File size: 6,608 Bytes
47acec0 | 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 | from __future__ import annotations
from dataclasses import dataclass
import librosa
import numpy as np
import pyworld
from scipy import signal
TARGET_SAMPLE_RATE = 16_000
MAX_DURATION_SECONDS = 15
MAX_SAMPLES = TARGET_SAMPLE_RATE * MAX_DURATION_SECONDS
MIN_LOW_CUT_HZ = 20
MAX_HIGH_CUT_HZ = 7_900
MIN_BANDWIDTH_HZ = 100
class AudioProcessingError(ValueError):
"""Raised for user-facing audio processing errors."""
@dataclass(frozen=True)
class ProcessingSettings:
pitch_semitones: int
low_cut_hz: float
high_cut_hz: float
def convert_voice(
audio_input: tuple[int, np.ndarray] | None,
pitch_semitones: int,
low_cut_hz: float,
high_cut_hz: float,
) -> tuple[int, np.ndarray, str]:
settings = ProcessingSettings(
pitch_semitones=int(pitch_semitones),
low_cut_hz=float(low_cut_hz),
high_cut_hz=float(high_cut_hz),
)
_validate_filter_settings(settings.low_cut_hz, settings.high_cut_hz)
audio = _prepare_audio(audio_input)
converted = _change_pitch_with_world(audio, settings.pitch_semitones)
filtered = _apply_band_filter(
converted,
TARGET_SAMPLE_RATE,
settings.low_cut_hz,
settings.high_cut_hz,
)
normalized = _normalize_audio(filtered)
if normalized.size == 0:
raise AudioProcessingError("出力音声が空です。")
if not np.all(np.isfinite(normalized)):
raise AudioProcessingError("出力音声に不正な値が含まれています。")
message = _build_success_message(settings)
return TARGET_SAMPLE_RATE, normalized.astype(np.float32), message
def _prepare_audio(audio_input: tuple[int, np.ndarray] | None) -> np.ndarray:
if audio_input is None:
raise AudioProcessingError("音声をアップロードするか、マイクで録音してください。")
sample_rate, waveform = audio_input
if sample_rate is None or int(sample_rate) <= 0:
raise AudioProcessingError("サンプリング周波数が不正です。")
if waveform is None:
raise AudioProcessingError("音声データが空です。")
audio = np.asarray(waveform)
if audio.size == 0:
raise AudioProcessingError("音声データが空です。")
if not np.all(np.isfinite(audio)):
raise AudioProcessingError("音声データにNaNまたはInfが含まれています。")
audio = _to_float_audio(audio)
audio = _to_mono(audio)
if int(sample_rate) != TARGET_SAMPLE_RATE:
audio = librosa.resample(
y=audio,
orig_sr=int(sample_rate),
target_sr=TARGET_SAMPLE_RATE,
)
audio = audio[:MAX_SAMPLES]
if audio.size == 0:
raise AudioProcessingError("音声データが空です。")
return np.ascontiguousarray(audio, dtype=np.float64)
def _to_float_audio(audio: np.ndarray) -> np.ndarray:
if np.issubdtype(audio.dtype, np.integer):
info = np.iinfo(audio.dtype)
scale = max(abs(info.min), info.max)
return audio.astype(np.float64) / scale
audio = audio.astype(np.float64)
peak = float(np.max(np.abs(audio)))
if peak > 1.0:
audio = audio / peak
return audio
def _to_mono(audio: np.ndarray) -> np.ndarray:
if audio.ndim == 1:
return audio
if audio.ndim != 2:
raise AudioProcessingError("音声データの形式が不正です。")
if audio.shape[1] in (1, 2):
return np.mean(audio, axis=1)
if audio.shape[0] in (1, 2):
return np.mean(audio, axis=0)
raise AudioProcessingError("音声チャンネル数が不正です。")
def _change_pitch_with_world(audio: np.ndarray, pitch_semitones: int) -> np.ndarray:
try:
f0, time_axis = pyworld.harvest(audio, TARGET_SAMPLE_RATE)
f0 = pyworld.stonemask(audio, f0, time_axis, TARGET_SAMPLE_RATE)
spectral_envelope = pyworld.cheaptrick(audio, f0, time_axis, TARGET_SAMPLE_RATE)
aperiodicity = pyworld.d4c(audio, f0, time_axis, TARGET_SAMPLE_RATE)
pitch_ratio = 2 ** (pitch_semitones / 12)
converted_f0 = f0 * pitch_ratio
synthesized = pyworld.synthesize(
converted_f0,
spectral_envelope,
aperiodicity,
TARGET_SAMPLE_RATE,
)
except Exception as error:
raise AudioProcessingError(f"WORLD分析または再合成に失敗しました: {error}") from error
if synthesized.size == 0:
raise AudioProcessingError("WORLD再合成後の音声が空です。")
synthesized = np.nan_to_num(synthesized, nan=0.0, posinf=0.0, neginf=0.0)
return np.ascontiguousarray(synthesized[: audio.size], dtype=np.float64)
def _apply_band_filter(
audio: np.ndarray,
sample_rate: int,
low_cut_hz: float,
high_cut_hz: float,
order: int = 4,
) -> np.ndarray:
if low_cut_hz <= MIN_LOW_CUT_HZ and high_cut_hz >= MAX_HIGH_CUT_HZ:
return audio
nyquist = sample_rate / 2
if low_cut_hz <= MIN_LOW_CUT_HZ:
sos = signal.butter(
order,
high_cut_hz,
btype="lowpass",
fs=sample_rate,
output="sos",
)
elif high_cut_hz >= MAX_HIGH_CUT_HZ:
sos = signal.butter(
order,
low_cut_hz,
btype="highpass",
fs=sample_rate,
output="sos",
)
else:
sos = signal.butter(
order,
[low_cut_hz, min(high_cut_hz, nyquist - 1)],
btype="bandpass",
fs=sample_rate,
output="sos",
)
try:
return signal.sosfiltfilt(sos, audio)
except ValueError:
return signal.sosfilt(sos, audio)
def _normalize_audio(audio: np.ndarray) -> np.ndarray:
audio = np.nan_to_num(audio, nan=0.0, posinf=0.0, neginf=0.0)
peak = float(np.max(np.abs(audio))) if audio.size else 0.0
if peak > 0:
audio = audio / peak * 0.95
return audio
def _validate_filter_settings(low_cut_hz: float, high_cut_hz: float) -> None:
if high_cut_hz - low_cut_hz < MIN_BANDWIDTH_HZ:
raise AudioProcessingError(
"高域カットオフ周波数は、低域カットオフ周波数より100 Hz以上高く設定してください。"
)
def _build_success_message(settings: ProcessingSettings) -> str:
sign = "+" if settings.pitch_semitones > 0 else ""
return (
"変換が完了しました。\n"
f"声の高さ:{sign}{settings.pitch_semitones}半音\n"
f"通過帯域:{int(settings.low_cut_hz)}~{int(settings.high_cut_hz)} Hz"
)
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