culture_shock.py

# git clone https://github.com/NVlabs/stylegan2
import os
import numpy as np
from scipy.interpolate import interp1d
from scipy.io import wavfile
import matplotlib.pyplot as plt
import PIL.Image
import moviepy.editor

import dnnlib
import dnnlib.tflib as tflib
import pretrained_networks

audio = {}
fps = 60

# https://www.google.com/search?q=death+grips+black+google+download
for mp3_filename in [f for f in os.listdir('data') if f.endswith('.mp3')]:
    mp3_filename = f'data/{mp3_filename}'
    wav_filename = mp3_filename[:-4] + '.wav'
    if not os.path.exists(wav_filename):
        audio_clip = moviepy.editor.AudioFileClip(mp3_filename)
        audio_clip.write_audiofile(wav_filename, fps=44100, nbytes=2, codec='pcm_s16le')
    track_name = os.path.basename(wav_filename)[15:-5]
    rate, signal = wavfile.read(wav_filename)
    signal = np.mean(signal, axis=1) # to mono
    signal = np.abs(signal)
    seed = signal.shape[0]
    duration = signal.shape[0] / rate
    frames = int(np.ceil(duration * fps))
    samples_per_frame = signal.shape[0] / frames
    audio[track_name] = np.zeros(frames, dtype=signal.dtype)
    for frame in range(frames):
        start = int(round(frame * samples_per_frame))
        stop = int(round((frame + 1) * samples_per_frame))
        audio[track_name][frame] = np.mean(signal[start:stop], axis=0)
    audio[track_name] /= max(audio[track_name])

for track in sorted(audio.keys()):
    plt.figure(figsize=(8, 3))
    plt.title(track)
    plt.plot(audio[track])
    plt.savefig(f'data/{track}.png')

network_pkl = 'gdrive:networks/stylegan2-ffhq-config-f.pkl'
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)

Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
Gs_syn_kwargs = dnnlib.EasyDict()
Gs_syn_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_syn_kwargs.randomize_noise = False
Gs_syn_kwargs.minibatch_size = 4
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
w_avg = Gs.get_var('dlatent_avg')

def get_ws(n, frames, seed):
    filename = f'data/ws_{n}_{frames}_{seed}.npy'
    if not os.path.exists(filename):
        src_ws = np.random.RandomState(seed).randn(n, 512)
        ws = np.empty((frames, 512))
        for i in range(512):
            # FIXME: retarded
            x = np.linspace(0, 3*frames, 3*len(src_ws), endpoint=False)
            y = np.tile(src_ws[:, i], 3)
            x_ = np.linspace(0, 3*frames, 3*frames, endpoint=False)
            y_ = interp1d(x, y, kind='quadratic', fill_value='extrapolate')(x_)
            ws[:, i] = y_[frames:2*frames]
        np.save(filename, ws)
    else:
        ws = np.load(filename)
    return ws

def mix_styles(wa, wb, ivs):
    w = np.copy(wa)
    for i, v in ivs:
        w[i] = wa[i] * (1 - v) + wb[i] * v
    return w

def normalize_vector(v):
    return v * np.std(w_avg) / np.std(v) + np.mean(w_avg) - np.mean(v)

def render_frame(t):
    global base_index
    frame = np.clip(np.int(np.round(t * fps)), 0, frames - 1)
    base_index += base_speed * audio['Instrumental'][frame]**2
    base_w = base_ws[int(round(base_index)) % len(base_ws)]
    base_w = np.tile(base_w, (18, 1))
    psi = 0.5 + audio['FX'][frame] / 2
    base_w = w_avg + (base_w - w_avg) * psi
    mix_w = np.tile(mix_ws[frame], (18, 1))
    mix_w = w_avg + (mix_w - w_avg) * 0.75
    ranges = [range(0, 4), range(4, 8), range(8, 18)]
    values = [audio[track][frame] for track in ['Drums', 'E Drums', 'Synth']]
    w = mix_styles(base_w, mix_w, zip(ranges, values))
    w += mouth_open * audio['Vocal'][frame] * 1.5
    image = Gs.components.synthesis.run(np.stack([w]), **Gs_syn_kwargs)[0]
    image = PIL.Image.fromarray(image).resize((size, size), PIL.Image.LANCZOS)
    return np.array(image)
    
size = 1080
seconds = int(np.ceil(duration))
resolution = 10
base_frames = resolution * frames
base_ws = get_ws(seconds, base_frames, seed)
base_speed = base_frames / sum(audio['Instrumental']**2)
base_index = 0
mix_ws = get_ws(seconds, frames, seed + 1)
# https://rolux.org/media/stylegan2/vectors/mouth_ratio.npy
mouth_open = normalize_vector(-np.load('data/mouth_ratio.npy'))

mp4_filename = 'data/Culture Shock.mp4'
video_clip = moviepy.editor.VideoClip(render_frame, duration=duration)
audio_clip_i = moviepy.editor.AudioFileClip('data/Culture Shock (Instrumental).wav')
audio_clip_v = moviepy.editor.AudioFileClip('data/Culture Shock (Vocal).wav')
audio_clip = moviepy.editor.CompositeAudioClip([audio_clip_i, audio_clip_v])
video_clip = video_clip.set_audio(audio_clip)
video_clip.write_videofile(mp4_filename, fps=fps, codec='libx264', audio_codec='aac', bitrate='8M')

me to：

Setting up TensorFlow plugin "fused_bias_act.cu": Preprocessing... Loading... Done.
Setting up TensorFlow plugin "upfirdn_2d.cu": Preprocessing... Loading... Done.
Traceback (most recent call last):
File "culture_shock.py", line 107, in
base_speed = base_frames / sum(audio['Instrumental']**2)
KeyError: 'Instrumental'

@zncook: See my comment above!

@zncook: See my comment above!

Hi. Where download these audio files? Culture Shock (Drums).mp3, Culture Shock (E Drums).mp3, etc.

can I customize these audio files?

@zncook: as it says above: https://www.google.com/search?q=death+grips+black+google+download

Of course, you can provide your own files, split them with https://www.google.com/search?q=spleeter, etc.

what python you use? stylegun2 is for 3.6 or later but PIL is for 2.7 or less

what python you use? stylegun2 is for 3.6 or later but PIL is for 2.7 or less

@nikitatishin5 install pillow instead of PIL and use python 3.6

if I want to combine another parameter to the drums for example, how should I do it? I already downloaded other vectors but I can't seem to make it work properly