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975f9a3 48c3b28 975f9a3 | 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 | from __future__ import annotations
import os
import tempfile
from dataclasses import dataclass
from typing import Any
import numpy as np
import soundfile as sf
from faster_whisper.audio import decode_audio
@dataclass
class SilenceTrimOptions:
enabled: bool
threshold_db: float
min_silence_sec: float
keep_padding_sec: float
analysis_window_ms: int
def seconds_from_samples(sample_count: int, sample_rate: int) -> float:
return sample_count / float(sample_rate)
def round_sec(value: float) -> float:
return round(value, 4)
def clamp(value: float, low: float, high: float) -> float:
return max(low, min(high, value))
def db_to_amplitude(db_value: float) -> float:
return float(10.0 ** (db_value / 20.0))
def resolve_trim_options(
enabled: bool,
threshold_db: float,
min_silence_sec: float,
keep_padding_sec: float,
analysis_window_ms: int,
) -> SilenceTrimOptions:
threshold_db = clamp(float(threshold_db), -80.0, -5.0)
min_silence_sec = clamp(float(min_silence_sec), 0.02, 10.0)
keep_padding_sec = clamp(float(keep_padding_sec), 0.0, min_silence_sec)
analysis_window_ms = int(max(1, min(250, analysis_window_ms)))
return SilenceTrimOptions(
enabled=bool(enabled),
threshold_db=threshold_db,
min_silence_sec=min_silence_sec,
keep_padding_sec=keep_padding_sec,
analysis_window_ms=analysis_window_ms,
)
def ensure_wav(audio_path: str, sample_rate: int) -> tuple[str, np.ndarray]:
audio = decode_audio(audio_path, sampling_rate=sample_rate)
tmp_dir = tempfile.mkdtemp(prefix="voice-intel-")
wav_path = os.path.join(tmp_dir, "input.wav")
sf.write(wav_path, audio, sample_rate, subtype="PCM_16")
return wav_path, np.asarray(audio, dtype=np.float32)
def save_wav(audio: np.ndarray, wav_path: str, sample_rate: int) -> None:
sf.write(wav_path, np.asarray(audio, dtype=np.float32), sample_rate, subtype="PCM_16")
def detect_silence_runs(
audio: np.ndarray,
sample_rate: int,
threshold_db: float,
min_silence_sec: float,
analysis_window_ms: int,
) -> list[tuple[int, int]]:
if audio.size == 0:
return []
threshold = db_to_amplitude(threshold_db)
window_samples = max(1, int(round((analysis_window_ms / 1000.0) * sample_rate)))
min_silence_samples = max(1, int(round(min_silence_sec * sample_rate)))
frame_ranges: list[tuple[int, int]] = []
frame_silent: list[bool] = []
for start in range(0, len(audio), window_samples):
end = min(len(audio), start + window_samples)
frame_ranges.append((start, end))
if end <= start:
rms = 0.0
else:
chunk = audio[start:end]
rms = float(np.sqrt(np.mean(np.square(chunk.astype(np.float32)))))
frame_silent.append(rms < threshold)
runs: list[tuple[int, int]] = []
run_start: int | None = None
for idx, is_silent in enumerate(frame_silent):
if is_silent and run_start is None:
run_start = idx
elif not is_silent and run_start is not None:
run_sample_start = frame_ranges[run_start][0]
run_sample_end = frame_ranges[idx - 1][1]
if run_sample_end - run_sample_start >= min_silence_samples:
runs.append((run_sample_start, run_sample_end))
run_start = None
if run_start is not None:
run_sample_start = frame_ranges[run_start][0]
run_sample_end = frame_ranges[-1][1]
if run_sample_end - run_sample_start >= min_silence_samples:
runs.append((run_sample_start, run_sample_end))
return runs
def trim_audio(
audio: np.ndarray,
sample_rate: int,
options: SilenceTrimOptions,
) -> tuple[np.ndarray, dict[str, Any], list[tuple[int, int]]]:
raw_duration_sec = round_sec(seconds_from_samples(len(audio), sample_rate))
if not options.enabled:
return audio, {
"enabled": False,
"threshold_db": options.threshold_db,
"min_silence_sec": options.min_silence_sec,
"keep_padding_sec": options.keep_padding_sec,
"analysis_window_ms": options.analysis_window_ms,
"detected_runs": [],
"removed_runs": [],
"removed_silence_sec": 0.0,
"raw_duration_sec": raw_duration_sec,
"processed_duration_sec": raw_duration_sec,
}, []
runs = detect_silence_runs(
audio=audio,
sample_rate=sample_rate,
threshold_db=options.threshold_db,
min_silence_sec=options.min_silence_sec,
analysis_window_ms=options.analysis_window_ms,
)
keep_pad_samples = int(round(options.keep_padding_sec * sample_rate))
removed_intervals: list[tuple[int, int]] = []
for start, end in runs:
remove_start = min(end, start + keep_pad_samples)
remove_end = max(start, end - keep_pad_samples)
if remove_end > remove_start:
removed_intervals.append((remove_start, remove_end))
if not removed_intervals:
return audio, {
"enabled": True,
"threshold_db": options.threshold_db,
"min_silence_sec": options.min_silence_sec,
"keep_padding_sec": options.keep_padding_sec,
"analysis_window_ms": options.analysis_window_ms,
"detected_runs": [_run_payload(start, end, sample_rate) for start, end in runs],
"removed_runs": [],
"removed_silence_sec": 0.0,
"raw_duration_sec": raw_duration_sec,
"processed_duration_sec": raw_duration_sec,
}, []
chunks: list[np.ndarray] = []
cursor = 0
for start, end in removed_intervals:
if start > cursor:
chunks.append(audio[cursor:start])
cursor = end
if cursor < len(audio):
chunks.append(audio[cursor:])
trimmed_audio = np.concatenate(chunks) if chunks else np.zeros(0, dtype=np.float32)
removed_total_samples = sum(end - start for start, end in removed_intervals)
silence_payload = {
"enabled": True,
"threshold_db": options.threshold_db,
"min_silence_sec": options.min_silence_sec,
"keep_padding_sec": options.keep_padding_sec,
"analysis_window_ms": options.analysis_window_ms,
"detected_runs": [_run_payload(start, end, sample_rate) for start, end in runs],
"removed_runs": [_run_payload(start, end, sample_rate) for start, end in removed_intervals],
"removed_silence_sec": round_sec(seconds_from_samples(removed_total_samples, sample_rate)),
"raw_duration_sec": raw_duration_sec,
"processed_duration_sec": round_sec(seconds_from_samples(len(trimmed_audio), sample_rate)),
}
return trimmed_audio, silence_payload, removed_intervals
def _run_payload(start_sample: int, end_sample: int, sample_rate: int) -> dict[str, Any]:
return {
"start_sample": start_sample,
"end_sample": end_sample,
"start_sec": round_sec(seconds_from_samples(start_sample, sample_rate)),
"end_sec": round_sec(seconds_from_samples(end_sample, sample_rate)),
"duration_sec": round_sec(seconds_from_samples(max(0, end_sample - start_sample), sample_rate)),
}
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