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0a88ee7 | 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 | from dataclasses import dataclass
import math
import numpy as np
from .config import TTSConfig
from .normalizer import normalize_text, text_to_symbols
@dataclass(frozen=True)
class VoiceProfile:
pitch_scale: float
formant_scale: float
brightness: float
VOICE_PROFILES = {
"neutral": VoiceProfile(pitch_scale=1.0, formant_scale=1.0, brightness=1.0),
"bright": VoiceProfile(pitch_scale=1.2, formant_scale=1.1, brightness=1.15),
"deep": VoiceProfile(pitch_scale=0.82, formant_scale=0.9, brightness=0.85),
}
VOWELS = {
"a": (800, 1200, 2500),
"e": (530, 1850, 2500),
"i": (300, 2200, 2900),
"o": (500, 900, 2400),
"u": (350, 800, 2200),
"A": (650, 1600, 2550),
"I": (320, 2400, 3000),
"U": (380, 1000, 2300),
"W": (450, 1100, 2350),
}
FRICATIVES = set("fszhvjxSFT")
STOPS = set("pbtdkgcqC")
NASALS = set("mn")
LIQUIDS = set("lrwy")
class TinyTTSSynthesizer:
def __init__(self, config: TTSConfig | None = None):
self.config = config or TTSConfig()
def synthesize(
self,
text: str,
voice: str = "neutral",
speed: float = 1.0,
pitch_shift: float = 0.0,
) -> tuple[int, np.ndarray, str]:
normalized = normalize_text(text)
symbols = text_to_symbols(text)
profile = VOICE_PROFILES.get(voice, VOICE_PROFILES["neutral"])
pieces: list[np.ndarray] = []
for symbol in symbols:
segment = self._render_symbol(
symbol=symbol,
profile=profile,
speed=max(speed, 0.1),
pitch_shift=pitch_shift,
)
if segment.size:
pieces.append(segment)
if not pieces:
pieces.append(self._silence(0.25))
audio = pieces[0]
for piece in pieces[1:]:
audio = self._crossfade(audio, piece)
peak = np.max(np.abs(audio))
if peak > 0:
audio = (audio / peak) * self.config.amplitude
return self.config.sample_rate, audio.astype(np.float32), normalized
def _render_symbol(
self,
symbol: str,
profile: VoiceProfile,
speed: float,
pitch_shift: float,
) -> np.ndarray:
if symbol == " ":
return self._silence(self.config.pause_duration_ms / 1000 / speed)
if symbol == "|":
return self._silence((self.config.pause_duration_ms * 2.2) / 1000 / speed)
if symbol in VOWELS:
return self._vowel(symbol, profile, speed, pitch_shift)
if symbol in FRICATIVES:
return self._fricative(profile, speed)
if symbol in STOPS:
return self._stop(profile, speed)
if symbol in NASALS:
return self._nasal(profile, speed, pitch_shift)
if symbol in LIQUIDS:
return self._liquid(profile, speed, pitch_shift)
if symbol.isdigit():
return self._digit(symbol, profile, speed, pitch_shift)
return self._soft_noise(speed)
def _vowel(
self,
symbol: str,
profile: VoiceProfile,
speed: float,
pitch_shift: float,
) -> np.ndarray:
duration = self._duration(1.0, speed)
t = self._timeline(duration)
pitch = self.config.base_pitch_hz * profile.pitch_scale * (1.0 + pitch_shift)
formants = [f * profile.formant_scale for f in VOWELS[symbol]]
source = (
np.sin(2 * math.pi * pitch * t)
+ 0.35 * np.sin(2 * math.pi * pitch * 2.0 * t)
+ 0.18 * np.sin(2 * math.pi * pitch * 3.0 * t)
)
resonance = (
0.42 * np.sin(2 * math.pi * formants[0] * t)
+ 0.22 * np.sin(2 * math.pi * formants[1] * t)
+ 0.12 * np.sin(2 * math.pi * formants[2] * t)
)
envelope = self._adsr(len(t), attack=0.08, decay=0.12, sustain=0.82, release=0.18)
return (0.7 * source + 0.5 * resonance) * envelope
def _fricative(self, profile: VoiceProfile, speed: float) -> np.ndarray:
duration = self._duration(0.8, speed)
n = self._num_samples(duration)
noise = np.random.uniform(-1.0, 1.0, n)
tilt = np.concatenate(([noise[0]], np.diff(noise)))
mix = 0.65 * tilt + 0.35 * noise * profile.brightness
envelope = self._adsr(n, attack=0.02, decay=0.05, sustain=0.6, release=0.2)
return mix * envelope * 0.7
def _stop(self, profile: VoiceProfile, speed: float) -> np.ndarray:
closure = self._silence(0.035 / speed)
burst = self._fricative(profile, speed)[: self._num_samples(0.04 / speed)]
return np.concatenate([closure, burst])
def _nasal(
self,
profile: VoiceProfile,
speed: float,
pitch_shift: float,
) -> np.ndarray:
duration = self._duration(0.9, speed)
t = self._timeline(duration)
pitch = self.config.base_pitch_hz * 0.92 * profile.pitch_scale * (1.0 + pitch_shift)
signal = (
np.sin(2 * math.pi * pitch * t)
+ 0.28 * np.sin(2 * math.pi * 280 * profile.formant_scale * t)
+ 0.12 * np.sin(2 * math.pi * 900 * profile.formant_scale * t)
)
envelope = self._adsr(len(t), attack=0.05, decay=0.08, sustain=0.72, release=0.2)
return signal * envelope * 0.7
def _liquid(
self,
profile: VoiceProfile,
speed: float,
pitch_shift: float,
) -> np.ndarray:
duration = self._duration(0.75, speed)
t = self._timeline(duration)
pitch = self.config.base_pitch_hz * 1.05 * profile.pitch_scale * (1.0 + pitch_shift)
glide = np.linspace(0.95, 1.05, len(t))
signal = (
np.sin(2 * math.pi * pitch * glide * t)
+ 0.22 * np.sin(2 * math.pi * 700 * profile.formant_scale * t)
+ 0.1 * np.sin(2 * math.pi * 1500 * profile.formant_scale * t)
)
envelope = self._adsr(len(t), attack=0.04, decay=0.08, sustain=0.7, release=0.18)
return signal * envelope * 0.65
def _digit(
self,
symbol: str,
profile: VoiceProfile,
speed: float,
pitch_shift: float,
) -> np.ndarray:
names = {
"0": "zero",
"1": "one",
"2": "two",
"3": "three",
"4": "four",
"5": "five",
"6": "six",
"7": "seven",
"8": "eight",
"9": "nine",
}
chunks = [self._render_symbol(s, profile, speed, pitch_shift) for s in text_to_symbols(names[symbol])]
result = chunks[0] if chunks else self._silence(0.08)
for chunk in chunks[1:]:
result = self._crossfade(result, chunk)
return result
def _soft_noise(self, speed: float) -> np.ndarray:
duration = self._duration(0.45, speed)
n = self._num_samples(duration)
noise = np.random.uniform(-0.3, 0.3, n)
envelope = self._adsr(n, attack=0.03, decay=0.1, sustain=0.2, release=0.12)
return noise * envelope
def _crossfade(self, left: np.ndarray, right: np.ndarray) -> np.ndarray:
fade = min(
int(self.config.sample_rate * self.config.crossfade_ms / 1000),
len(left),
len(right),
)
if fade <= 0:
return np.concatenate([left, right])
curve_out = np.linspace(1.0, 0.0, fade)
curve_in = np.linspace(0.0, 1.0, fade)
mixed = left[-fade:] * curve_out + right[:fade] * curve_in
return np.concatenate([left[:-fade], mixed, right[fade:]])
def _duration(self, scale: float, speed: float) -> float:
base = self.config.symbol_duration_ms / 1000
return max(0.03, (base * scale) / speed)
def _num_samples(self, duration: float) -> int:
return max(1, int(self.config.sample_rate * duration))
def _timeline(self, duration: float) -> np.ndarray:
return np.linspace(0.0, duration, self._num_samples(duration), endpoint=False)
def _silence(self, duration: float) -> np.ndarray:
return np.zeros(self._num_samples(duration), dtype=np.float32)
def _adsr(
self,
n: int,
attack: float,
decay: float,
sustain: float,
release: float,
) -> np.ndarray:
attack_n = max(1, int(n * attack))
decay_n = max(1, int(n * decay))
release_n = max(1, int(n * release))
sustain_n = max(1, n - attack_n - decay_n - release_n)
attack_curve = np.linspace(0.0, 1.0, attack_n, endpoint=False)
decay_curve = np.linspace(1.0, sustain, decay_n, endpoint=False)
sustain_curve = np.full(sustain_n, sustain)
release_curve = np.linspace(sustain, 0.0, release_n, endpoint=True)
envelope = np.concatenate([attack_curve, decay_curve, sustain_curve, release_curve])
if len(envelope) < n:
envelope = np.pad(envelope, (0, n - len(envelope)))
return envelope[:n]
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