feat(rec): audio analyzer and My Wave v1 engine (M4)
- migration 00006: track features and embeddings (pgvector, HNSW), analysis queue filled by a trigger on new tracks, wave sessions and impressions, recommender profiles, model versions - analyzer basic-1 (ADR-014): ffmpeg decode, three 30 s excerpts, 256-d timbre/rhythm embedding, tempo by onset autocorrelation, key, loudness, energy, danceability, acousticness, intro length; worker with SKIP LOCKED claims, retries and re-analysis on version change - wave engine v1: candidates (session/taste similarity, fresh, liked artists, rediscover, popular, exploration, seed), filters (dislikes, played in 3 h, already served), heuristic score, activity and mood fit from contexts.yaml with relaxing thresholds, MMR with one track per artist, exploration share, session vector updated by reactions and a change of course after three skips - POST /internal/wave/batch with the catalog index cached in memory and the session kept in Redis Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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"""Аудиопризнаки трека на numpy: тембр (эмбеддинг), темп, громкость, энергия, тональность.
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Версия `basic-1` — без нейросетевых моделей (ADR-014): работает на любом CPU без TensorFlow.
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Нейросетевые признаки Essentia (жанры, настроения, valence/arousal) — следующая версия анализатора;
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смена `ANALYZER_VER` ставит пересчёт.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import numpy as np
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from numpy.typing import NDArray
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ANALYZER_VER = "basic-1"
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SAMPLE_RATE = 22050
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N_FFT = 2048
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HOP = 512
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N_MELS = 64
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EMBEDDING_DIM = 256
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SEGMENT_S = 30.0
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Float = NDArray[np.float32]
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@dataclass(frozen=True)
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class Features:
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bpm: float | None
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bpm_confidence: float
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key: str | None
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scale: str | None
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loudness_lufs: float
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energy: float
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danceability: float
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valence: float
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acousticness: float
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intro_ms: int
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embedding: Float # (256,), L2-нормирован
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def segments(samples: Float, sr: int = SAMPLE_RATE, seconds: float = SEGMENT_S) -> list[Float]:
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"""Три фрагмента по 30 с — начало (после первых 10%), середина, конец; короткий трек — целиком."""
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n = int(seconds * sr)
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if len(samples) <= 3 * n:
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return [samples]
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total = len(samples)
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starts = [int(total * 0.1), total // 2 - n // 2, total - n - int(total * 0.05)]
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return [samples[s : s + n] for s in starts]
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def _stft_mag(x: Float) -> Float:
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if len(x) < N_FFT:
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x = np.pad(x, (0, N_FFT - len(x)))
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frames = 1 + (len(x) - N_FFT) // HOP
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idx = np.arange(N_FFT)[None, :] + HOP * np.arange(frames)[:, None]
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window = np.hanning(N_FFT).astype(np.float32)
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return np.abs(np.fft.rfft(x[idx] * window, axis=1)).astype(np.float32) # (frames, bins)
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def _mel_filterbank(sr: int = SAMPLE_RATE) -> Float:
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def hz_to_mel(f: NDArray[np.float64]) -> NDArray[np.float64]:
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return 2595.0 * np.log10(1.0 + f / 700.0)
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def mel_to_hz(m: NDArray[np.float64]) -> NDArray[np.float64]:
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return 700.0 * (10 ** (m / 2595.0) - 1.0)
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bins = N_FFT // 2 + 1
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freqs = np.linspace(0, sr / 2, bins)
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mel_points = mel_to_hz(np.linspace(hz_to_mel(np.array(30.0)), hz_to_mel(np.array(sr / 2)), N_MELS + 2))
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fb = np.zeros((N_MELS, bins), dtype=np.float32)
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for m in range(N_MELS):
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lo, mid, hi = mel_points[m], mel_points[m + 1], mel_points[m + 2]
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up = (freqs - lo) / (mid - lo)
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down = (hi - freqs) / (hi - mid)
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fb[m] = np.maximum(0, np.minimum(up, down))
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return fb
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_MEL = _mel_filterbank()
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# Профили тональностей Крумханзла.
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_MAJOR = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
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_MINOR = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17])
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_NOTES = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
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def _log_mel(mag: Float) -> Float:
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"""log-mel в дБ с полом на −60 дБ от максимума: шум в пустых полосах не превращается в «атаки»."""
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mel = mag**2 @ _MEL.T
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return (10 * np.log10(mel + 1e-6 * float(mel.max()) + 1e-12)).astype(np.float32)
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def estimate_tempo(onset: Float, sr: int = SAMPLE_RATE) -> tuple[float | None, float]:
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"""Темп по автокорреляции огибающей онсетов, 60–200 BPM с мягким приором к 120.
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Возвращает (bpm, уверенность 0..1); без выраженного ритма — (None, 0)."""
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fps = sr / HOP
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env = onset - onset.mean()
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# Ровный звук без атак: огибающая — численный шум, «ритма» в нём нет.
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if len(env) < int(fps * 4) or float(onset.std()) < 0.5:
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return None, 0.0
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ac = np.correlate(env, env, mode="full")[len(env) - 1 :]
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ac /= ac[0] + 1e-9
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lags = np.arange(len(ac))
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bpm_of = 60.0 * fps / np.maximum(lags, 1)
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ok = (bpm_of >= 60) & (bpm_of <= 200)
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if not np.any(ok):
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return None, 0.0
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prior = np.exp(-0.5 * (np.log2(bpm_of / 120.0) / 0.9) ** 2)
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weighted = np.where(ok, ac * prior, -np.inf)
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lag = int(np.argmax(weighted))
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# Уточнение по параболе вокруг пика.
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if 1 <= lag < len(ac) - 1:
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a, b, c = ac[lag - 1], ac[lag], ac[lag + 1]
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denom = a - 2 * b + c
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shift = 0.5 * (a - c) / denom if denom != 0 else 0.0
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else:
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shift = 0.0
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bpm = 60.0 * fps / (lag + shift)
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# Ясность пульса: насколько пик выше типичного уровня автокорреляции в диапазоне темпов.
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base = float(np.median(ac[ok]))
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confidence = float(np.clip((ac[lag] - base) / (1 - base + 1e-9), 0, 1))
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if confidence < 0.1:
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return None, confidence
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return round(float(bpm), 1), confidence
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def _key(mag: Float, sr: int = SAMPLE_RATE) -> tuple[str | None, str | None, float]:
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"""Тональность по хромаграмме; возвращает (нота, mode, уверенность)."""
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freqs = np.linspace(0, sr / 2, mag.shape[1])
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valid = (freqs > 60) & (freqs < 5000)
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pitch = np.round(12 * np.log2(freqs[valid] / 440.0) + 69).astype(int) % 12
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energy = mag[:, valid].sum(axis=0)
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chroma = np.bincount(pitch, weights=energy, minlength=12)
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if chroma.sum() <= 0:
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return None, None, 0.0
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chroma = chroma / chroma.sum()
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best = (-2.0, 0, "major")
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for shift in range(12):
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for name, prof in (("major", _MAJOR), ("minor", _MINOR)):
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r = float(np.corrcoef(chroma, np.roll(prof, shift))[0, 1])
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if r > best[0]:
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best = (r, shift, name)
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r, shift, scale = best
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return _NOTES[shift], scale, max(r, 0.0)
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def analyze(samples: Float, sr: int = SAMPLE_RATE) -> Features:
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"""Признаки трека по моно-сигналу sr Гц (float32, −1..1)."""
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if sr != SAMPLE_RATE:
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raise ValueError(f"expected {SAMPLE_RATE} Hz")
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parts = segments(samples, sr)
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mags = [_stft_mag(p) for p in parts]
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mag = np.concatenate(mags, axis=0)
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logmel = _log_mel(mag) # (frames, mels)
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# Огибающая онсетов — положительный прирост log-mel; темп по каждому фрагменту, берём самый уверенный.
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tempos = []
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for m in mags:
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lm = _log_mel(m)
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onset = np.maximum(np.diff(lm, axis=0), 0).sum(axis=1).astype(np.float32)
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tempos.append(estimate_tempo(onset, sr))
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bpm, bpm_conf = max(tempos, key=lambda t: t[1])
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flux = np.maximum(np.diff(logmel, axis=0), 0).mean() if len(logmel) > 1 else 0.0
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rms = np.sqrt(np.mean(samples.astype(np.float64) ** 2) + 1e-12)
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loudness = float(-0.691 + 20 * np.log10(rms)) # без K-фильтра: близко к LUFS для музыки
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centroid = float((mag * np.arange(mag.shape[1])).sum() / (mag.sum() + 1e-9) / mag.shape[1])
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brightness = float(np.clip(centroid * 4, 0, 1))
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loud_part = np.clip((loudness + 30) / 22, 0, 1)
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energy = float(np.clip(0.5 * loud_part + 0.3 * np.clip(flux / 3, 0, 1) + 0.2 * brightness, 0, 1))
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danceability = float(np.clip(bpm_conf * 1.6, 0, 1)) if bpm else 0.0
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key, scale, key_conf = _key(mag, sr)
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mode = 1.0 if scale == "major" else -1.0 if scale == "minor" else 0.0
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valence = float(np.clip(0.45 + 0.2 * mode * key_conf + 0.35 * (brightness - 0.4), 0, 1))
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# Акустичность: мало высоких частот и плавная динамика.
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acousticness = float(np.clip(1 - brightness * 1.4 - flux / 6, 0, 1))
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# Вступление: время, пока громкость первого фрагмента не дойдёт до половины медианной.
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first = np.sqrt((mags[0] ** 2).mean(axis=1))
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above = np.nonzero(first >= 0.5 * np.median(first))[0]
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intro_ms = int(above[0] * HOP / sr * 1000) if len(above) else 0
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# Эмбеддинг: форма спектра (среднее без общей громкости), разброс, динамика по полосам, ритмичность.
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mean = logmel.mean(axis=0)
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shape = mean - mean.mean()
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std = logmel.std(axis=0)
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diff = np.diff(logmel, axis=0) if len(logmel) > 1 else np.zeros((1, N_MELS), np.float32)
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delta = np.abs(diff).mean(axis=0)
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band_flux = np.maximum(diff, 0).std(axis=0)
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emb = np.concatenate([shape / 10, std / 10, delta / 3, band_flux / 3]).astype(np.float32)
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emb = emb / (np.linalg.norm(emb) + 1e-9)
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return Features(
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bpm=bpm,
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bpm_confidence=round(bpm_conf, 3),
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key=key,
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scale=scale,
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loudness_lufs=round(loudness, 2),
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energy=round(energy, 3),
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danceability=round(danceability, 3),
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valence=round(valence, 3),
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acousticness=round(acousticness, 3),
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intro_ms=intro_ms,
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embedding=emb,
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)
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