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feat(skills): unify bgm-to-video flows into music-to-video
Replace bgm-to-video, bgm-to-video-new, bgm-to-video-refactor, and the standalone beat-sync/montage skills with a single music-to-video skill. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Miao Yang
co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
0dcf914e14
commit
a34d3dba4d
@@ -0,0 +1,531 @@
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#!/usr/bin/env python3
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"""Beat-grid + drum/event analysis engine for music-to-video.
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Turns a BGM track directly into a deterministic `audiomap.json` — the music skeleton
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that the Director and Builder hang visuals on. It merges:
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- a reliable tempo + beat grid + downbeat (librosa beat tracker),
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- metrical position per event (strong / weak / syncopated / off-grid) over a 16th-note bar grid,
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- drum-element classification (kick / snare / hihat / perc) via band-split,
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- special audio events (riser / glitch / crash-impact / hard-stop silence),
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- an energy narrative (audio-driven energy phases + builds + key moments; the Music
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Reader names the sections — no fixed Intro/Build/Drop/Outro template),
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- a phrase layer + per-section density budgets for visual planning.
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Output is the canonical `audiomap.json` documented in the skill.
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Usage:
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python3 analyze-beatgrid.py track.mp3 -o audiomap.json
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python3 analyze-beatgrid.py track.mp3 --print # also print a readable brief
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Deps: ffmpeg/ffprobe on PATH + librosa, numpy, soundfile (band-split heuristics,
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no learned models / no madmom).
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"""
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from __future__ import annotations
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import argparse
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import json
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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import librosa
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import numpy as np
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import soundfile as sf
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SR = 22050
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HOP = 512 # ~23 ms frames
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AUDIOMAP_VERSION = 2
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# ── decode ────────────────────────────────────────────────────────────────
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def load_audio(path: str) -> tuple[np.ndarray, int, float]:
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"""Decode any ffmpeg-readable file to mono float32 @ SR via a temp wav."""
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
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wav = tmp.name
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subprocess.run(
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["ffmpeg", "-y", "-i", path, "-ac", "1", "-ar", str(SR), wav],
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capture_output=True, check=True,
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)
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y, sr = sf.read(wav, dtype="float32")
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Path(wav).unlink(missing_ok=True)
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if y.ndim > 1:
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y = y.mean(axis=1)
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return y, sr, len(y) / sr
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# ── tempo + beat grid + downbeat phase ──────────────────────────────────────
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def beat_grid(y: np.ndarray, sr: int) -> dict:
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tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr, hop_length=HOP, units="frames")
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beats = librosa.frames_to_time(beat_frames, sr=sr, hop_length=HOP)
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return {"bpm": float(np.atleast_1d(tempo)[0]), "beats": beats, "beat_frames": beat_frames}
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def _norm_flux(band: np.ndarray) -> np.ndarray:
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"""Positive first-difference (onset flux) of a band-energy curve, normalized."""
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flux = np.maximum(0.0, np.diff(np.sqrt(band), prepend=band[:1]))
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return flux / (flux.max() + 1e-9)
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def band_energy_curves(y: np.ndarray, sr: int) -> dict:
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"""Per-frame band energy + per-band normalized onset flux (for drum typing)."""
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S = np.abs(librosa.stft(y, hop_length=HOP)) ** 2
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freqs = librosa.fft_frequencies(sr=sr)
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# Tight drum bands: kick fundamental, snare body, hihat sizzle. Narrow bands
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# keep one drum's transient from leaking into another's flux on a full mix.
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low = (freqs < 150) # kick
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mid = (freqs >= 150) & (freqs < 900) # snare body
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high = (freqs >= 5000) # hihat / cymbal
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e_low, e_mid, e_high = S[low].sum(axis=0), S[mid].sum(axis=0), S[high].sum(axis=0)
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return {
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"S": S,
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"low": e_low, "mid": e_mid, "high": e_high,
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"total": S.sum(axis=0) + 1e-9,
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"flux_low": _norm_flux(e_low), "flux_mid": _norm_flux(e_mid), "flux_high": _norm_flux(e_high),
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"flatness": librosa.feature.spectral_flatness(S=np.sqrt(S))[0],
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"centroid": librosa.feature.spectral_centroid(S=np.sqrt(S), sr=sr)[0],
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"n": S.shape[1],
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}
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def downbeat_phase(beat_frames: np.ndarray, bc: dict, beats_per_bar: int = 4) -> int:
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"""Pick the bar phase whose beats carry the most KICK (low-band) energy."""
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kick = bc["low"] / bc["total"]
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best_p, best_score = 0, -1.0
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for p in range(beats_per_bar):
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idx = [bf for i, bf in enumerate(beat_frames) if (i - p) % beats_per_bar == 0]
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idx = [min(f, bc["n"] - 1) for f in idx]
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score = float(np.sum([kick[f] for f in idx])) if idx else 0.0
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if score > best_score:
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best_p, best_score = p, score
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return best_p
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# ── metrical position: strong / weak / syncopated / off-grid ─────────────────
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# 16-step bar grid (4 beats x 4 sixteenths). Strength by metrical weight.
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GRID_CLASS = {0: "strong", 8: "strong", 4: "weak", 12: "weak",
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2: "weak", 6: "weak", 10: "weak", 14: "weak"} # else (odd 16ths) -> syncopated
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def classify_metric(t: float, beats: np.ndarray, phase: int, bpb: int = 4) -> tuple:
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"""Return (grid_class, bar, beat_in_bar, step16) for a time t."""
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if len(beats) < 2:
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return "off-grid", -1, -1, -1
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i = int(np.searchsorted(beats, t) - 1)
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i = max(0, min(i, len(beats) - 2))
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beat_dur = beats[i + 1] - beats[i]
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frac = (t - beats[i]) / max(beat_dur, 1e-6) # 0..1 within the beat
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sixteenth = int(round(frac * 4)) % 4 # nearest 16th in beat
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carry = 1 if round(frac * 4) >= 4 else 0
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beat_idx = i + carry
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beat_in_bar = (beat_idx - phase) % bpb # 0..3
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bar = (beat_idx - phase) // bpb
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step16 = beat_in_bar * 4 + sixteenth # 0..15
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# distance to the nearest 16th line (in seconds) → off-grid test
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nearest = beats[i] + (round(frac * 4) / 4) * beat_dur
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if abs(t - nearest) > 0.5 * (beat_dur / 4):
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return "off-grid", bar, beat_in_bar + 1, step16
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return GRID_CLASS.get(step16, "syncopated"), bar, beat_in_bar + 1, step16
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# ── drum classification (band-split heuristic) ──────────────────────────────
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def frame_at(t: float, sr: int, n: int) -> int:
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return min(int(round(t * sr / HOP)), n - 1)
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def classify_drum(t: float, bc: dict, sr: int) -> tuple:
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"""(drum_type, energy_norm, feel): which band's onset TRANSIENT dominates.
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Uses per-band normalized flux (relative transient strength), the standard
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way to separate kick (low) / snare (mid+noise) / hihat (high). Falls back to
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glitch for noisy non-harmonic bursts and perc when no band clearly leads.
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"""
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f = frame_at(t, sr, bc["n"])
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win = slice(max(0, f - 1), min(bc["n"], f + 2))
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fl = float(bc["flux_low"][win].max())
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fm = float(bc["flux_mid"][win].max())
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fh = float(bc["flux_high"][win].max())
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lo = float(bc["low"][win].mean()); md = float(bc["mid"][win].mean())
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hi = float(bc["high"][win].mean()); tot = float(bc["total"][win].mean())
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flat = float(bc["flatness"][win].mean())
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lr, mr, hr = lo / tot, md / tot, hi / tot
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fluxes = {"kick": fl, "snare": fm, "hihat": fh}
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lead = max(fluxes, key=fluxes.get)
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lead_val = fluxes[lead]
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if lead_val < 0.06: # no real transient → texture/perc
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drum = "glitch" if flat > 0.30 else "perc"
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elif lead == "snare" and flat > 0.30 and mr < 0.30:
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drum = "glitch" # mid-band but noisy & thin → scratch/glitch
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else:
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drum = lead
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# feel (frequency character)
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has_bot, has_top, has_mid = lr > 0.30, hr > 0.20, mr > 0.30
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feel = ("full" if has_bot and has_top and has_mid else
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"heavy" if has_bot and not has_top else
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"bright" if has_top and not has_bot else
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"intimate" if has_mid else "sparse")
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return drum, tot, feel
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# ── energy structure (RMS @1s) + sections + key moments + builds ────────────
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def energy_structure(y: np.ndarray, sr: int, dur: float, first_onset: float = 0.0) -> dict:
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rms = librosa.feature.rms(y=y, hop_length=sr)[0] # ~1s frames
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rms = rms / (rms.max() + 1e-9)
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norms = rms.tolist()
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def lvl(n):
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return "VOID" if n < 0.2 else "LOW" if n < 0.4 else "MEDIUM" if n < 0.65 else "HIGH"
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phases, cur, cs = [], None, 0
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for i, n in enumerate(norms):
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l = lvl(n)
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if l != cur:
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if cur:
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phases.append({"s": cs, "e": i, "lvl": cur})
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cur, cs = l, i
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if cur:
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phases.append({"s": cs, "e": len(norms), "lvl": cur})
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moments = []
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for i in range(1, len(norms)):
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d = norms[i] - norms[i - 1]
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if abs(d) > 0.12:
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moments.append({"t": i, "kind": "DROP" if d < 0 else "SURGE", "delta": round(d, 2)})
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moments.sort(key=lambda m: abs(m["delta"]), reverse=True)
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# hard stop: a HIGH→low cliff (a sudden stop) in the back third
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hard_stops = [m for m in moments if m["kind"] == "DROP" and m["t"] > dur * 0.6 and m["delta"] < -0.25]
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# NO forced Intro/Build/Drop/Outro template. The energy phases (audio-driven runs of
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# one energy level, variable count) are the raw structural blocks. The Music Reader
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# (LLM) decides the actual sections — count, boundaries, and free-form names — from
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# these phases + key_moments + rolls + hard_stops + phrases. Sections are
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# interpretation; only the timing they snap to is fact.
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phases_sec = []
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for p in phases:
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seg = norms[p["s"]:max(p["s"] + 1, p["e"])]
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phases_sec.append({
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"start": float(p["s"]),
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"end": float(min(p["e"], round(dur, 1))),
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"level": p["lvl"],
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"energy": round(float(np.mean(seg)) if seg else 0.0, 2),
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})
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return {"norms": [round(n, 2) for n in norms], "phases": phases_sec,
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"moments": moments[:8], "hard_stops": hard_stops}
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# ── rolls / fills (localized rapid-onset runs) ───────────────────────────────
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# A roll is where choreography should switch from discrete hits to a continuous /
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# cascading visual (per-letter cascade, stagger). Derived straight from the onset
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# stream — runs never overlap, so no dedup is needed (unlike a band-energy detector).
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ROLL_MIN_HITS = 4
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ROLL_CONT = 0.55 # × beat_dur: gap up to ~half a beat still keeps a run alive
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ROLL_ACCEPT = 0.42 # × beat_dur: mean spacing denser than an 8th note counts
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ROLL_DEDUP = 0.08 # seconds: merge onsets closer than a 32nd (double-trigger)
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def detect_rolls(events: list, beat_dur: float) -> list:
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"""Runs of >=4 onsets whose MEAN spacing is denser than an 8th note. Tuned so a
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full hihat/snare roll is captured as ONE span (not fragmented down to its tail),
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while a sparse groove stays out — validated against the golden 7.5-9.5s roll.
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The linear scan means runs never overlap (no LEGACY-style double-counting)."""
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cont = beat_dur * ROLL_CONT # max gap that keeps a run alive
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accept = beat_dur * ROLL_ACCEPT # max MEAN gap for a run to count
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# collapse onset double-triggers (two onsets < a 32nd apart = one hit) so a
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# held/sparse passage can't masquerade as a roll on a duplicated transient.
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ev = []
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for e in events:
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if ev and e["t"] - ev[-1]["t"] <= ROLL_DEDUP:
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if e.get("energy", 0) > ev[-1].get("energy", 0):
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ev[-1] = e
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continue
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ev.append(e)
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times = [e["t"] for e in ev]
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n = len(times)
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rolls, i = [], 0
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while i < n - 1:
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j = i
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while j + 1 < n and (times[j + 1] - times[j]) <= cont:
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j += 1
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if j - i + 1 >= ROLL_MIN_HITS:
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gaps = [times[k + 1] - times[k] for k in range(i, j)]
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if sum(gaps) / len(gaps) <= accept:
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t0, t1 = times[i], times[j]
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half = len(gaps) // 2
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accel = (half >= 1 and
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sum(gaps[half:]) / (len(gaps) - half) <
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sum(gaps[:half]) / half * 0.85)
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dcount: dict[str, int] = {}
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for e in ev[i:j + 1]:
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dcount[e["drum"]] = dcount.get(e["drum"], 0) + 1
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rolls.append({
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"start": round(t0, 3), "end": round(t1, 3),
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"dur_sec": round(t1 - t0, 3),
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"hits": j - i + 1,
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"rate_per_min": round((j - i) / max(t1 - t0, 1e-6) * 60),
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"kind": "accel-roll" if accel else ("sustained-fill" if t1 - t0 > 1.2 else "fill"),
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"drum": max(dcount, key=dcount.get),
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})
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i = j + 1
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return rolls
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# ── per-section spectral character (sustained "feel") ────────────────────────
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# Coarse, reliable bands for how a SECTION sounds (not the noisy per-second dump).
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# Distinct from a per-event `feel`, which is the transient color of a single hit.
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FEEL_BANDS = [("sub", 0, 60), ("bass", 60, 250), ("low_mid", 250, 800),
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("mid", 800, 2500), ("presence", 2500, 6000), ("air", 6000, 1e9)]
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def annotate_section_feel(bc: dict, sr: int, sections: list) -> None:
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"""Attach {character, bands} to each energy phase from its sustained band balance."""
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S, n = bc["S"], bc["n"]
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freqs = librosa.fft_frequencies(sr=sr)
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fps = sr / HOP
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masks = [(name, (freqs >= lo) & (freqs < hi)) for name, lo, hi in FEEL_BANDS]
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for s in sections:
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f0 = int(s["start"] * fps)
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f1 = min(max(f0 + 1, int(s["end"] * fps)), n)
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seg = S[:, f0:f1]
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if seg.shape[1] == 0 or s.get("energy", 0) < 0.15:
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s["feel"] = {"character": "sparse", "bands": []}
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continue
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en = {name: float(seg[m].sum()) for name, m in masks}
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tot = sum(en.values()) + 1e-9
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ratios = {name: en[name] / tot for name in en}
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present = sorted([bn for bn, r in ratios.items() if r > 0.12],
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key=lambda bn: -ratios[bn])
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lo = ratios["sub"] + ratios["bass"]
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hi = ratios["presence"] + ratios["air"]
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mid = ratios["low_mid"] + ratios["mid"]
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char = ("heavy" if lo > 0.5 else "bright" if hi > 0.45 else
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"full" if lo > 0.25 and hi > 0.25 else
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"warm" if mid > 0.5 else "sparse")
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s["feel"] = {"character": char, "bands": present}
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# ── audiomap enrichment: phrase layer + section density budgets ─────────────
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def round3(n: float) -> float:
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return round(float(n), 3)
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def derive_phrases(downbeats: list[float], phrase_bars: int, duration_sec: float) -> list:
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"""Group downbeats into phrase spans of `phrase_bars` bars."""
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phrases = []
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if not downbeats:
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return phrases
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index = 0
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for i in range(0, len(downbeats), phrase_bars):
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start = downbeats[i]
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next_idx = i + phrase_bars
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end = downbeats[next_idx] if next_idx < len(downbeats) else duration_sec
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phrases.append({
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"index": index,
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"start": round3(start),
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"end": round3(end),
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"bars": min(phrase_bars, len(downbeats) - i),
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})
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index += 1
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return phrases
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def count_in(times: list[float], start: float, end: float) -> int:
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return sum(1 for t in times if t >= start - 1e-6 and t < end - 1e-6)
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def derive_phase_budgets(timeline: dict) -> list:
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"""Attach an objective density read to each energy phase.
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Density is a fact (onsets-per-second + rolls). It is a hint for how much visual
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content a span can hold; it does not set timing or sections. The Music Reader uses
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these phases to decide the actual sections.
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"""
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phases = timeline.get("energy_phases", [])
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onset_times = [e["t"] for e in timeline.get("events", [])]
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rolls = timeline.get("rolls", [])
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hard_stops = timeline.get("hard_stops", [])
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out = []
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for s in phases:
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span = max(1e-6, float(s.get("end", 0)) - float(s.get("start", 0)))
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onsets = count_in(onset_times, s["start"], s["end"])
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ph_rolls = [
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{"start": r["start"], "end": r["end"], "kind": r["kind"], "drum": r["drum"]}
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for r in rolls
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if r["start"] < s["end"] - 1e-6 and r["end"] > s["start"] + 1e-6
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]
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ph_stops = [
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h["t"]
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for h in hard_stops
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if h["t"] >= s["start"] - 1e-6 and h["t"] < s["end"] + 1e-6
|
||||
]
|
||||
|
||||
if s.get("energy", 0) < 0.2 or onsets < 6:
|
||||
density = "sparse"
|
||||
elif onsets >= 18 or ph_rolls:
|
||||
density = "dense"
|
||||
else:
|
||||
density = "medium"
|
||||
|
||||
enriched = dict(s)
|
||||
enriched["onsets"] = onsets
|
||||
enriched["onsetRate"] = round(onsets / span, 1)
|
||||
enriched["rolls"] = ph_rolls
|
||||
enriched["hardStops"] = ph_stops
|
||||
enriched["density"] = density
|
||||
out.append(enriched)
|
||||
return out
|
||||
|
||||
|
||||
def finalize_audiomap(timeline: dict, phrase_bars: int = 4) -> dict:
|
||||
downbeats = timeline.get("grid", {}).get("downbeats_sec", [])
|
||||
duration_sec = timeline.get("audio", {}).get("duration_sec", 0)
|
||||
energy_phases = derive_phase_budgets(timeline)
|
||||
phrases = derive_phrases(downbeats, phrase_bars, duration_sec)
|
||||
return {
|
||||
"version": AUDIOMAP_VERSION,
|
||||
"phraseBars": phrase_bars,
|
||||
**timeline,
|
||||
"energy_phases": energy_phases,
|
||||
"phrases": phrases,
|
||||
}
|
||||
|
||||
|
||||
# ── main ────────────────────────────────────────────────────────────────────
|
||||
def analyze(path: str, phrase_bars: int = 4) -> dict:
|
||||
y, sr, dur = load_audio(path)
|
||||
bg = beat_grid(y, sr)
|
||||
bc = band_energy_curves(y, sr)
|
||||
phase = downbeat_phase(bg["beat_frames"], bc)
|
||||
beats = bg["beats"]
|
||||
downbeats = [float(beats[i]) for i in range(len(beats)) if (i - phase) % 4 == 0]
|
||||
|
||||
# onsets → events
|
||||
onset_t = librosa.onset.onset_detect(
|
||||
y=y, sr=sr, hop_length=HOP, units="time", backtrack=True
|
||||
)
|
||||
en_at = bc["total"]
|
||||
en_max = float(en_at.max()) + 1e-9
|
||||
events = []
|
||||
for t in onset_t:
|
||||
gclass, bar, bib, step16 = classify_metric(float(t), beats, phase)
|
||||
drum, energy, feel = classify_drum(float(t), bc, sr)
|
||||
f = frame_at(float(t), sr, bc["n"])
|
||||
events.append({
|
||||
"t": round(float(t), 3),
|
||||
"bar": int(bar), "beat_in_bar": int(bib), "step16": int(step16),
|
||||
"grid": gclass, "drum": drum,
|
||||
"energy": round(float(en_at[f]) / en_max, 2), "feel": feel,
|
||||
"special": None,
|
||||
})
|
||||
|
||||
first_onset = next((float(t) for t in onset_t if t >= 2.0), 0.0)
|
||||
es = energy_structure(y, sr, dur, first_onset)
|
||||
|
||||
# tag specials onto nearby events
|
||||
for hs in es["hard_stops"]:
|
||||
for e in events:
|
||||
if abs(e["t"] - hs["t"]) < 0.6:
|
||||
e["special"] = "hard_stop"
|
||||
# riser: events inside a 1.5s+ rising-energy run that precedes a SURGE
|
||||
surges = [m["t"] for m in es["moments"] if m["kind"] == "SURGE"]
|
||||
for st in surges:
|
||||
for e in events:
|
||||
if st - 2.0 <= e["t"] < st and e["special"] is None and e["drum"] in ("perc", "glitch"):
|
||||
e["special"] = "riser"
|
||||
|
||||
# rolls / fills + whether each leads straight into a surge/drop (cascade cue)
|
||||
beat_dur = float(np.median(np.diff(beats))) if len(beats) > 1 else 60.0 / max(bg["bpm"], 1e-6)
|
||||
rolls = detect_rolls(events, beat_dur)
|
||||
for r in rolls:
|
||||
r["leads_to"] = next((m["kind"] for m in es["moments"]
|
||||
if 0 <= m["t"] - r["end"] <= 1.2), None)
|
||||
# near-silent windows (= VOID energy phases) — convenience for "hold / breathe"
|
||||
silences = [{"start": p["start"], "end": p["end"]}
|
||||
for p in es["phases"] if p["level"] == "VOID"]
|
||||
# per-phase sustained spectral character (how each energy block FEELS)
|
||||
annotate_section_feel(bc, sr, es["phases"])
|
||||
|
||||
n_drum = {}
|
||||
for e in events:
|
||||
n_drum[e["drum"]] = n_drum.get(e["drum"], 0) + 1
|
||||
n_grid = {}
|
||||
for e in events:
|
||||
n_grid[e["grid"]] = n_grid.get(e["grid"], 0) + 1
|
||||
|
||||
summary = (f"{bg['bpm']:.0f} BPM · {len(beats)} beats / {len(downbeats)} bars · "
|
||||
f"{len(events)} events ({n_drum}) · {len(rolls)} rolls · "
|
||||
f"{len(es['phases'])} energy phases · {dur:.1f}s")
|
||||
|
||||
timeline = {
|
||||
"summary": summary,
|
||||
"audio": {"path": path, "duration_sec": round(dur, 3), "sr": sr},
|
||||
"tempo": {"bpm": round(bg["bpm"], 1), "beats_per_bar": 4,
|
||||
"downbeat_phase": phase, "n_beats": len(beats), "n_bars": len(downbeats)},
|
||||
"grid": {"beats_sec": [round(float(b), 3) for b in beats],
|
||||
"downbeats_sec": [round(b, 3) for b in downbeats]},
|
||||
"energy_phases": es["phases"],
|
||||
"key_moments": es["moments"],
|
||||
"hard_stops": es["hard_stops"],
|
||||
"rolls": rolls,
|
||||
"silences": silences,
|
||||
"stats": {"drum_counts": n_drum, "grid_counts": n_grid},
|
||||
"events": events,
|
||||
}
|
||||
return finalize_audiomap(timeline, phrase_bars)
|
||||
|
||||
|
||||
def print_brief(d: dict) -> None:
|
||||
print(f"\n{d['summary']}\n{'='*70}")
|
||||
print("ENERGY PHASES (audio-driven blocks; the Music Reader names the sections)")
|
||||
for s in d.get("energy_phases", []):
|
||||
feel = s.get("feel", {})
|
||||
bands = ",".join(feel.get("bands", []))
|
||||
print(f" {s['start']:5.1f}-{s['end']:5.1f}s {s.get('level', ''):6s} energy={s.get('energy')} "
|
||||
f"{feel.get('character', ''):6s} [{bands}] {s.get('density', '?')}")
|
||||
print("PHRASES")
|
||||
for p in d.get("phrases", []):
|
||||
print(f" #{p['index']} {p['start']:5.2f}-{p['end']:5.2f}s bars={p['bars']}")
|
||||
print("KEY MOMENTS")
|
||||
for m in d["key_moments"]:
|
||||
print(f" {m['t']:3d}s {m['kind']:5s} Δ{m['delta']:+.2f}")
|
||||
print(f"HARD STOPS: {[h['t'] for h in d['hard_stops']]}")
|
||||
print("ROLLS / FILLS")
|
||||
for r in d.get("rolls", []):
|
||||
lead = f" → {r['leads_to']}" if r.get("leads_to") else ""
|
||||
print(f" {r['start']:6.2f}-{r['end']:5.2f}s {r['hits']:2d} hits @ {r['rate_per_min']:4d}/min "
|
||||
f"{r['kind']:10s} ({r['drum']}){lead}")
|
||||
print(f"SILENCES: {[(s['start'], s['end']) for s in d.get('silences', [])]}")
|
||||
print(f"DRUM COUNTS: {d['stats']['drum_counts']} GRID: {d['stats']['grid_counts']}")
|
||||
print(f"\nEVENTS ({len(d['events'])}) [t · bar:beat · grid · drum · energy · special]")
|
||||
for e in d["events"]:
|
||||
sp = f" <{e['special']}>" if e["special"] else ""
|
||||
print(f" {e['t']:6.2f}s b{e['bar']}:{e['beat_in_bar']} {e['grid']:3s} "
|
||||
f"{e['drum']:6s} e={e['energy']:.2f} {e['feel']:8s}{sp}")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("audio")
|
||||
ap.add_argument("-o", "--out", default=None)
|
||||
ap.add_argument("--phrase-bars", type=int, default=4)
|
||||
ap.add_argument("--print", action="store_true", dest="do_print")
|
||||
a = ap.parse_args()
|
||||
d = analyze(a.audio, phrase_bars=a.phrase_bars)
|
||||
if a.out:
|
||||
Path(a.out).write_text(json.dumps(d, ensure_ascii=False, indent=2))
|
||||
dens = " ".join(f"{s.get('level', '?')}:{s.get('density', '?')}" for s in d.get("energy_phases", []))
|
||||
print(
|
||||
f"[analyze-beatgrid] wrote audiomap {a.out} · {len(d.get('energy_phases', []))} phases · density [{dens}]",
|
||||
file=sys.stderr,
|
||||
)
|
||||
if a.do_print or not a.out:
|
||||
print_brief(d)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user