
# Wav2Vec2 XLS-R Adult/Child Speech Classifier

Wav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a private adult/child 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-xls-r-adult-child-cls` | 300M    | XLS-R | Adult/Child Speech Classification Dataset |

## Evaluation Results

The model achieves the following results on evaluation:

| Dataset                           | Loss   | Accuracy | F1     |
| --------------------------------- | ------ | -------- | ------ |
| Adult/Child Speech Classification | 0.1851 | 94.69%   | 0.9508 |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- `learning_rate`: 3e-05
- `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_ratio`: 0.1
- `num_epochs`: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |   F1   |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
|    0.2906     |  1.0  | 383  |     0.1856      |  0.9372  | 0.9421 |
|    0.1749     |  2.0  | 766  |     0.1925      |  0.9418  | 0.9465 |
|    0.1681     |  3.0  | 1149 |     0.1893      |  0.9414  | 0.9459 |
|    0.1295     |  4.0  | 1532 |     0.1851      |  0.9469  | 0.9508 |
|    0.2031     |  5.0  | 1915 |     0.1944      |  0.9423  | 0.9460 |

## Disclaimer

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

## Authors

Wav2Vec2 XLS-R Adult/Child Speech Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Kaggle.

## Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0

{"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-xls-r-adult-child-cls", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer en arxiv:2111.09296 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #arxiv-2111.09296 #license-apache-2.0 #endpoints_compatible #region-us 

Wav2Vec2 XLS-R Adult/Child Speech Classifier
============================================


Wav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the XLS-R architecture. This model is a fine-tuned version of wav2vec2-xls-r-300m on a private adult/child 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': 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\_ratio': 0.1
* '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 XLS-R Adult/Child Speech Classifier was trained and evaluated by Wilson Wongso. All computation and development are done on Kaggle.


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


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* '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\_ratio': 0.1
* '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 XLS-R Adult/Child Speech Classifier was trained and evaluated by Wilson Wongso. All computation and development are done on Kaggle.


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


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #arxiv-2111.09296 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* '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\_ratio': 0.1
* '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 XLS-R Adult/Child Speech Classifier was trained and evaluated by Wilson Wongso. All computation and development are done on Kaggle.


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


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #arxiv-2111.09296 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* '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\_ratio': 0.1
* '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 XLS-R Adult/Child Speech Classifier was trained and evaluated by Wilson Wongso. All computation and development are done on Kaggle.


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


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0