AudioNet-V1

KO | EN

gitlite — search

#hacktoberfest #golang #android #machine-learning #docker #macos #nodejs #java #windows #cli #kubernetes #awesome-list #openai #open-source

(★ 9)

1D CNN based classifier for Speech Commands Dataset

AudioNet-V1 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# AudioNet-V1
AudioNet is a simple convolutional neural net based on 1-D convolutions. This is trained and tested on google's speech command dataset.

## Requirements

Tested with following setup

### Software
1) Python 3.5
2) Numpy
3) Scipy
4) Keras 2.0.8
5) Tensorflow 1.4.1
6) Scikit-learn

### Hardware

1) GTX 1050 TI 4 GB

## One Dimensional CNN
Here, 1-D convolutions (linear convolutions) are used on top of regular hidden layers to classify the speech signal. The dataset used is Google's speech Commands Dataset

The network has five 1-D convolutional layers with kernel size 32 and stride of 4. They are followed by four hidden layers with 512 neurons each. The network has approximately 10 million parameters in total.

### Data Augmentation used

1) Random noise
2) Random shift

### Training Loss vs Epochs

![](https://github.com/vj-1988/AudioNet-V1/blob/master/Images/training_loss.png)

### Training Acuracy vs Epochs

![](https://github.com/vj-1988/AudioNet-V1/blob/master/Images/training_accuracy.png)

## Training

The dataset has to be in appropriate subfolders with each folder name being the class label. The script AudioNet32.py needs the following inputs to train

1) data_path : root folder of dataset
2) train_ratio : ratio of files to be used for training and remaining is for validation
3) batch_size : minibatch size for training.
4) num_epochs : total no. of epochs
5) dst : destination folder to save weights, logs

The script will generate a pickle file that contains synset for validation, training and validation files path and labels. This can be used to resume training using resume_training() function.

The script will save weights once in every 2 epochs.

## Validation

The synset used for training is available in train_data_dic.pkl file. The pretrained weights are available in the following link

[Download pretrained weights (Epoch 10)](https://drive.google.com/file/d/1vrfeXhtb8mRiLI1ja3Ep80M-zv9a-IVw/view?usp=sharing)

AudioNet-V1

AudioNet-V1 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

AudioNet-V1

AudioNet-V1 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README