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Monorepo for Aesthetic.Computer aesthetic.computer
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123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149#!/usr/bin/env python3"""Measure the Flower Eater primary take (6992837952212569350).
What comes out (all into analysis/): tempo.json — onset-derived tempo: global, windowed drift, verdict melody.json — per-word pyin: median f0, note name, cents off, degree transcript.json — the curated word stream (from whisper-raw/, -ml 1 tokens merged back into words with timestamps)
Run with pop/.venv: ../../.venv/bin/python3 analyze.py"""import json, os, reimport numpy as npimport librosa
HERE = os.path.dirname(os.path.abspath(__file__))SRC = os.path.join(HERE, "..", "source", "flwe-6992837952212569350.wav")ANA = os.path.join(HERE, "..", "analysis")RAW = os.path.join(ANA, "whisper-raw", "flwe-6992837952212569350.json")
SONG = (18.0, 80.0) # the sung piece inside the take (spoken intro/outro outside)
# ---------------------------------------------------------------- transcriptdef load_words(): """Merge whisper's -ml 1 sub-word tokens back into words.""" segs = json.load(open(RAW))["transcription"] toks = [] for s in segs: txt = s["text"] if not txt.strip() or txt.strip() in ".,!?": continue toks.append((s["offsets"]["from"] / 1000, s["offsets"]["to"] / 1000, txt)) words = [] for t0, t1, txt in toks: # a token that doesn't start with a space continues the previous word if words and not txt.startswith(" "): w = words[-1] w["word"] += txt.strip("'") if txt.strip() == "'" else txt w["end"] = t1 else: words.append({"word": txt.strip(), "start": round(t0, 2), "end": round(t1, 2)}) for w in words: w["end"] = round(w["end"], 2) return words
# ---------------------------------------------------------------- audioy, sr = librosa.load(SRC, sr=48000, mono=True)
def clip(t0, t1): return y[int(t0 * sr):int(t1 * sr)]
# ---------------------------------------------------------------- temposong = clip(*SONG)oenv = librosa.onset.onset_strength(y=song, sr=sr, hop_length=512)tempo_global = float(librosa.feature.tempo(onset_envelope=oenv, sr=sr, hop_length=512)[0])onsets = librosa.onset.onset_detect(onset_envelope=oenv, sr=sr, hop_length=512, units="time")
# windowed tempo → drift (10 s windows, 5 s hop)win, hop = 10.0, 5.0frames_per_sec = sr / 512drift = []t = 0.0dur = len(song) / srwhile t + win <= dur: seg = oenv[int(t * frames_per_sec):int((t + win) * frames_per_sec)] bpm = float(librosa.feature.tempo(onset_envelope=seg, sr=sr, hop_length=512)[0]) drift.append({"t": round(SONG[0] + t, 1), "bpm": round(bpm, 1)}) t += hopbpms = [d["bpm"] for d in drift]# fold octave errors toward the median before judging driftmed = float(np.median(bpms))folded = [b / 2 if b > med * 1.5 else b * 2 if b < med / 1.5 else b for b in bpms]med = float(np.median(folded))
tempo = { "source": os.path.basename(SRC), "song_region_sec": list(SONG), "method": "librosa onset_strength -> feature.tempo, global + 10s windows (5s hop), octave-folded", "bpm_global": round(tempo_global, 1), "bpm_median": round(med, 1), "bpm_windows": drift, "bpm_windows_folded": [round(b, 1) for b in folded], "drift_range": [round(min(folded), 1), round(max(folded), 1)], "verdict": "rubato" if (max(folded) - min(folded)) > 0.12 * med else "steady", "onset_count": int(len(onsets)),}
# ---------------------------------------------------------------- melody (pyin per word)words = load_words()f0, vflag, vprob = librosa.pyin( y, sr=sr, fmin=librosa.note_to_hz("C2"), fmax=librosa.note_to_hz("C6"), frame_length=2048, hop_length=512,)times = librosa.times_like(f0, sr=sr, hop_length=512)
def word_pitch(w): m = (times >= w["start"]) & (times <= max(w["end"], w["start"] + 0.08)) fs = f0[m] fs = fs[~np.isnan(fs)] if len(fs) < 2: return None hz = float(np.median(fs)) midi = librosa.hz_to_midi(hz) note = librosa.midi_to_note(round(midi)) cents = round((midi - round(midi)) * 100) return {"hz": round(hz, 1), "note": note, "cents": cents}
melody = []for w in words: sung = SONG[0] <= w["start"] <= SONG[1] p = word_pitch(w) entry = {**w, "sung": sung} if p: entry.update(p) melody.append(entry)
# key: chroma over the sung region, voiced pyin histogramsung_notes = [m["note"][:-1] for m in melody if m["sung"] and "note" in m]hist = {}for n in sung_notes: hist[n] = hist.get(n, 0) + 1
chroma = librosa.feature.chroma_cqt(y=song, sr=sr)chroma_mean = chroma.mean(axis=1)pcs = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]chroma_rank = sorted(zip(pcs, chroma_mean.round(3).tolist()), key=lambda x: -x[1])
out = { "source": os.path.basename(SRC), "method": "librosa.pyin C2-C6 over full take, median f0 per whisper word window", "words": melody, "pitch_class_histogram": hist, "chroma_cqt_ranked": chroma_rank,}
os.makedirs(ANA, exist_ok=True)json.dump(tempo, open(os.path.join(ANA, "tempo.json"), "w"), indent=1)json.dump(out, open(os.path.join(ANA, "melody.json"), "w"), indent=1)json.dump({"source": os.path.basename(SRC), "words": words, "song_region_sec": list(SONG)}, open(os.path.join(ANA, "transcript.json"), "w"), indent=1)
print("tempo:", json.dumps({k: tempo[k] for k in ("bpm_global", "bpm_median", "drift_range", "verdict")}))print("pitch classes:", json.dumps(hist))print("chroma:", chroma_rank[:6])for m in melody: if m["sung"] and "note" in m: print(f"{m['start']:7.2f} {m['word']:<14} {m['note']:>4} {m['cents']:+4d}c {m['hz']}Hz")