audioset_transfer

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audioset_transfer

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using Google's pretrained audioset model, to classify new audio categories

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Description

This simple project, uses Google'es pretrained model, to build a new classifer, The VGGish model can be used for extracting audio features, which I pass into my own network, which classifies audio into 4 categories. More in the jupyter notebook file.

Adapted from, Tensorflow Models Audioset pretrained model code, provided by communnity
https://github.com/tensorflow/models/tree/master/research/audioset

please follow install instructions from above link

Usage :

For Training :

use shell scripts provided in shell_scripts folder

1. download.sh < youtube url> <class index>
    this will download the audio of youtube url, and convert it into a suitable format, and split
    them into 2 seconds fragments using wav_splitter project ( see subfolder )

2. In the output folder, you will get your files, however the end fragment file is less than 2 second, so delete that

3. repeat this for all categories

4. then use dataset_creation.sh, this will create a dataset.txt file, each row has training filename
    (labels will be inferred automatically in jupyter notebook)

5. Now follow instructions on train_my_modelv3 notebook

For Inference :

1. Use sox commands below to download audio from youtube, and finally extract 1 second file , or provide your own file(but do required preprocessing as indicated in one of the commands ), then follow instructions on train_my_modelv3 notebook

To Do / Further Improvements

Add Sound Normalization ?
Experiment with freesounds.org data

Important Sox Commands

split audio into multiple chunks of 1 second 
ffmpeg -i b52.wav -f segment -segment_time 1 -c copy fragments/out%03d.wav

download audio from a youtube url
youtube-dl --extract-audio --audio-format mp3 https://www.youtube.com/watch?v=EdQJVGZmp-E

convert into suitable format
sox "${now}.mp3" "${now}.wav" channels 1 rate 16000

audioset_transfer

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