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Add idents playback section — loads from idents/ folder, plays on ads channel

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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
tcpsyn
2026-02-23 22:24:40 -07:00
parent b1bd4ed365
commit bbcf767a8f
7 changed files with 237 additions and 17 deletions
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16
postprod.py
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@@ -3,7 +3,7 @@
Usage: python postprod.py recordings/2026-02-07_213000/ -o episode.mp3
Processes 5 aligned WAV stems (host, caller, music, sfx, ads) into a
Processes 6 aligned WAV stems (host, caller, music, sfx, ads, idents) into a
broadcast-ready MP3 with gap removal, voice compression, music ducking,
and loudness normalization.
"""
@@ -17,7 +17,7 @@ from pathlib import Path
import numpy as np
import soundfile as sf
STEM_NAMES = ["host", "caller", "music", "sfx", "ads"]
STEM_NAMES = ["host", "caller", "music", "sfx", "ads", "idents"]
def load_stems(stems_dir: Path) -> tuple[dict[str, np.ndarray], int]:
@@ -69,7 +69,7 @@ def remove_gaps(stems: dict[str, np.ndarray], sr: int,
# Detect gaps in everything except music (which always plays).
# This catches TTS latency gaps while protecting ad breaks and SFX transitions.
content = stems["host"] + stems["caller"] + stems["sfx"] + stems["ads"]
content = stems["host"] + stems["caller"] + stems["sfx"] + stems["ads"] + stems["idents"]
rms = compute_rms(content, window_samples)
# Threshold: percentile-based to sit above the mic noise floor
@@ -386,7 +386,7 @@ def apply_ducking(music: np.ndarray, dialog: np.ndarray, sr: int,
def match_voice_levels(stems: dict[str, np.ndarray], target_rms: float = 0.1) -> dict[str, np.ndarray]:
"""Normalize host, caller, and ads stems to the same RMS level."""
for name in ["host", "caller", "ads"]:
for name in ["host", "caller", "ads", "idents"]:
audio = stems[name]
# Only measure non-silent portions
active = audio[np.abs(audio) > 0.001]
@@ -408,7 +408,7 @@ def mix_stems(stems: dict[str, np.ndarray],
levels: dict[str, float] | None = None,
stereo_imaging: bool = True) -> np.ndarray:
if levels is None:
levels = {"host": 0, "caller": 0, "music": -6, "sfx": -10, "ads": 0}
levels = {"host": 0, "caller": 0, "music": -6, "sfx": -10, "ads": 0, "idents": 0}
gains = {name: 10 ** (db / 20) for name, db in levels.items()}
@@ -417,7 +417,7 @@ def mix_stems(stems: dict[str, np.ndarray],
if stereo_imaging:
# Pan positions: -1.0 = full left, 0.0 = center, 1.0 = full right
# Using constant-power panning law
pans = {"host": 0.0, "caller": 0.15, "music": 0.0, "sfx": 0.0, "ads": 0.0}
pans = {"host": 0.0, "caller": 0.15, "music": 0.0, "sfx": 0.0, "ads": 0.0, "idents": 0.0}
# Music gets stereo width via slight L/R decorrelation
music_width = 0.3
@@ -774,7 +774,7 @@ def main():
print(f"\n[3/{total_steps}] Limiting ads + SFX...")
with tempfile.TemporaryDirectory() as tmp:
tmp_dir = Path(tmp)
for name in ["ads", "sfx"]:
for name in ["ads", "sfx", "idents"]:
if np.any(stems[name] != 0):
stems[name] = limit_stem(stems[name], sr, tmp_dir, name)
@@ -834,7 +834,7 @@ def main():
dialog = stems["host"] + stems["caller"]
if np.any(dialog != 0) and np.any(stems["music"] != 0):
stems["music"] = apply_ducking(stems["music"], dialog, sr, duck_db=args.duck_amount,
mute_signal=stems["ads"])
mute_signal=stems["ads"] + stems["idents"])
print(" Applied")
else:
print(" No dialog or music to duck")