File size: 9,311 Bytes
7344bef | 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 | import numpy as np
DEFAULT_TRANSIENT_MAX_SECONDS = 0.1
DEFAULT_TRANSIENT_CONTEXT_SILENCE_SECONDS = 0.2
DEFAULT_TRANSIENT_WINDOW_SECONDS = 0.01
DEFAULT_TRANSIENT_SILENCE_THRESHOLD = 0.015
def _mono(audio_np: np.ndarray) -> np.ndarray:
return audio_np.mean(axis=1) if audio_np.ndim == 2 else audio_np
def _window_settings(sample_rate: int, max_transient_seconds: float, context_silence_seconds: float, window_seconds: float) -> tuple[int, int, int]:
window = max(1, int(window_seconds * sample_rate))
max_noise_windows = max(1, int(np.ceil(max_transient_seconds * sample_rate / window)))
min_silence_windows = max(1, int(np.ceil(context_silence_seconds * sample_rate / window)))
return window, max_noise_windows, min_silence_windows
def _rms_windows(mono: np.ndarray, window: int, frame_count: int, offset: int = 0) -> np.ndarray:
return np.array([np.sqrt(np.mean(mono[offset + i * window : offset + (i + 1) * window].astype(np.float64) ** 2)) for i in range(frame_count)])
def _debug_print(debug: bool, label: str, message: str) -> None:
if debug:
print(f"[{label}] {message}")
def trim_leading_transient_noise(
audio_np: np.ndarray,
sample_rate: int,
*,
max_transient_seconds: float = DEFAULT_TRANSIENT_MAX_SECONDS,
context_silence_seconds: float = DEFAULT_TRANSIENT_CONTEXT_SILENCE_SECONDS,
window_seconds: float = DEFAULT_TRANSIENT_WINDOW_SECONDS,
threshold: float = DEFAULT_TRANSIENT_SILENCE_THRESHOLD,
debug: bool = False,
label: str = "Audio Cleaning",
) -> np.ndarray:
mono = _mono(audio_np)
window, max_noise_windows, min_silence_windows = _window_settings(sample_rate, max_transient_seconds, context_silence_seconds, window_seconds)
frame_count = min(len(mono) // window, max_noise_windows + min_silence_windows)
if frame_count < max_noise_windows + min_silence_windows:
return audio_np
active = _rms_windows(mono, window, frame_count) > threshold
if not active[0]:
return audio_np
for silence_start in range(1, max_noise_windows + 1):
if not active[silence_start : silence_start + min_silence_windows].any():
trim_end = silence_start * window
_debug_print(debug, label, f"Trimmed leading transient noise ({trim_end / sample_rate:.2f}s)")
return audio_np[trim_end:]
return audio_np
def trim_trailing_transient_noise(
audio_np: np.ndarray,
sample_rate: int,
*,
max_transient_seconds: float = DEFAULT_TRANSIENT_MAX_SECONDS,
context_silence_seconds: float = DEFAULT_TRANSIENT_CONTEXT_SILENCE_SECONDS,
window_seconds: float = DEFAULT_TRANSIENT_WINDOW_SECONDS,
threshold: float = DEFAULT_TRANSIENT_SILENCE_THRESHOLD,
debug: bool = False,
label: str = "Audio Cleaning",
) -> np.ndarray:
mono = _mono(audio_np)
window, max_noise_windows, min_silence_windows = _window_settings(sample_rate, max_transient_seconds, context_silence_seconds, window_seconds)
frame_count = min(len(mono) // window, max_noise_windows + min_silence_windows)
if frame_count < max_noise_windows + min_silence_windows:
return audio_np
offset = len(mono) - frame_count * window
active = _rms_windows(mono, window, frame_count, offset=offset) > threshold
if not active[-1]:
return audio_np
for noise_start in range(frame_count - 1, frame_count - max_noise_windows - 1, -1):
if not active[noise_start - min_silence_windows : noise_start].any():
trim_start = offset + noise_start * window
if trim_start <= 0 or trim_start >= len(audio_np):
return audio_np
_debug_print(debug, label, f"Trimmed trailing transient noise ({(len(audio_np) - trim_start) / sample_rate:.2f}s)")
return audio_np[:trim_start]
return audio_np
def mute_isolated_transient_noise(
audio_np: np.ndarray,
sample_rate: int,
*,
max_transient_seconds: float = DEFAULT_TRANSIENT_MAX_SECONDS,
context_silence_seconds: float = DEFAULT_TRANSIENT_CONTEXT_SILENCE_SECONDS,
window_seconds: float = DEFAULT_TRANSIENT_WINDOW_SECONDS,
threshold: float = DEFAULT_TRANSIENT_SILENCE_THRESHOLD,
debug: bool = False,
label: str = "Audio Cleaning",
) -> np.ndarray:
mono = _mono(audio_np)
window, max_noise_windows, min_silence_windows = _window_settings(sample_rate, max_transient_seconds, context_silence_seconds, window_seconds)
frame_count = len(mono) // window
if frame_count < max_noise_windows + 2 * min_silence_windows:
return audio_np
active = _rms_windows(mono, window, frame_count) > threshold
muted = audio_np.copy()
active_start = None
muted_count = 0
for idx, is_active in enumerate(active):
if is_active and active_start is None:
active_start = idx
elif not is_active and active_start is not None:
if idx - active_start <= max_noise_windows:
prev_start = max(0, active_start - min_silence_windows)
next_end = min(frame_count, idx + min_silence_windows)
if not active[prev_start:active_start].any() and not active[idx:next_end].any() and active_start > 0 and idx < frame_count:
muted[active_start * window : idx * window] = 0
muted_count += 1
active_start = None
_debug_print(debug and muted_count > 0, label, f"Muted {muted_count} isolated transient noise segment(s)")
return muted
def trim_leading_noise_before_speech(
audio_np: np.ndarray,
sample_rate: int,
*,
speech_threshold: float = 0.03,
max_leading_seconds: float = 1.0,
keep_silence_seconds: float = 0.1,
window_seconds: float = DEFAULT_TRANSIENT_WINDOW_SECONDS,
debug: bool = False,
label: str = "Audio Cleaning",
) -> np.ndarray:
mono = _mono(audio_np)
window = max(1, int(window_seconds * sample_rate))
frame_count = min(len(mono) // window, max(1, int(max_leading_seconds * sample_rate / window)))
if frame_count == 0:
return audio_np
strong_windows = np.where(_rms_windows(mono, window, frame_count) > speech_threshold)[0]
if len(strong_windows) == 0:
return audio_np
first_speech = int(strong_windows[0]) * window
keep_samples = int(keep_silence_seconds * sample_rate)
trim_end = max(0, first_speech - keep_samples)
if trim_end <= 0:
return audio_np
_debug_print(debug, label, f"Trimmed leading low-level noise before speech ({trim_end / sample_rate:.2f}s)")
return audio_np[trim_end:]
def ensure_trailing_silence(audio_np: np.ndarray, sample_rate: int, min_silence_seconds: float, *, threshold: float = DEFAULT_TRANSIENT_SILENCE_THRESHOLD) -> np.ndarray:
if min_silence_seconds <= 0:
return audio_np
mono = _mono(audio_np)
window = max(1, int(DEFAULT_TRANSIENT_WINDOW_SECONDS * sample_rate))
frame_count = len(mono) // window
if frame_count == 0:
return audio_np
active = _rms_windows(mono, window, frame_count) > threshold
active_windows = np.where(active)[0]
if len(active_windows) == 0:
return audio_np
last_active_end = min(len(audio_np), (int(active_windows[-1]) + 1) * window)
existing_silence = len(audio_np) - last_active_end
target_silence = int(min_silence_seconds * sample_rate)
missing_silence = target_silence - existing_silence
if missing_silence <= 0:
return audio_np
pad_shape = (missing_silence,) if audio_np.ndim == 1 else (missing_silence, audio_np.shape[1])
return np.concatenate([audio_np, np.zeros(pad_shape, dtype=audio_np.dtype)], axis=0)
def trim_after_silence_boundary(
audio_np: np.ndarray,
sample_rate: int,
earliest_seconds: float,
*,
search_seconds: float = 2.0,
min_silence_seconds: float = 0.18,
keep_silence_seconds: float = 0.12,
window_seconds: float = DEFAULT_TRANSIENT_WINDOW_SECONDS,
threshold: float = DEFAULT_TRANSIENT_SILENCE_THRESHOLD,
debug: bool = False,
label: str = "Audio Cleaning",
) -> np.ndarray:
if earliest_seconds <= 0 or search_seconds <= 0 or len(audio_np) == 0:
return audio_np
mono = _mono(audio_np)
window = max(1, int(window_seconds * sample_rate))
earliest_sample = max(0, int(earliest_seconds * sample_rate))
if earliest_sample >= len(mono):
return audio_np
offset = (earliest_sample // window) * window
search_end = min(len(mono), earliest_sample + int(search_seconds * sample_rate))
frame_count = max(0, (search_end - offset) // window)
min_silence_windows = max(1, int(np.ceil(min_silence_seconds * sample_rate / window)))
if frame_count < min_silence_windows:
return audio_np
active = _rms_windows(mono, window, frame_count, offset=offset) > threshold
for idx in range(0, len(active) - min_silence_windows + 1):
if not active[idx : idx + min_silence_windows].any():
cut_sample = min(len(audio_np), offset + idx * window + int(keep_silence_seconds * sample_rate))
if cut_sample <= 0 or cut_sample >= len(audio_np):
return audio_np
_debug_print(debug, label, f"Trimmed chunk tail at silence boundary ({cut_sample / sample_rate:.2f}s)")
return audio_np[:cut_sample]
return audio_np
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