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50ee618 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | #!/usr/bin/env python3
"""
Generate a dancing skeleton video from any audio file.
Usage:
python generate.py --audio song.wav --out dance.mp4
"""
from __future__ import annotations
import argparse
import os
import subprocess
import librosa
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
import soundfile as sf
import torch
from tqdm import tqdm
from audio_features import AUDIO_SR, POSE_FPS, audio_to_features
from inference import generate_poses, load_checkpoint, resolve_checkpoint
SKELETON_EDGES = [
(0, 1), (1, 2), (2, 3), (3, 7), (0, 4), (4, 5), (5, 6), (6, 8), (9, 10),
(11, 12), (11, 13), (13, 15), (12, 14), (14, 16),
(11, 23), (12, 24), (23, 24), (23, 25), (24, 26), (25, 27), (26, 28),
(27, 29), (28, 30), (29, 31), (30, 32),
]
FACE = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
LEFT = {11, 13, 15, 23, 25, 27, 29, 31}
RIGHT = {12, 14, 16, 24, 26, 28, 30, 32}
def load_audio(path: str) -> np.ndarray:
"""Load any audio file as mono float32 at 32 kHz."""
data, sr = sf.read(path, dtype="float32", always_2d=True)
mono = data.mean(axis=1)
if sr != AUDIO_SR:
mono = librosa.resample(mono, orig_sr=sr, target_sr=AUDIO_SR)
return mono.astype(np.float32)
def _segment_color(a: int, b: int) -> str:
if a in FACE or b in FACE:
return "#a0c4ff"
if a in LEFT and b in LEFT:
return "#ffd6a5"
if a in RIGHT and b in RIGHT:
return "#ffadad"
return "#b9fbc0"
def render_skeleton_video(poses_xyz: np.ndarray, title: str, tmp_path: str) -> None:
"""Render a silent skeleton animation (720×1080)."""
T = poses_xyz.shape[0]
vel = np.diff(poses_xyz, axis=0)
energy = np.concatenate([[0], np.linalg.norm(vel, axis=-1).mean(axis=-1)])
energy = energy / (energy.max() + 1e-6)
xmin = poses_xyz[:, :, 0].min() - 0.15
xmax = poses_xyz[:, :, 0].max() + 0.15
ymin = poses_xyz[:, :, 1].min() - 0.1
ymax = poses_xyz[:, :, 1].max() + 0.1
fig = plt.figure(figsize=(5.4, 8), facecolor="#0d1117")
ax = fig.add_axes([0.05, 0.08, 0.90, 0.86])
ax.set_facecolor("#0d1117")
ax.set_xlim(xmin, xmax)
ax.set_ylim(-ymax, -ymin)
ax.set_aspect("equal")
ax.axis("off")
fig.text(0.5, 0.97, title, color="#e6edf3", fontsize=11,
ha="center", va="top", fontweight="bold")
bar_ax = fig.add_axes([0.05, 0.02, 0.90, 0.04])
bar_ax.set_xlim(0, T)
bar_ax.set_ylim(0, 1)
bar_ax.axis("off")
progress_bar = bar_ax.barh(0.5, 0, height=0.8, color="#3fb950", left=0)
time_txt = bar_ax.text(T * 0.5, 0.5, "0.0 s", color="white",
fontsize=7, ha="center", va="center")
scat = ax.scatter([], [], s=18, zorder=4)
lines = [ax.plot([], [], lw=2.2, solid_capstyle="round")[0] for _ in SKELETON_EDGES]
def init():
scat.set_offsets(np.empty((0, 2)))
for line in lines:
line.set_data([], [])
return [scat, *lines]
def update(t):
kpts = poses_xyz[t]
xs, ys = kpts[:, 0], -kpts[:, 1]
cols = []
for i in range(33):
if i in FACE:
cols.append("#a0c4ff")
elif i in LEFT:
cols.append("#ffd6a5")
elif i in RIGHT:
cols.append("#ffadad")
else:
cols.append("#b9fbc0")
scat.set_offsets(np.c_[xs, ys])
scat.set_color(cols)
for idx, (a, b) in enumerate(SKELETON_EDGES):
lines[idx].set_data([xs[a], xs[b]], [ys[a], ys[b]])
lines[idx].set_color(_segment_color(a, b))
lines[idx].set_alpha(0.85 + 0.15 * energy[t])
progress_bar[0].set_width(t + 1)
time_txt.set_text(f"{t / POSE_FPS:.1f} s / {T / POSE_FPS:.1f} s")
return [scat, *lines, progress_bar[0], time_txt]
ani = animation.FuncAnimation(
fig, update, frames=T, init_func=init,
interval=1000 / POSE_FPS, blit=True,
)
writer = animation.FFMpegWriter(
fps=POSE_FPS, bitrate=2000,
extra_args=["-vcodec", "libx264", "-pix_fmt", "yuv420p"],
)
ani.save(tmp_path, writer=writer, dpi=150)
plt.close(fig)
def mux_audio(video_path: str, audio_path: str, out_path: str, duration: float) -> None:
"""Combine silent video with the original audio track."""
subprocess.run([
"ffmpeg", "-y",
"-i", video_path,
"-i", audio_path,
"-c:v", "copy",
"-c:a", "aac", "-b:a", "192k",
"-t", str(duration),
"-shortest",
out_path,
], check=True, capture_output=True)
def main() -> None:
parser = argparse.ArgumentParser(description="Music → dance skeleton video")
parser.add_argument("--audio", required=True, help="Input audio file")
parser.add_argument("--checkpoint", default=None, help="Path to .pt weights")
parser.add_argument("--out", default="dance.mp4", help="Output video path")
args = parser.parse_args()
os.makedirs(os.path.dirname(args.out) or ".", exist_ok=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
ckpt_path = resolve_checkpoint(args.checkpoint)
print(f"Device: {device}")
print(f"Loading audio: {args.audio}")
waveform = load_audio(args.audio)
duration = len(waveform) / AUDIO_SR
print(f" Duration: {duration:.1f}s")
print("Extracting audio features…")
audio_feat = audio_to_features(waveform)
print(f" {audio_feat.shape[0]} frames @ {POSE_FPS} fps")
print(f"Loading model: {ckpt_path}")
model, ckpt = load_checkpoint(ckpt_path, device)
print(f" Epoch {ckpt['epoch']}")
print("Generating poses…")
poses_xyz = generate_poses(
model, audio_feat,
ckpt["x_mean"], ckpt["x_std"],
ckpt["y_mean"], ckpt["y_std"],
device,
)
title = os.path.splitext(os.path.basename(args.audio))[0]
tmp = args.out.replace(".mp4", "_silent.mp4")
print("Rendering video…")
render_skeleton_video(poses_xyz, title=title, tmp_path=tmp)
print("Mixing audio…")
mux_audio(tmp, args.audio, args.out, duration)
os.remove(tmp)
print(f"Done → {args.out}")
if __name__ == "__main__":
main()
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