├── .gitignore
├── M4L-rhythm-can
└── M4L-rhythm-can.maxproj
├── README.md
├── images
├── abletonlive.png
├── bg.png
├── logo.png
└── ui_screenshot_ise.png
├── package-lock.json
├── package.json
├── releases
├── M4L-rhythm-can.amxd
└── models
│ ├── conditional_generator
│ ├── group1-shard1of1.bin
│ └── model.json
│ └── creative_gan_generator
│ ├── group1-shard1of1.bin
│ └── model.json
├── rhythm-gan.js
├── rhythm-gan.maxpat
├── src
└── constants.js
└── subpatches
├── count_for_me.maxpat
├── makenote_for_me.maxpat
├── setup_1_16.maxpat
└── shuffle_metro.maxpat
/.gitignore:
--------------------------------------------------------------------------------
1 |
2 | node_modules/*
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/README.md:
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1 | # M4L-CreativeGAN-Rhythm
2 |
3 | An Ableton Live Device for Creative-GAN-based rhythm generation models trained with [CreativeGAN-Rhythm repository](https://github.com/naotokui/CreativeGAN-Rhythm).
4 |
5 |
6 |
7 | # Download Ableton Live Device(M4L) and pre-trained tfjs models
8 | You can find an exported M4L device and models [here](https://github.com/naotokui/M4L-CreativeGAN-Rhythm/tree/master/releases).
9 |
10 | # How it works
11 |
12 |
13 |
14 | # Project Page
15 | https://cclab.sfc.keio.ac.jp/projects/rhythmcan/
16 |
17 | # Design
18 | Title logo, Background Texture: [Naoki Ise](http://naokiise.com/)
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/images/abletonlive.png:
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https://raw.githubusercontent.com/naotokui/M4L-CreativeGAN-Rhythm/e1cb5b27e798166330624fbd7622a76a0448f2b2/images/abletonlive.png
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/images/bg.png:
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https://raw.githubusercontent.com/naotokui/M4L-CreativeGAN-Rhythm/e1cb5b27e798166330624fbd7622a76a0448f2b2/images/bg.png
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/images/logo.png:
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https://raw.githubusercontent.com/naotokui/M4L-CreativeGAN-Rhythm/e1cb5b27e798166330624fbd7622a76a0448f2b2/images/logo.png
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/images/ui_screenshot_ise.png:
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https://raw.githubusercontent.com/naotokui/M4L-CreativeGAN-Rhythm/e1cb5b27e798166330624fbd7622a76a0448f2b2/images/ui_screenshot_ise.png
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1 | {
2 | "name": "M4L-x-rhythm-can",
3 | "version": "1.0.0",
4 | "description": "",
5 | "main": "rhythm-gan.js",
6 | "scripts": {
7 | "test": "echo \"Error: no test specified\" && exit 1"
8 | },
9 | "repository": {
10 | "type": "git",
11 | "url": "git+https://github.com/sfc-computational-creativity-lab/M4L-x-rhythm-can.git"
12 | },
13 | "keywords": [],
14 | "author": "",
15 | "license": "ISC",
16 | "bugs": {
17 | "url": "https://github.com/sfc-computational-creativity-lab/M4L-x-rhythm-can/issues"
18 | },
19 | "homepage": "https://github.com/sfc-computational-creativity-lab/M4L-x-rhythm-can#readme",
20 | "dependencies": {
21 | "@tensorflow/tfjs-node": "^1.5.1"
22 | }
23 | }
24 |
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/releases/models/creative_gan_generator/group1-shard1of1.bin:
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https://raw.githubusercontent.com/naotokui/M4L-CreativeGAN-Rhythm/e1cb5b27e798166330624fbd7622a76a0448f2b2/releases/models/creative_gan_generator/group1-shard1of1.bin
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/releases/models/creative_gan_generator/model.json:
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1 | {"format": "layers-model", "generatedBy": "keras v2.3.1", "convertedBy": "TensorFlow.js Converter v1.4.0", "modelTopology": {"keras_version": "2.3.1", "backend": "tensorflow", "model_config": {"class_name": "Model", "config": {"name": "model_2", "layers": [{"name": "input_1", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 100], "dtype": "float32", "sparse": false, "name": "input_1"}, "inbound_nodes": []}, {"name": "dense_3", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 512, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"name": "leaky_re_lu_2", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_2", "trainable": true, "dtype": "float32", "alpha": 0.20000000298023224}, "inbound_nodes": [[["dense_3", 0, 0, {}]]]}, {"name": "batch_normalization_3", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": "float32", "axis": -1, "momentum": 0.9, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["leaky_re_lu_2", 0, 0, {}]]]}, {"name": "dense_4", "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 1024, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_3", 0, 0, {}]]]}, {"name": "leaky_re_lu_3", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_3", "trainable": true, "dtype": "float32", "alpha": 0.20000000298023224}, "inbound_nodes": [[["dense_4", 0, 0, {}]]]}, {"name": "batch_normalization_4", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": -1, "momentum": 0.9, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": 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"shape": [1024], "dtype": "float32"}, {"name": "batch_normalization_4/beta", "shape": [1024], "dtype": "float32"}, {"name": "batch_normalization_4/moving_mean", "shape": [1024], "dtype": "float32"}, {"name": "batch_normalization_4/moving_variance", "shape": [1024], "dtype": "float32"}, {"name": "dense_3/kernel", "shape": [100, 512], "dtype": "float32"}, {"name": "dense_3/bias", "shape": [512], "dtype": "float32"}, {"name": "dense_4/kernel", "shape": [512, 1024], "dtype": "float32"}, {"name": "dense_4/bias", "shape": [1024], "dtype": "float32"}, {"name": "lstm_5/kernel", "shape": [32, 512], "dtype": "float32"}, {"name": "lstm_5/recurrent_kernel", "shape": [128, 512], "dtype": "float32"}, {"name": "lstm_5/bias", "shape": [512], "dtype": "float32"}, {"name": "lstm_6/kernel", "shape": [128, 512], "dtype": "float32"}, {"name": "lstm_6/recurrent_kernel", "shape": [128, 512], "dtype": "float32"}, {"name": "lstm_6/bias", "shape": [512], "dtype": "float32"}, {"name": "lstm_7/kernel", "shape": [128, 36], "dtype": "float32"}, {"name": "lstm_7/recurrent_kernel", "shape": [9, 36], "dtype": "float32"}, {"name": "lstm_7/bias", "shape": [36], "dtype": "float32"}]}]}
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/rhythm-gan.js:
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1 | const path = require('path');
2 | const Max = require('max-api');
3 |
4 | const tf = require('@tensorflow/tfjs-node');
5 |
6 | // Constants
7 | const MIDI_DRUM_MAP = require('./src/constants.js').MIDI_DRUM_MAP;
8 | const DRUM_CLASSES = require('./src/constants.js').DRUM_CLASSES;
9 | const NUM_DRUM_CLASSES = require('./src/constants.js').NUM_DRUM_CLASSES;
10 | const LOOP_DURATION = require('./src/constants.js').LOOP_DURATION;
11 |
12 | const Z_DIM = 100;
13 |
14 | // Global Variables
15 | var generator;
16 | var isModelLoaded = false;
17 | var isGenerating = false;
18 | var last_z = null;
19 |
20 | async function loadModel(filepath){
21 | generator = await tf.loadLayersModel(filepath);
22 | }
23 |
24 | function generate(genre=-1, noise=-1){
25 | let zs = tf.randomNormal([1, Z_DIM]);
26 |
27 | if (last_z != null && noise >= 0.0){
28 | let ns = zs.mul(tf.scalar(noise));
29 | zs = tf.add(last_z, ns);
30 | } else {
31 | last_z = zs;
32 | }
33 |
34 | return doesGenerate(zs, genre);
35 | }
36 |
37 | function doesGenerate(zs, genre=-1){
38 |
39 | // conditioned with genre
40 | if (genre >= 0){
41 | let condition = tf.tensor([[genre]])
42 | var outputsOn = generator.apply([zs, condition]);
43 | }
44 | // non conditional
45 | else {
46 | var outputsOn = generator.apply(zs);
47 | }
48 | return outputsOn.squeeze().transpose().arraySync();
49 | }
50 |
51 | Max.addHandler("loadmodel", (path)=>{
52 | filepath = "file://" + path;
53 | loadModel(filepath);
54 | isModelLoaded = true;
55 | console.log("Model loaded!");
56 | Max.outlet("status", "Ready to generate!");
57 |
58 | });
59 |
60 | Max.addHandler("generate", (threshold, genre=-1)=>{
61 | generatePattern(threshold, genre);
62 | });
63 |
64 | Max.addHandler("generate_with_last_z", (noise, threshold, genre=-1)=>{
65 | generatePattern(threshold, genre, noise);
66 | });
67 |
68 | async function generatePattern(threshold, genre, noise){
69 | Max.outlet("status", "-");
70 | if (isModelLoaded){
71 | if (isGenerating) return;
72 |
73 | isGenerating = true;
74 | let onsets = generate(genre, noise);
75 |
76 | // console.log(onsets);
77 |
78 | Max.outlet("matrix_clear", 1); // clear all
79 | for (var i=0; i< NUM_DRUM_CLASSES; i++){
80 | var sequence = []; // for velocity
81 | // var sequenceTS = []; // for timeshift
82 | // output for matrix view
83 | for (var j=0; j < LOOP_DURATION; j++){
84 | // if (pattern[i * LOOP_DURATION + j] > 0.2) x = 1;
85 | if (onsets[i][j] > threshold){
86 | Max.outlet("matrix_output", j + 1, i + 1, 1); // index for live.grid starts from 1
87 |
88 | // for live.step
89 | sequence.push(Math.floor(onsets[i][j]*127.)); // 0-1 -> 0-127
90 | // sequenceTS.push(Math.floor(utils.scale(timeshifts[i][j], -1., 1, 0, 127))); // -1 - 1 -> 0 - 127
91 | } else {
92 | sequence.push(0);
93 | // sequenceTS.push(64);
94 | }
95 | }
96 |
97 | // output for live.step object
98 | Max.outlet("seq_output", i+1, sequence.join(" "));
99 | // Max.outlet("timeshift_output", i+1, sequenceTS.join(" "));
100 | }
101 | Max.outlet("generated", 1);
102 | isGenerating = false;
103 | } else {
104 | console.error("Model is not loaded yet");
105 | Max.outlet("status", "No Model Loaded");
106 | }
107 | }
108 |
109 |
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/src/constants.js:
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1 |
2 |
3 | const DRUM_CLASSES = [
4 | 'Kick',
5 | 'Snare',
6 | 'Hi-hat closed',
7 | 'Hi-hat open',
8 | 'Tom low',
9 | 'Tom mid',
10 | 'Tom high',
11 | 'Clap',
12 | 'Rim'
13 | ]
14 |
15 | const MIDI_DRUM_MAP = {
16 | 36: 0,
17 | 35: 0,
18 | 38: 1,
19 | 27: 1,
20 | 28: 1,
21 | 31: 1,
22 | 32: 1,
23 | 33: 1,
24 | 34: 1,
25 | 37: 1,
26 | 39: 1,
27 | 40: 1,
28 | 56: 1,
29 | 65: 1,
30 | 66: 1,
31 | 75: 1,
32 | 85: 1,
33 | 42: 2,
34 | 44: 2,
35 | 54: 2,
36 | 68: 2,
37 | 69: 2,
38 | 70: 2,
39 | 71: 2,
40 | 73: 2,
41 | 78: 2,
42 | 80: 2,
43 | 46: 3,
44 | 67: 3,
45 | 72: 3,
46 | 74: 3,
47 | 79: 3,
48 | 81: 3,
49 | 45: 4,
50 | 29: 4,
51 | 41: 4,
52 | 61: 4,
53 | 64: 4,
54 | 84: 4,
55 | 48: 5,
56 | 47: 5,
57 | 60: 5,
58 | 63: 5,
59 | 77: 5,
60 | 86: 5,
61 | 87: 5,
62 | 50: 6,
63 | 30: 6,
64 | 43: 6,
65 | 62: 6,
66 | 76: 6,
67 | 83: 6,
68 | 49: 7,
69 | 55: 7,
70 | 57: 7,
71 | 58: 7,
72 | 51: 8,
73 | 52: 8,
74 | 53: 8,
75 | 59: 8,
76 | 82: 8
77 | }
78 |
79 | const NUM_DRUM_CLASSES = DRUM_CLASSES.length;
80 | const LOOP_DURATION = 32; // 2bars x 16th note
81 |
82 | const ORIGINAL_DIM = NUM_DRUM_CLASSES * LOOP_DURATION;
83 |
84 | exports.MIDI_DRUM_MAP = MIDI_DRUM_MAP;
85 | exports.DRUM_CLASSES = DRUM_CLASSES;
86 |
87 | exports.NUM_DRUM_CLASSES = NUM_DRUM_CLASSES;
88 | exports.LOOP_DURATION = LOOP_DURATION;
89 | exports.ORIGINAL_DIM = ORIGINAL_DIM;
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