java-tensorflow-music

Music classification, music search, music recommender and music encoder implemented in Tensorflow and Java

The trained models were obtained from the Keras audio deep learning project

Install

Add the following dependency to your POM file:

<dependency>
  <groupId>com.github.chen0040</groupId>
  <artifactId>java-tensorflow-music</artifactId>
  <version>1.0.1</version>
</dependency>

Usage

Run audio classifier in Java

The sample codes below shows how to use the cifar audio classifier to predict the genres of music:

import com.github.chen0040.tensorflow.classifiers.models.cifar10.Cifar10AudioClassifier;
import com.github.chen0040.tensorflow.classifiers.utils.ResourceUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

public class Demo {
    public static void main(String[] args) {
        
        Cifar10AudioClassifier classifier = new Cifar10AudioClassifier();
        classifier.load_model();
        
        List<String> paths = getAudioFiles();
        
        Collections.shuffle(paths);
        
        for (String path : paths) {
            System.out.println("Predicting " + path + " ...");
            File f = new File(path);
            String label = classifier.predict_audio(f);
        
            System.out.println("Predicted: " + label);
        }
    }
}

The sample codes below shows how to use the resnet v2 audio classifier to predict the genres of music:

import com.github.chen0040.tensorflow.classifiers.resnet_v2.ResNetV2AudioClassifier;
import com.github.chen0040.tensorflow.classifiers.utils.ResourceUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

public class Demo {
    public static void main(String[] args) {
        
        ResNetV2AudioClassifier classifier = new ResNetV2AudioClassifier();
        classifier.load_model();
        
        List<String> paths = getAudioFiles();
        
        Collections.shuffle(paths);
        
        for (String path : paths) {
            System.out.println("Predicting " + path + " ...");
            File f = new File(path);
            String label = classifier.predict_audio(f);
        
            System.out.println("Predicted: " + label);
        }
    }
}

Extract features from audio in Java

The sample codes below shows how to use the cifar audio classifier to encode an audio file into an float array:

import com.github.chen0040.tensorflow.classifiers.models.cifar10.Cifar10AudioClassifier;
import com.github.chen0040.tensorflow.classifiers.utils.ResourceUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

public class Demo {
    public static void main(String[] args){
        
        Cifar10AudioClassifier classifier = new Cifar10AudioClassifier();
        classifier.load_model();
        
        List<String> paths = getAudioFiles();
        
        Collections.shuffle(paths);
        
        for (String path : paths) {
            System.out.println("Encoding " + path + " ...");
            File f = new File(path);
            float[] encoded_audio = classifier.encode_audio(f);
        
            System.out.println("Encoded: " + Arrays.toString(encoded_audio));
        }
    }
}

The sample codes below shows how to the resnet v2 audio classifier to encode an audio file into an float array:

import com.github.chen0040.tensorflow.classifiers.resnet_v2.ResNetV2AudioClassifier;
import com.github.chen0040.tensorflow.classifiers.utils.ResourceUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

public class Demo {
    public static void main(String[] args) {
        
        ResNetV2AudioClassifier classifier = new ResNetV2AudioClassifier();
        classifier.load_model();
        
        List<String> paths = getAudioFiles();
        
        Collections.shuffle(paths);
        
        for (String path : paths) {
            System.out.println("Encoding " + path + " ...");
            File f = new File(path);
            float[] encoded_audio = classifier.encode_audio(f);
        
            System.out.println("Encoded: " + Arrays.toString(encoded_audio));
        }
    }
}

Audio Search Engine

The sample codes below shows how to index and search for audio file using the AudioSearchEngine class:

import com.github.chen0040.tensorflow.search.models.AudioSearchEngine;
import com.github.chen0040.tensorflow.search.models.AudioSearchEntry;

import java.io.File;
import java.util.List;

public class Demo {
    public static void main(String[] args){
        AudioSearchEngine searchEngine = new AudioSearchEngine();
        if(!searchEngine.loadIndexDbIfExists()) {
            searchEngine.indexAll(FileUtils.getAudioFiles());
            searchEngine.saveIndexDb();
        }
        
        int pageIndex = 0;
        int pageSize = 20;
        boolean skipPerfectMatch = true;
        File f = new File("mp3_samples/example.mp3");
        System.out.println("querying similar music to " + f.getName());
        List<AudioSearchEntry> result = searchEngine.query(f, pageIndex, pageSize, skipPerfectMatch);
        for(int i=0; i < result.size(); ++i){
            System.out.println("# " + i + ": " + result.get(i).getPath() + " (distSq: " + result.get(i).getDistance() + ")");
        }
    }
}

Music Recommend-er

The sample codes below shows how to recommend musics based on user's music history using the KnnAudioRecommender class:

import com.github.chen0040.tensorflow.classifiers.utils.FileUtils;
import com.github.chen0040.tensorflow.recommenders.models.AudioUserHistory;
import com.github.chen0040.tensorflow.recommenders.models.KnnAudioRecommender;
import com.github.chen0040.tensorflow.search.models.AudioSearchEntry;

import java.io.File;
import java.util.Collections;
import java.util.List;

public class Demo {
    public static void main(String[] args){
        // create fake listening history of songs
        AudioUserHistory userHistory = new AudioUserHistory();
        
        List<String> audioFiles = FileUtils.getAudioFilePaths();
        Collections.shuffle(audioFiles);
        
        for(int i=0; i < 40; ++i){
            String filePath = audioFiles.get(i);
            userHistory.logAudio(filePath);
            try {
                Thread.sleep(100L);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        }
        
        KnnAudioRecommender recommender = new KnnAudioRecommender();
        if(!recommender.loadIndexDbIfExists()) {
            recommender.indexAll(new File("music_samples").listFiles(a -> a.getAbsolutePath().toLowerCase().endsWith(".au")));
            recommender.saveIndexDb();
        }
        
        System.out.println(userHistory.head(10));
        
        int k = 10;
        List<AudioSearchEntry> result = recommender.recommends(userHistory.getHistory(), k);
        
        for(int i=0; i < result.size(); ++i){
            AudioSearchEntry entry = result.get(i);
            System.out.println("Search Result #" + (i+1) + ": " + entry.getPath());
        }
    }
}

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