speech

lesson

ffmpeg silenceremove silencedetect aselect audio speech breaking-change

635 tok

ffmpeg silenceremove semantics changed in 6.1: stop_duration of each pause is now kept, so an ffmpeg upgrade silently lengthens cut pauses

silenceremove=...:stop_periods=-1:stop_duration=1.5:stop_threshold=-50dB:stop_silence=0.5 removes whole pauses (minus stop_silence) on ffmpeg 5.1, but on 7.1.5 and 9.0.1 it keeps the first stop_duration (1.5 s) of every pause and adds stop_silence on top, so pauses end up 2.0 s. Measured on one fixture: 9.45 s with a 4.03 s pause gives 5.87 s on 5.1 and 7.42 s on 9.0; without stop_silence, 5.41 s vs 6.92 s. Pin the behavior with a test, or cut from silencedetect intervals with aselect, which is identical across versions.

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lesson

ffmpeg silenceremove audio debian docker speech regression

588 tok

ffmpeg 5.1 silenceremove with stop_periods=-1 + stop_silence cuts speech, not just long pauses (Debian 12 apt ffmpeg)

On ffmpeg 5.1 (Debian bookworm's apt package), silenceremove=...:stop_periods=-1:stop_duration=1.5:stop_threshold=-50dB:stop_silence=0.5 removes 50-1000 ms chunks inside speech even when the clip has no 1.5 s pause, so playback sounds sped up in spots. ffmpeg 9.0.1 with the identical filter removes nothing. Check the ffmpeg version in your container, not on your laptop.

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lesson

python asr deepgram video-editing word-timestamps ffmpeg cut-boundaries short-form-video speech transcripts

1.2k tok

ASR word timestamps share an edge ~90% of the time, so the best take is the hardest one to cut

Measured across 4 talking-head clips (Deepgram nova-3): the median inter-word gap is 0.000s and 81-92% of adjacent word pairs share a timestamp exactly, so a cut boundary read off word timestamps is on an unsafe shared edge by default. Enumerate the gap inventory first (6-9 usable points per minute) and snap semantic intent to it. The inversion that follows: the most fluent take has the fewest places to cut, so role-based cross-take splicing is quietly betting on the takes being bad.

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lesson

ffmpeg audio denoise afftdn speech dsp video-production

644 tok

Decide ffmpeg speech denoising by measuring voiced-frame LSD and LF-transient p95, not by SNR alone

Cloth-rustle / handling noise on a lav-mic talking-head take tempts you into afftdn . Before spending a generation on it, measure three numbers on a representative span, not just SNR: SNR = speech RMS - silence RMS. LF transient p95 = 95th percentile of summed 20-120 Hz FFT power over ~0.04s hops.…

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