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8fbc553 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | """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)
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