\_accumulation\_steps': 1
* 'num\_epochs': 5


### Training results



Disclaimer
----------


Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.


Authors
-------


Wav2Vec2 Adult/Child Indonesian Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle.


Framework versions
------------------


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.2.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* 'train\_batch\_size': 32
* 'eval\_batch\_size': 32
* 'seed': 42
* 'optimizer': Adam with 'betas=(0.9,0.999)' and 'epsilon=1e-08'
* 'lr\_scheduler\_type': linear
* 'lr\_scheduler\_warmup\_ratio': 0.1
* 'gradient\_accumulation\_steps': 1
* 'num\_epochs': 5 ### Training results



Disclaimer
----------


Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.


Authors
-------


Wav2Vec2 Adult/Child Indonesian Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle.


Framework versions
------------------


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.2.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #id #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* 'train\_batch\_size': 32
* 'eval\_batch\_size': 32
* 'seed': 42
* 'optimizer': Adam with 'betas=(0.9,0.999)' and 'epsilon=1e-08'
* 'lr\_scheduler\_type': linear
* 'lr\_scheduler\_warmup\_ratio': 0.1
* 'gradient\_accumulation\_steps': 1
* 'num\_epochs': 5 ### Training results



Disclaimer
----------


Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.


Authors
-------


Wav2Vec2 Adult/Child Indonesian Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle.


Framework versions
------------------


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.2.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #id #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* 'train\_batch\_size': 32
* 'eval\_batch\_size': 32
* 'seed': 42
* 'optimizer': Adam with 'betas=(0.9,0.999)' and 'epsilon=1e-08'
* 'lr\_scheduler\_type': linear
* 'lr\_scheduler\_warmup\_ratio': 0.1
* 'gradient\_accumulation\_steps': 1
* 'num\_epochs': 5### Training results



Disclaimer
----------


Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.


Authors
-------


Wav2Vec2 Adult/Child Indonesian Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle.


Framework versions
------------------


* Transformers 4.18.0
* Pytorch 1.11.0+cu102
* Datasets 2.2.0
* Tokenizers 0.12.1