"""Real-time speech-driven head sway animation. Based on reachy_mini_conversation_app's SwayRollRT algorithm. Analyzes audio loudness to drive natural head movements during TTS playback. """ import math from collections import deque from itertools import islice from typing import Any, Callable, Dict, List, Optional import numpy as np from numpy.typing import NDArray # Tunables (from reachy_mini_conversation_app) SR = 16_000 FRAME_MS = 20 HOP_MS = 50 SWAY_MASTER = 1.5 SENS_DB_OFFSET = +4.0 VAD_DB_ON = -35.0 VAD_DB_OFF = -45.0 VAD_ATTACK_MS = 40 VAD_RELEASE_MS = 250 ENV_FOLLOW_GAIN = 0.65 SWAY_F_PITCH = 2.2 SWAY_A_PITCH_DEG = 4.5 SWAY_F_YAW = 0.6 SWAY_A_YAW_DEG = 7.5 SWAY_F_ROLL = 1.3 SWAY_A_ROLL_DEG = 2.25 SWAY_F_X = 0.35 SWAY_A_X_MM = 4.5 SWAY_F_Y = 0.45 SWAY_A_Y_MM = 3.75 SWAY_F_Z = 0.25 SWAY_A_Z_MM = 2.25 SWAY_DB_LOW = -46.0 SWAY_DB_HIGH = -18.0 LOUDNESS_GAMMA = 0.9 SWAY_ATTACK_MS = 50 SWAY_RELEASE_MS = 250 # Derived FRAME = int(SR * FRAME_MS / 1000) HOP = int(SR * HOP_MS / 1000) ATTACK_FR = max(1, int(VAD_ATTACK_MS / HOP_MS)) RELEASE_FR = max(1, int(VAD_RELEASE_MS / HOP_MS)) SWAY_ATTACK_FR = max(1, int(SWAY_ATTACK_MS / HOP_MS)) SWAY_RELEASE_FR = max(1, int(SWAY_RELEASE_MS / HOP_MS)) def _rms_dbfs(x: NDArray[np.float32]) -> float: """Root-mean-square in dBFS for float32 mono array in [-1,1].""" x = x.astype(np.float32, copy=False) rms = np.sqrt(np.mean(x * x, dtype=np.float32) + 1e-12, dtype=np.float32) return float(20.0 * math.log10(float(rms) + 1e-12)) def _loudness_gain(db: float, offset: float = SENS_DB_OFFSET) -> float: """Normalize dB into [0,1] with gamma; clipped to [0,1].""" t = (db + offset - SWAY_DB_LOW) / (SWAY_DB_HIGH - SWAY_DB_LOW) t = max(0.0, min(1.0, t)) return t ** LOUDNESS_GAMMA if LOUDNESS_GAMMA != 1.0 else t def _to_float32_mono(x: NDArray[Any]) -> NDArray[np.float32]: """Convert arbitrary PCM array to float32 mono in [-1,1].""" a = np.asarray(x) if a.ndim == 0: return np.zeros(0, dtype=np.float32) if a.ndim == 2: if a.shape[0] <= 8 and a.shape[0] <= a.shape[1]: a = np.mean(a, axis=0) else: a = np.mean(a, axis=1) elif a.ndim > 2: a = np.mean(a.reshape(a.shape[0], -1), axis=0) if np.issubdtype(a.dtype, np.floating): return a.astype(np.float32, copy=False) info = np.iinfo(a.dtype) scale = float(max(-info.min, info.max)) return a.astype(np.float32) / (scale if scale != 0.0 else 1.0) def _resample_linear(x: NDArray[np.float32], sr_in: int, sr_out: int) -> NDArray[np.float32]: """Lightweight linear resampler for short buffers.""" if sr_in == sr_out or x.size == 0: return x n_out = int(round(x.size * sr_out / sr_in)) if n_out <= 1: return np.zeros(0, dtype=np.float32) t_in = np.linspace(0.0, 1.0, num=x.size, dtype=np.float32, endpoint=True) t_out = np.linspace(0.0, 1.0, num=n_out, dtype=np.float32, endpoint=True) return np.interp(t_out, t_in, x).astype(np.float32, copy=False) class SpeechSwayRT: """Real-time speech-driven sway animation. Feed audio chunks and get sway offsets for head motion. Based on reachy_mini_conversation_app's SwayRollRT algorithm. """ def __init__(self, rng_seed: int = 7): """Initialize state.""" self._seed = int(rng_seed) self.samples: deque[float] = deque(maxlen=10 * SR) self.carry: NDArray[np.float32] = np.zeros(0, dtype=np.float32) self.vad_on = False self.vad_above = 0 self.vad_below = 0 self.sway_env = 0.0 self.sway_up = 0 self.sway_down = 0 rng = np.random.default_rng(self._seed) self.phase_pitch = float(rng.random() * 2 * math.pi) self.phase_yaw = float(rng.random() * 2 * math.pi) self.phase_roll = float(rng.random() * 2 * math.pi) self.phase_x = float(rng.random() * 2 * math.pi) self.phase_y = float(rng.random() * 2 * math.pi) self.phase_z = float(rng.random() * 2 * math.pi) self.t = 0.0 def reset(self) -> None: """Reset state but keep initial phases/seed.""" self.samples.clear() self.carry = np.zeros(0, dtype=np.float32) self.vad_on = False self.vad_above = 0 self.vad_below = 0 self.sway_env = 0.0 self.sway_up = 0 self.sway_down = 0 self.t = 0.0 def feed(self, pcm: NDArray[Any], sr: Optional[int] = None) -> List[Dict[str, float]]: """Stream in PCM chunk. Returns list of sway dicts, one per hop. Args: pcm: Audio samples, shape (N,) or (C,N)/(N,C); int or float. sr: Sample rate of pcm (None -> assume 16kHz). Returns: List of dicts with keys: pitch_rad, yaw_rad, roll_rad, x_m, y_m, z_m """ sr_in = SR if sr is None else int(sr) x = _to_float32_mono(pcm) if x.size == 0: return [] if sr_in != SR: x = _resample_linear(x, sr_in, SR) if x.size == 0: return [] if self.carry.size: self.carry = np.concatenate([self.carry, x]) else: self.carry = x out: List[Dict[str, float]] = [] while self.carry.size >= HOP: hop = self.carry[:HOP] self.carry = self.carry[HOP:] self.samples.extend(hop.tolist()) if len(self.samples) < FRAME: self.t += HOP_MS / 1000.0 continue frame = np.fromiter( islice(self.samples, len(self.samples) - FRAME, len(self.samples)), dtype=np.float32, count=FRAME, ) db = _rms_dbfs(frame) # VAD with hysteresis + attack/release if db >= VAD_DB_ON: self.vad_above += 1 self.vad_below = 0 if not self.vad_on and self.vad_above >= ATTACK_FR: self.vad_on = True elif db <= VAD_DB_OFF: self.vad_below += 1 self.vad_above = 0 if self.vad_on and self.vad_below >= RELEASE_FR: self.vad_on = False if self.vad_on: self.sway_up = min(SWAY_ATTACK_FR, self.sway_up + 1) self.sway_down = 0 else: self.sway_down = min(SWAY_RELEASE_FR, self.sway_down + 1) self.sway_up = 0 up = self.sway_up / SWAY_ATTACK_FR down = 1.0 - (self.sway_down / SWAY_RELEASE_FR) target = up if self.vad_on else down self.sway_env += ENV_FOLLOW_GAIN * (target - self.sway_env) self.sway_env = max(0.0, min(1.0, self.sway_env)) loud = _loudness_gain(db) * SWAY_MASTER env = self.sway_env self.t += HOP_MS / 1000.0 # Oscillators pitch = (math.radians(SWAY_A_PITCH_DEG) * loud * env * math.sin(2 * math.pi * SWAY_F_PITCH * self.t + self.phase_pitch)) yaw = (math.radians(SWAY_A_YAW_DEG) * loud * env * math.sin(2 * math.pi * SWAY_F_YAW * self.t + self.phase_yaw)) roll = (math.radians(SWAY_A_ROLL_DEG) * loud * env * math.sin(2 * math.pi * SWAY_F_ROLL * self.t + self.phase_roll)) x_m = (SWAY_A_X_MM / 1000.0) * loud * env * math.sin( 2 * math.pi * SWAY_F_X * self.t + self.phase_x) y_m = (SWAY_A_Y_MM / 1000.0) * loud * env * math.sin( 2 * math.pi * SWAY_F_Y * self.t + self.phase_y) z_m = (SWAY_A_Z_MM / 1000.0) * loud * env * math.sin( 2 * math.pi * SWAY_F_Z * self.t + self.phase_z) out.append({ "pitch_rad": pitch, "yaw_rad": yaw, "roll_rad": roll, "x_m": x_m, "y_m": y_m, "z_m": z_m, }) return out def analyze_audio_for_sway( audio_data: NDArray[Any], sample_rate: int, callback: Callable[[Dict[str, float]], None], ) -> None: """Analyze entire audio and call callback for each sway frame. This is for pre-analyzed playback where we process the whole file and emit sway frames at the correct timing. Args: audio_data: Audio samples sample_rate: Sample rate callback: Called with sway dict for each frame """ sway = SpeechSwayRT() frames = sway.feed(audio_data, sample_rate) for frame in frames: callback(frame)