
# DistilWav2Vec2 Adult/Child Speech Classifier 37M

DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a distilled version of [wav2vec2-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-adult-child-cls) 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)           |
| ------------------------------------- | ------- | ----------- | ----------------------------------------- |
| `distil-wav2vec2-adult-child-cls-37m` | 37M     | wav2vec 2.0 | Adult/Child Speech Classification Dataset |

## Evaluation Results

The model achieves the following results on evaluation:

| Dataset                           | Loss   | Accuracy | F1     |
| --------------------------------- | ------ | -------- | ------ |
| Adult/Child Speech Classification | 0.1431 | 95.89%   | 0.9624 |

## 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
- `gradient_accumulation_steps`: 4
- `total_train_batch_size`: 128
- `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   |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
|    0.2586     |  1.0  |  96  |     0.2257      |  0.9298  | 0.9363 |
|    0.1917     |  2.0  | 192  |     0.1743      |  0.9460  | 0.9500 |
|    0.1568     |  3.0  | 288  |     0.1701      |  0.9511  | 0.9545 |
|    0.0965     |  4.0  | 384  |     0.1501      |  0.9548  | 0.9584 |
|    0.1179     |  5.0  | 480  |     0.1431      |  0.9589  | 0.9624 |

## Disclaimer

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

## Authors

DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/). All computation and development are done on Kaggle.

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
{"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distil-wav2vec2-adult-child-cls-37m", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer en arxiv:2006.11477 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us 

DistilWav2Vec2 Adult/Child Speech Classifier 37M
================================================


DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a distilled version of wav2vec2-adult-child-cls 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': 32
* 'eval\_batch\_size': 32
* 'seed': 42
* 'gradient\_accumulation\_steps': 4
* 'total\_train\_batch\_size': 128
* '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
-------


DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle.


### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* 'learning\_rate': 3e-05
* 'train\_batch\_size': 32
* 'eval\_batch\_size': 32
* 'seed': 42
* 'gradient\_accumulation\_steps': 4
* 'total\_train\_batch\_size': 128
* '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
-------


DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle. ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #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
* 'gradient\_accumulation\_steps': 4
* 'total\_train\_batch\_size': 128
* '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
-------


DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle. ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #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
* 'gradient\_accumulation\_steps': 4
* 'total\_train\_batch\_size': 128
* '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
-------


DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by Ananto Joyoadikusumo. All computation and development are done on Kaggle.### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.10.3