dorothy-cci 0.0.1

A Creative Computing Python Library for Interactive Audio Generation and Audio Reactive Drawing

Dorothy

A Creative Computing Python Library for Interactive Audio Generation and Audio Reactive Drawing

Leaning on the work of openCV, sounddevice and librosa with a Processing / p5.js -like API, make some easy sketching with shapes and images reacting to FFT, Beats and Amplitude in Python! Also, as its Python and the canvas is just a numpy pixel array, you can do any of the cool Python stuff you would normally do, or use other powerful libraries like NumPy, PyTorch or Tensorflow.

• Brief Guide

• Examples

• Full API Reference

• Generating Music

Brief Guide

Has setup() and draw() functions that can be overwritten using a custom class

from Dorothy import Dorothy

dot = Dorothy()

class MySketch:

    def __init__(self):
        dot.start_loop(self.setup, self.draw)  

    def setup(self):
        print("setup")
        dot.background((255,255,255))
    
    def draw(self):
        return

MySketch()   

Running

Launch your sketch (.py) from terminal. Your sketch must be in the same directory as Dorothy.py

Install requirements with pip install -r requirements.txt

Closing the window

Either hold q with the window in focus, or use ctrl-z in the terminal to close. You must close the current window before re-running to see changes.

Drawing

For drawing, its suggested to use the openCV drawing functions, with dot.canvas as the first argument (this is the image to draw onto).

dot.canvas is just a 3 channel 4D numpy array that you can edit you like in the sketch or draw functions

Other Processing like functions are

• dot.background((colour), alpha)

• dot.mouse_x, dot.mouse_y, dot.mouse_down

• dot.millis, dot.frames

• dot.width, dot.height

Reacting to Music

Makes analysis information available for real time visualisation. Works out the threading to work with Dorothy.py

We have

• dot.music.fft_vals (updates with music playback)

• dot.music.amplitude (updates with music playback)

• dot.music.is_beat() (call in draw(), returns true if there has been a beat since last frame

Examples

Seed

This is the bare bones starter for any projects

Alpha

Shows how to draw shapes with transparency. openCV doesnt do this natively so you call dot.to_alpha(alpha_val) to get a new layer to draw to (instead of dot.canvas). We then take care of the masking and blending to make it work.

Alpha Background

Add a second argument to the an.background() to draw a transparent background and make trails

Grid

Audio reactive color pattern from a nested for loop in the def draw() function

Molnar

Audio reactive scaled pattern from a nested for loop in the def setup() function

Mouse Position

Use dot.mouse_x and dot.mouse_y to control where a circle is drawn, with size moving to amplitude.

Webcam Capture

Use openCV to grab and draw the webcam and bounce centre panel to music.

Linear Transforms

Apply linear transforms and translations to canvases. This works in the opposite way to Processing, in that you

1. Get a new canvas (dot.get_layer())

2. Draw to it

3. Apply transformations (dot.transform(),dot.rotate(),dot.scale()). This function also takes a new origin about which to make the transformation if required (a translation).

4. Put back onto main canvas (dot.draw_layer())

Rotate

Rotate In a Grid

Scale

Beat Tracking

Shows how to use dot.music.is_beat() to display the beat tracking data in real time. Also shows how to use properties of the MySketch class to have variables that persist outside of the def draw() and def setup() functions.

FFT

Visualise live fft data

Amplitude

Visualise live amplitude data

Images

Use get_images() to load in a dataset of images and dot.paste() to copy onto canvas

Contours

Get contours and mask out, moving image sections radially in response to fft values. More complex example!

RAVE

Examples on generating with / interacting with RAVE models

MAGNet

Examples on generating with / interacting with MAGNet models

YOLO Body Tracking

Example drawing pose from web cam

Interactive GAN

Use the mouse to move through the latent space of an MNIST GAN

Interactive RAVE / YOLO

Control interpolation points of RAVE with pose tracked hand position

Music Generation

Picking Music Source

You can either play a soundfile

file_path = "../audio/hiphop.wav"
dot.music.start_file_stream(file_path)

Or pick a an output device playing on your computer. On MacOSX I use Blackhole and Multioutput device to pump audio to here, and to listen in speakers as well. Should work on windows but I havent tested anything yet!

You could also use this approach to get in the stream of your laptops microphone, or an external microphone. print(sd.query_devices()) will give the you list of available devices, and their device ids to pass to the set up function.

print(sd.query_devices())
dot.music.start_device_stream(2)

Both use

dot.music.play()
dot.music.stop()
dot.music.pause()
dot.music.resume()

Generating Audio with RAVE

There is also a player to generate, visualise and interact with pretrained RAVE models.

Examples here

rave_id = dot.music.start_rave_stream("vintage.ts", latent_dim=latent_dim)

Will load in a .ts model. Remember to play() to start!

It will initially just start at a random place in latent space but there are two key ways to interact

1. Manually set the z vector using where z is a torch tensor and has the shape (1, latent_dims, 1)

z = torch.randn((1,16,1))
dot.music.update_rave_latent(z)

3. Do timbre transfer from audio

   • Pipe audio from a stream you have already started (e.g. blackhole to pull whatever is coming from your computer, or a microphone). If you want to listen to the output of the RAVE model, you should manually set its output device so that it doesnt interfere with the stream you have hi-jacked from your machine.

# Give an output device (e.g. your speakers) so you can hear the output
rave_id = dot.music.start_rave_stream("vintage.ts", output_device=4, latent_dim=latent_dim)
# Set stream to be blackhole / microphone device
device_id = dot.music.start_device_stream(2)
dot.music.update_rave_from_stream(device_id)
dot.music.play()

• Or a file player stream

rave_id = dot.music.start_rave_stream("vintage.ts", latent_dim=latent_dim)
device_id = dot.music.start_file_stream("../audio/gospel.wav")
# Set as input to rave (this mutes the source stream, use .gain property to hear both)
dot.music.update_rave_from_stream(device_id)
dot.music.play()

Z Bias

You can also add a constant bias to the z vector to allow for some controllable / random variation.

If you want to change this over time, you can use the on_new_frame callback. This is called whenever the chosen audio device (in this case the RAVE audio player) requests a new buffer and this function returns that buffer (so you can get the size, or do any custom analysis)

New random bias every frame

def on_new_frame(buffer=np.zeros(2048)):
    n= len(buffer)
    #Update a new random 
    dot.music.audio_outputs[0].z_bias = torch.randn(1,latent_dim,1)*0.05

dot.music.audio_outputs[rave_id].on_new_frame = on_new_frame

Oscillating bias at a given frequency

def sine_bias(frame_number, frequency=1, amplitude=1.0, phase=0, sample_rate=44100):
    t = frame_number / sample_rate
    value = amplitude * math.sin(2 * math.pi * frequency * t + phase)
    return value

self.ptr = 0
def on_new_frame(buffer=np.zeros(2048)):
    n= len(buffer)
    #update with oscilating bias
    val = sine_bias(self.ptr, 5, 0.4)
    dot.music.audio_outputs[0].z_bias = torch.tensor([val for n in range(latent_dim)]).reshape((1,latent_dim,1))
    self.ptr += n

dot.music.audio_outputs[rave_id] = on_new_frame

Generating Audio with MAGNet

MAGNet is a lightweight LSTM spectral model. You can train models here with as little as 30 seconds of audio in minutes.

This generates in realtime given a trained model the original source audio file / dataset (to use as an impulse)

dot.music.start_magnet_stream("models/magnet_wiley.pth", "../audio/Wiley.wav")