For each frame: extract chunk, apply window, FFT, compute band energies.
| Feature | Formula | Controls |
| --------- | --------- | ---------- |
rms | sqrt(mean(chunk²)) | Overall loudness/energy |
sub..hi | sqrt(mean(band_magnitudes²)) | Per-band energy |
centroid | sum(freq*mag) / sum(mag) | Brightness/timbre |
flatness | geomean(mag) / mean(mag) | Noise vs tone |
flux | sum(max(0, mag - prev_mag)) | Transient strength |
sub_r..hi_r | band / sum(all_bands) | Spectral shape (volume-independent) |
cent_d | abs(gradient(centroid)) | Timbral change rate |
beat | Flux peak detection | Binary beat onset |
bdecay | Exponential decay from beats | Smooth beat pulse (0→1→0) |
Band ratios are critical — they decouple spectral shape from volume, so a quiet bass section and a loud bass section both read as "bassy" rather than just "loud" vs "quiet".
Smoothing
EMA prevents visual jitter:
`python
def ema(arr, alpha):
out = np.empty_like(arr); out[0] = arr[0]
for i in range(1, len(arr)):
out[i] = alpha arr[i] + (1 - alpha) out[i-1]
return out
Slow-moving features (alpha=0.12): centroid, flatness, band ratios, cent_d
Fast-moving features (alpha=0.3): rms, flux, raw bands
`
Beat Detection
`python
flux_smooth = np.convolve(flux, np.ones(5)/5, mode="same")
peaks, _ = signal.find_peaks(flux_smooth, height=0.15, distance=fps//5, prominence=0.05)
beat = np.zeros(n_frames)
bdecay = np.zeros(n_frames, dtype=np.float32)
for p in peaks:
beat[p] = 1.0
for d in range(fps // 2):
if p + d < n_frames:
bdecay[p + d] = max(bdecay[p + d], math.exp(-d * 2.5 / (fps // 2)))
`
bdecay gives smooth 0→1→0 pulse per beat, decaying over ~0.5s. Use for flash/glitch/mirror triggers.
Normalization
After computing all frames, normalize each feature to 0-1:
`python
for k in features:
a = features[k]
lo, hi = a.min(), a.max()
features[k] = (a - lo) / (hi - lo + 1e-10)
`
Video Sampling
Frame Extraction
`python
Method 1: ffmpeg pipe (memory efficient)
cmd = ["ffmpeg", "-i", input_video, "-f", "rawvideo", "-pix_fmt", "rgb24",
"-s", f"{target_w}x{target_h}", "-r", str(fps), "-"]
pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
frame_size = target_w target_h 3
for fi in range(n_frames):
raw = pipe.stdout.read(frame_size)
if len(raw) < frame_size: break
frame = np.frombuffer(raw, dtype=np.uint8).reshape(target_h, target_w, 3)
# process frame...
Method 2: OpenCV (if available)
cap = cv2.VideoCapture(input_video)
`
Luminance-to-Character Mapping
Convert video pixels to ASCII characters based on brightness:
`python
def frame_to_ascii(frame_rgb, grid, pal=PAL_DEFAULT):
"""Convert video frame to character + color arrays."""
rows, cols = grid.rows, grid.cols
# Resize frame to grid dimensions
small = np.array(Image.fromarray(frame_rgb).resize((cols, rows), Image.LANCZOS))
# Luminance
lum = (0.299 small[:,:,0] + 0.587 small[:,:,1] + 0.114 * small[:,:,2]) / 255.0
# Map to chars
chars = val2char(lum, lum > 0.02, pal)
# Colors: use source pixel colors, scaled by luminance for visibility
colors = np.clip(small np.clip(lum[:,:,None] 1.5 + 0.3, 0.3, 1), 0, 255).astype(np.uint8)
return chars, colors
`
Edge-Weighted Character Mapping
Use edge detection for more detail in contour regions:
`python
def frame_to_ascii_edges(frame_rgb, grid, pal=PAL_DEFAULT, edge_pal=PAL_BOX):
gray = np.mean(frame_rgb, axis=2)
small_gray = resize(gray, (grid.rows, grid.cols))
lum = small_gray / 255.0
# Sobel edge detection
gx = np.abs(small_gray[:, 2:] - small_gray[:, :-2])
gy = np.abs(small_gray[2:, :] - small_gray[:-2, :])
edge = np.zeros_like(small_gray)
edge[:, 1:-1] += gx; edge[1:-1, :] += gy
edge = np.clip(edge / edge.max(), 0, 1)
# Edge regions get box drawing chars, flat regions get brightness chars
is_edge = edge > 0.15
chars = val2char(lum, lum > 0.02, pal)
edge_chars = val2char(edge, is_edge, edge_pal)
chars[is_edge] = edge_chars[is_edge]
return chars, colors
`
Motion Detection
Detect pixel changes between frames for motion-reactive effects:
`python
prev_frame = None
def compute_motion(frame):
global prev_frame
if prev_frame is None:
prev_frame = frame.astype(np.float32)
return np.zeros(frame.shape[:2])
diff = np.abs(frame.astype(np.float32) - prev_frame).mean(axis=2)
prev_frame = frame.astype(np.float32) 0.7 + prev_frame 0.3 # smoothed
return np.clip(diff / 30.0, 0, 1) # normalized motion map
`
Use motion map to drive particle emission, glitch intensity, or character density.
Video Feature Extraction
Per-frame features analogous to audio features, for driving effects:
`python
def analyze_video_frame(frame_rgb):
gray = np.mean(frame_rgb, axis=2)
return {
"brightness": gray.mean() / 255.0,
"contrast": gray.std() / 128.0,
"edge_density": compute_edge_density(gray),
"motion": compute_motion(frame_rgb).mean(),
"dominant_hue": compute_dominant_hue(frame_rgb),
"color_variance": compute_color_variance(frame_rgb),
}
`
Image Sequence
Static Image to ASCII
Same as single video frame conversion. For animated sequences:
`python
import glob
frames = sorted(glob.glob("frames/*.png"))
for fi, path in enumerate(frames):
img = np.array(Image.open(path).resize((VW, VH)))
chars, colors = frame_to_ascii(img, grid, pal)
`
Image as Texture Source
Use an image as a background texture that effects modulate:
`python
def load_texture(path, grid):
img = np.array(Image.open(path).resize((grid.cols, grid.rows)))
lum = np.mean(img, axis=2) / 255.0
return lum, img # luminance for char mapping, RGB for colors
`
Text / Lyrics
SRT Parsing
`python
import re
def parse_srt(path):
"""Returns [(start_sec, end_sec, text), ...]"""
entries = []
with open(path) as f:
content = f.read()
blocks = content.strip().split("\n\n")
for block in blocks:
lines = block.strip().split("\n")
if len(lines) >= 3:
times = lines[1]
m = re.match(r"(\d+):(\d+):(\d+),(\d+) --> (\d+):(\d+):(\d+),(\d+)", times)
if m:
g = [int(x) for x in m.groups()]
start = g[0]3600 + g[1]60 + g[2] + g[3]/1000
end = g[4]3600 + g[5]60 + g[6] + g[7]/1000
text = " ".join(lines[2:])
entries.append((start, end, text))
return entries
`
Lyrics Display Modes
- - Typewriter: characters appear left-to-right over the time window
- - Fade-in: whole line fades from dark to bright
- - Flash: appear instantly on beat, fade out
- - Scatter: characters start at random positions, converge to final position
- - Wave: text follows a sine wave path
`python
def lyrics_typewriter(ch, co, text, row, col, t, t_start, t_end, color):
"""Reveal characters progressively over time window."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
n_visible = int(len(text) * progress)
stamp(ch, co, text[:n_visible], row, col, color)
`
Generative (No Input)
For pure generative ASCII art, the "features" dict is synthesized from time:
`python
def synthetic_features(t, bpm=120):
"""Generate audio-like features from time alone."""
beat_period = 60.0 / bpm
beat_phase = (t % beat_period) / beat_period
return {
"rms": 0.5 + 0.3 math.sin(t 0.5),
"bass": 0.5 + 0.4 math.sin(t 2 * math.pi / beat_period),
"sub": 0.3 + 0.3 math.sin(t 0.8),
"mid": 0.4 + 0.3 math.sin(t 1.3),
"hi": 0.3 + 0.2 math.sin(t 2.1),
"cent": 0.5 + 0.2 math.sin(t 0.3),
"flat": 0.4,
"flux": 0.3 + 0.2 math.sin(t 3),
"beat": 1.0 if beat_phase < 0.05 else 0.0,
"bdecay": max(0, 1.0 - beat_phase * 4),
# ratios
"sub_r": 0.2, "bass_r": 0.25, "lomid_r": 0.15,
"mid_r": 0.2, "himid_r": 0.12, "hi_r": 0.08,
"cent_d": 0.1,
}
`
TTS Integration
For narrated videos (testimonials, quotes, storytelling), generate speech audio per segment and mix with background music.
ElevenLabs Voice Generation
`python
import requests, time, os
def generate_tts(text, voice_id, api_key, output_path, model="eleven_multilingual_v2"):
"""Generate TTS audio via ElevenLabs API. Streams response to disk."""
# Skip if already generated (idempotent re-runs)
if os.path.exists(output_path) and os.path.getsize(output_path) > 1000:
return
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
headers = {"xi-api-key": api_key, "Content-Type": "application/json"}
data = {
"text": text,
"model_id": model,
"voice_settings": {
"stability": 0.65,
"similarity_boost": 0.80,
"style": 0.15,
"use_speaker_boost": True,
},
}
resp = requests.post(url, json=data, headers=headers, stream=True)
resp.raise_for_status()
with open(output_path, "wb") as f:
for chunk in resp.iter_content(chunk_size=4096):
f.write(chunk)
time.sleep(0.3) # rate limit: avoid 429s on batch generation
`
Voice settings notes:
- -
stability 0.65 gives natural variation without drift. Lower (0.3-0.5) for more expressive reads, higher (0.7-0.9) for monotone/narration.
- -
similarity_boost 0.80 keeps it close to the voice profile. Lower for more generic sound.
- -
style 0.15 adds slight stylistic variation. Keep low (0-0.2) for straightforward reads.
- -
use_speaker_boost True improves clarity at the cost of slightly more processing time. Voice Pool
ElevenLabs has ~20 built-in voices. Use multiple voices for variety across quotes. Reference pool:
`python
VOICE_POOL = [
("JBFqnCBsd6RMkjVDRZzb", "George"),
("nPczCjzI2devNBz1zQrb", "Brian"),
("pqHfZKP75CvOlQylNhV4", "Bill"),
("CwhRBWXzGAHq8TQ4Fs17", "Roger"),
("cjVigY5qzO86Huf0OWal", "Eric"),
("onwK4e9ZLuTAKqWW03F9", "Daniel"),
("IKne3meq5aSn9XLyUdCD", "Charlie"),
("iP95p4xoKVk53GoZ742B", "Chris"),
("bIHbv24MWmeRgasZH58o", "Will"),
("TX3LPaxmHKxFdv7VOQHJ", "Liam"),
("SAz9YHcvj6GT2YYXdXww", "River"),
("EXAVITQu4vr4xnSDxMaL", "Sarah"),
("Xb7hH8MSUJpSbSDYk0k2", "Alice"),
("pFZP5JQG7iQjIQuC4Bku", "Lily"),
("XrExE9yKIg1WjnnlVkGX", "Matilda"),
("FGY2WhTYpPnrIDTdsKH5", "Laura"),
("SOYHLrjzK2X1ezoPC6cr", "Harry"),
("hpp4J3VqNfWAUOO0d1Us", "Bella"),
("N2lVS1w4EtoT3dr4eOWO", "Callum"),
("cgSgspJ2msm6clMCkdW9", "Jessica"),
("pNInz6obpgDQGcFmaJgB", "Adam"),
]
`
Voice Assignment
Shuffle deterministically so re-runs produce the same voice mapping:
`python
import random as _rng
def assign_voices(n_quotes, voice_pool, seed=42):
"""Assign a different voice to each quote, cycling if needed."""
r = _rng.Random(seed)
ids = [v[0] for v in voice_pool]
r.shuffle(ids)
return [ids[i % len(ids)] for i in range(n_quotes)]
`
Pronunciation Control
TTS text must be separate from display text. The display text has line breaks for visual layout; the TTS text is a flat sentence with phonetic fixes.
Common fixes:
- - Brand names: spell phonetically ("Nous" -> "Noose", "nginx" -> "engine-x")
- - Abbreviations: expand ("API" -> "A P I", "CLI" -> "C L I")
- - Technical terms: add phonetic hints
- - Punctuation for pacing: periods create pauses, commas create slight pauses
`python
Display text: line breaks control visual layout
QUOTES = [
("It can do far more than the Claws,\nand you don't need to buy a Mac Mini.\nNous Research has a winner here.", "Brian Roemmele"),
]
TTS text: flat, phonetically corrected for speech
QUOTES_TTS = [
"It can do far more than the Claws, and you don't need to buy a Mac Mini. Noose Research has a winner here.",
]
Keep both arrays in sync -- same indices
`
Audio Pipeline
1. Generate individual TTS clips (MP3 per quote, skipping existing)
2. Convert each to WAV (mono, 22050 Hz) for duration measurement and concatenation
3. Calculate timing: intro pad + speech + gaps + outro pad = target duration
4. Concatenate into single TTS track with silence padding
5. Mix with background music
`python
def build_tts_track(tts_clips, target_duration, intro_pad=5.0, outro_pad=4.0):
"""Concatenate TTS clips with calculated gaps, pad to target duration.
Returns:
timing: list of (start_time, end_time, quote_index) tuples
"""
sr = 22050
# Convert MP3s to WAV for duration and sample-level concatenation
durations = []
for clip in tts_clips:
wav = clip.replace(".mp3", ".wav")
subprocess.run(
["ffmpeg", "-y", "-i", clip, "-ac", "1", "-ar", str(sr),
"-sample_fmt", "s16", wav],
capture_output=True, check=True)
result = subprocess.run(
["ffprobe", "-v", "error", "-show_entries", "format=duration",
"-of", "csv=p=0", wav],
capture_output=True, text=True)
durations.append(float(result.stdout.strip()))
# Calculate gap to fill target duration
total_speech = sum(durations)
n_gaps = len(tts_clips) - 1
remaining = target_duration - total_speech - intro_pad - outro_pad
gap = max(1.0, remaining / max(1, n_gaps))
# Build timing and concatenate samples
timing = []
t = intro_pad
all_audio = [np.zeros(int(sr * intro_pad), dtype=np.int16)]
for i, dur in enumerate(durations):
wav = tts_clips[i].replace(".mp3", ".wav")
with wave.open(wav) as wf:
samples = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16)
timing.append((t, t + dur, i))
all_audio.append(samples)
t += dur
if i < len(tts_clips) - 1:
all_audio.append(np.zeros(int(sr * gap), dtype=np.int16))
t += gap
all_audio.append(np.zeros(int(sr * outro_pad), dtype=np.int16))
# Pad or trim to exactly target_duration
full = np.concatenate(all_audio)
target_samples = int(sr * target_duration)
if len(full) < target_samples:
full = np.pad(full, (0, target_samples - len(full)))
else:
full = full[:target_samples]
# Write concatenated TTS track
with wave.open("tts_full.wav", "w") as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(sr)
wf.writeframes(full.tobytes())
return timing
`
Audio Mixing
Mix TTS (center) with background music (wide stereo, low volume). The filter chain:
1. TTS mono duplicated to both channels (centered)
2. BGM loudness-normalized, volume reduced to 15%, stereo widened with extrastereo
3. Mixed together with dropout transition for smooth endings
`python
def mix_audio(tts_path, bgm_path, output_path, bgm_volume=0.15):
"""Mix TTS centered with BGM panned wide stereo."""
filter_complex = (
# TTS: mono -> stereo center
"[0:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=mono,"
"pan=stereo|c0=c0|c1=c0[tts];"
# BGM: normalize loudness, reduce volume, widen stereo
f"[1:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo,"
f"loudnorm=I=-16:TP=-1.5:LRA=11,"
f"volume={bgm_volume},"
f"extrastereo=m=2.5[bgm];"
# Mix with smooth dropout at end
"[tts][bgm]amix=inputs=2:duration=longest:dropout_transition=3,"
"aformat=sample_fmts=s16:sample_rates=44100:channel_layouts=stereo[out]"
)
cmd = [
"ffmpeg", "-y",
"-i", tts_path,
"-i", bgm_path,
"-filter_complex", filter_complex,
"-map", "[out]", output_path,
]
subprocess.run(cmd, capture_output=True, check=True)
`
Per-Quote Visual Style
Cycle through visual presets per quote for variety. Each preset defines a background effect, color scheme, and text color:
`python
QUOTE_STYLES = [
{"hue": 0.08, "accent": 0.7, "bg": "spiral", "text_rgb": (255, 220, 140)}, # warm gold
{"hue": 0.55, "accent": 0.6, "bg": "rings", "text_rgb": (180, 220, 255)}, # cool blue
{"hue": 0.75, "accent": 0.7, "bg": "wave", "text_rgb": (220, 180, 255)}, # purple
{"hue": 0.35, "accent": 0.6, "bg": "matrix", "text_rgb": (140, 255, 180)}, # green
{"hue": 0.95, "accent": 0.8, "bg": "fire", "text_rgb": (255, 180, 160)}, # red/coral
{"hue": 0.12, "accent": 0.5, "bg": "interference", "text_rgb": (255, 240, 200)}, # amber
{"hue": 0.60, "accent": 0.7, "bg": "tunnel", "text_rgb": (160, 210, 255)}, # cyan
{"hue": 0.45, "accent": 0.6, "bg": "aurora", "text_rgb": (180, 255, 220)}, # teal
]
style = QUOTE_STYLES[quote_index % len(QUOTE_STYLES)]
`
This guarantees no two adjacent quotes share the same look, even without randomness.
Typewriter Text Rendering
Display quote text character-by-character synced to speech progress. Recently revealed characters are brighter, creating a "just typed" glow:
`python
def render_typewriter(ch, co, lines, block_start, cols, progress, total_chars, text_rgb, t):
"""Overlay typewriter text onto character/color grids.
progress: 0.0 (nothing visible) to 1.0 (all text visible)."""
chars_visible = int(total_chars min(1.0, progress 1.2)) # slight overshoot for snappy feel
tr, tg, tb = text_rgb
char_count = 0
for li, line in enumerate(lines):
row = block_start + li
col = (cols - len(line)) // 2
for ci, c in enumerate(line):
if char_count < chars_visible:
age = chars_visible - char_count
bri_factor = min(1.0, 0.5 + 0.5 / (1 + age * 0.015)) # newer = brighter
hue_shift = math.sin(char_count 0.3 + t 2) * 0.05
stamp(ch, co, c, row, col + ci,
(int(min(255, tr bri_factor (1.0 + hue_shift))),
int(min(255, tg * bri_factor)),
int(min(255, tb bri_factor (1.0 - hue_shift)))))
char_count += 1
# Blinking cursor at insertion point
if progress < 1.0 and int(t * 3) % 2 == 0:
# Find cursor position (char_count == chars_visible)
cc = 0
for li, line in enumerate(lines):
for ci, c in enumerate(line):
if cc == chars_visible:
stamp(ch, co, "\u258c", block_start + li,
(cols - len(line)) // 2 + ci, (255, 220, 100))
return
cc += 1
`
Feature Analysis on Mixed Audio
Run the standard audio analysis (FFT, beat detection) on the final mixed track so visual effects react to both TTS and music:
`python
Analyze mixed_final.wav (not individual tracks)
features = analyze_audio("mixed_final.wav", fps=24)
`
Visuals pulse with both the music beats and the speech energy.
Audio-Video Sync Verification
After rendering, verify that visual beat markers align with actual audio beats. Drift accumulates from frame timing errors, ffmpeg concat boundaries, and rounding in fi / fps.
Beat Timestamp Extraction
`python
def extract_beat_timestamps(features, fps, threshold=0.5):
"""Extract timestamps where beat feature exceeds threshold."""
beat = features["beat"]
timestamps = []
for fi in range(len(beat)):
if beat[fi] > threshold:
timestamps.append(fi / fps)
return timestamps
def extract_visual_beat_timestamps(video_path, fps, brightness_jump=30):
"""Detect visual beats by brightness jumps between consecutive frames.
Returns timestamps where mean brightness increases by more than threshold."""
import subprocess
cmd = ["ffmpeg", "-i", video_path, "-f", "rawvideo", "-pix_fmt", "gray", "-"]
proc = subprocess.run(cmd, capture_output=True)
frames = np.frombuffer(proc.stdout, dtype=np.uint8)
# Infer frame dimensions from total byte count
n_pixels = len(frames)
# For 1080p: 1920*1080 pixels per frame
# Auto-detect from video metadata is more robust:
probe = subprocess.run(
["ffprobe", "-v", "error", "-select_streams", "v:0",
"-show_entries", "stream=width,height",
"-of", "csv=p=0", video_path],
capture_output=True, text=True)
w, h = map(int, probe.stdout.strip().split(","))
ppf = w * h # pixels per frame
n_frames = n_pixels // ppf
frames = frames[:n_frames * ppf].reshape(n_frames, ppf)
means = frames.mean(axis=1)
timestamps = []
for i in range(1, len(means)):
if means[i] - means[i-1] > brightness_jump:
timestamps.append(i / fps)
return timestamps
`
Sync Report
`python
def sync_report(audio_beats, visual_beats, tolerance_ms=50):
"""Compare audio beat timestamps to visual beat timestamps.
Args:
audio_beats: list of timestamps (seconds) from audio analysis
visual_beats: list of timestamps (seconds) from video brightness analysis
tolerance_ms: max acceptable drift in milliseconds
Returns:
dict with matched/unmatched/drift statistics
"""
tolerance = tolerance_ms / 1000.0
matched = []
unmatched_audio = []
unmatched_visual = list(visual_beats)
for at in audio_beats:
best_match = None
best_delta = float("inf")
for vt in unmatched_visual:
delta = abs(at - vt)
if delta < best_delta:
best_delta = delta
best_match = vt
if best_match is not None and best_delta < tolerance:
matched.append({"audio": at, "visual": best_match, "drift_ms": best_delta * 1000})
unmatched_visual.remove(best_match)
else:
unmatched_audio.append(at)
drifts = [m["drift_ms"] for m in matched]
return {
"matched": len(matched),
"unmatched_audio": len(unmatched_audio),
"unmatched_visual": len(unmatched_visual),
"total_audio_beats": len(audio_beats),
"total_visual_beats": len(visual_beats),
"mean_drift_ms": np.mean(drifts) if drifts else 0,
"max_drift_ms": np.max(drifts) if drifts else 0,
"p95_drift_ms": np.percentile(drifts, 95) if len(drifts) > 1 else 0,
}
Usage:
audio_beats = extract_beat_timestamps(features, fps=24)
visual_beats = extract_visual_beat_timestamps("output.mp4", fps=24)
report = sync_report(audio_beats, visual_beats)
print(f"Matched: {report['matched']}/{report['total_audio_beats']} beats")
print(f"Mean drift: {report['mean_drift_ms']:.1f}ms, Max: {report['max_drift_ms']:.1f}ms")
Target: mean drift < 20ms, max drift < 42ms (1 frame at 24fps)
`
Common Sync Issues
| Symptom | Cause | Fix |
| --------- | ------- | ----- |
| Consistent late visual beats | ffmpeg concat adds frames at boundaries | Use -vsync cfr flag; pad segments to exact frame count |
| Drift increases over time | Floating-point accumulation in t = fi / fps | Use integer frame counter, compute t fresh each frame |
| Random missed beats | Beat threshold too high / feature smoothing too aggressive | Lower threshold; reduce EMA alpha for beat feature |
| Beats land on wrong frame | Off-by-one in frame indexing | Verify: frame 0 = t=0, frame 1 = t=1/fps (not t=0) |