Hermes Agent
4590dc0fb9
Sermon-clean v0.2.0. Five new subcommands that round out the editor: - normalize (norm): Apply EBU R128 two-pass loudness normalization. Default -16 LUFS (podcast/YouTube). Configurable target LUFS, true peak, and loudness range. Output reports measured input loudness and applied gain offset. - silence-stats (ss): Quantitative summary of silence distribution — count, total/mean/median/longest silence, silence fraction, and silence runs per minute. Outputs JSON via --json. Useful for comparing recordings and picking the right threshold. - threshold-tune (tt): Auto-pick the silence threshold for the audio. Scans a set of candidate thresholds (default -25..-50), scores each against the target silence-runs-per-minute (default 4.0), picks the closest match. Shows the full scoring table. - denoise: Apply ffmpeg's afftdn filter for light FFT-based noise reduction. Configurable noise reduction dB (default 12) and noise floor dB (default -50). Output at 48kHz to match normalize. - batch: Run any of the subcommands across many files via glob. Output goes to --output-dir with --suffix (default '-fixed') and optional --extension override. Failures are collected, not raised — one bad file doesn't kill the whole batch. Implementation: - sermon_clean/processing.py: normalize_loudness + SilenceStats dataclass + silence_stats + threshold_tune. - sermon_clean/denoise.py: DenoiseResult + denoise. - sermon_clean/batch.py: run_batch + _expand_globs + _make_output_path. - sermon_clean/cli.py: 5 new cmd_* functions + 5 subparser registrations. Tests: - tests/test_processing.py (7 tests): silence-stats on silent vs loud, threshold-tune picks closest, normalize produces output + measures loud. - tests/test_denoise_batch.py (11 tests): denoise roundtrip, batch helpers (glob expansion, output naming), batch run with normalize + denoise, unknown subcommand raises, one-bad-file-in-batch continues. Total: 82/82 tests passing in 48s (was 64/64). Bumped version to 0.2.0. README updated: step-by-step workflow adds 1d-1g; subcommand table adds the 5 new commands; new 'Batch processing' section.
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
"""Light noise reduction via ffmpeg's afftdn filter.
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Use cases:
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- Recordings with hiss (cheap mic preamp, RF interference)
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- Recordings with AC hum (50/60Hz line noise — there's a separate `hum` filter
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for that, not implemented here)
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- Tapes digitized with analog tape hiss
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The `afftdn` filter is ffmpeg's built-in adaptive FFT denoiser. It's
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lightweight (CPU-friendly) and good for gentle hiss removal without
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artifacting speech. For heavy noise, consider a real denoiser like
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RNNoise or DeepFilterNet — out of scope for sermon-clean.
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"""
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from __future__ import annotations
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import subprocess
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from dataclasses import dataclass
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from pathlib import Path
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@dataclass
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class DenoiseResult:
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output_path: Path
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noise_floor_db: float
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noise_reduction_db: float
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def to_dict(self) -> dict:
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return {
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"output_path": str(self.output_path),
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"noise_floor_db": self.noise_floor_db,
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"noise_reduction_db": self.noise_reduction_db,
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}
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def denoise(
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src: Path,
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out: Path,
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*,
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noise_reduction_db: float = 12.0,
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noise_floor_db: float = -50.0,
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) -> DenoiseResult:
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"""Apply FFT-based noise reduction.
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Args:
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src: input audio file
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out: output audio file
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noise_reduction_db: how much to attenuate the noise component (dB).
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Higher = more aggressive. 12dB is a reasonable default; 20dB
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starts to artifact speech.
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noise_floor_db: expected level of the noise floor below which everything
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is considered noise. -50dB is conservative. If your recording has
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louder noise (e.g. AC hum at -35dB), set this higher.
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Returns:
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DenoiseResult with the measured parameters.
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"""
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src = Path(src)
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out = Path(out)
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proc = subprocess.run([
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"ffmpeg", "-y", "-v", "error",
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"-i", str(src),
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"-af", f"afftdn=nr={noise_reduction_db}:nf={noise_floor_db}",
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"-ar", "48000", # output 48kHz to match normalize convention
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str(out),
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], capture_output=True, text=True)
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if proc.returncode != 0:
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raise RuntimeError(f"denoise failed: {proc.stderr}")
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return DenoiseResult(
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output_path=out,
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noise_floor_db=noise_floor_db,
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noise_reduction_db=noise_reduction_db,
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)
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