silence trimmer
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@ -4,6 +4,7 @@ Falls back to a basic FFmpeg noise filter if DeepFilterNet is not installed.
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"""
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import logging
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import re
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import subprocess
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import tempfile
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from pathlib import Path
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@ -77,3 +78,54 @@ def _clean_with_ffmpeg(input_path: str, output_path: str) -> str:
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def is_deepfilter_available() -> bool:
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return DEEPFILTER_AVAILABLE
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def detect_silence_ranges(input_path: str, min_silence_ms: int, silence_db: float):
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"""Detect silence ranges using ffmpeg silencedetect.
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Returns a list of dicts: {start, end, duration} in seconds.
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"""
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min_silence_seconds = max(0.05, float(min_silence_ms) / 1000.0)
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noise_threshold = float(silence_db)
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cmd = [
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"ffmpeg",
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"-hide_banner",
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"-i",
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input_path,
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"-af",
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f"silencedetect=noise={noise_threshold}dB:d={min_silence_seconds}",
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"-f",
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"null",
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"-",
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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# silencedetect prints to stderr even on success.
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output = result.stderr or ""
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start_pat = re.compile(r"silence_start:\s*([0-9.]+)")
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end_pat = re.compile(r"silence_end:\s*([0-9.]+)\s*\|\s*silence_duration:\s*([0-9.]+)")
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starts = [float(m.group(1)) for m in start_pat.finditer(output)]
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ends = [(float(m.group(1)), float(m.group(2))) for m in end_pat.finditer(output)]
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ranges = []
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pair_count = min(len(starts), len(ends))
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for i in range(pair_count):
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start = max(0.0, starts[i])
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end, duration = ends[i]
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if end > start and duration >= min_silence_seconds:
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ranges.append({
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"start": round(start, 3),
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"end": round(end, 3),
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"duration": round(duration, 3),
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})
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logger.info(
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"Detected %s silence ranges in %s (min=%sms, threshold=%sdB)",
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len(ranges),
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input_path,
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min_silence_ms,
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silence_db,
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)
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return ranges
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