Detect Speech Segments in a Video
Pick a video — its audio is scanned for speech right in your browser (nothing is uploaded) and the speech / non-speech timestamps come back as a downloadable report.
About this tool
This tool answers one question about any video: when is someone speaking, and when not? It runs voice-activity detection (VAD) on the audio track and returns the timestamps of every speech and non-speech segment — it does not transcribe words and it does not modify the video. The timestamps are what editors, subtitle workflows, and scripts consume: jump-cut lists, subtitle scaffolds, ad-break or dead-air audits, "how much of this lecture is actually talking" statistics.
Detection is energy-based, the same technique audio engineers use with ffmpeg by hand:
- Band-pass — with Voice band only on (the default), the audio is first filtered to 200–3000 Hz, where speech lives. Low rumble (traffic, handling noise) and high hiss stop counting as "sound", so the result tracks voices, not noise.
- Detect — ffmpeg's
silencedetectfilter finds every stretch quieter than the silence threshold that lasts at least the min pause. Everything else is speech. - Clean up — bursts shorter than min burst (coughs, clicks, door slams) are folded back into the surrounding non-speech, and optional padding widens each speech segment so soft word edges aren't clipped when the timestamps drive a cutter.
Everything runs in your browser via WebAssembly ffmpeg — the file is never uploaded.
Worked example
A 6-second clip where someone talks for 2 seconds, pauses for 2, and talks 2 more. With the defaults (threshold -30 dB, min pause 0.5 s, min burst 0.25 s), the report reads:
speech: 2 segments, 4.000s of 6.000s (66.7%)
non-speech: 1 segment, 2.000s (33.3%)
#1 0:00.000 → 0:02.000 speech 2.000s
#2 0:02.000 → 0:04.000 non-speech 2.000s
#3 0:04.000 → 0:06.000 speech 2.000s
Switch Output format to CSV for start,end,duration,label rows, to SRT for
subtitle-timed segments (a ready-made scaffold if you transcribe afterwards), or to an
Audacity label track you can import over the original audio. List narrows the rows
to speech-only or non-speech-only — the summary always covers both.
Tuning: raise the threshold (e.g. -20 dB) for noisy recordings where hiss keeps counting as speech; lower it (e.g. -45 dB) for quiet or distant speakers. A larger min pause (1–2 s) keeps whole sentences in one segment; a smaller one (0.2 s) splits on every breath.
Limits and edge cases
- Detection is loudness-based, not a neural VAD — loud non-voice sound inside the voice band (music, applause) counts as "speech", and whispering below the threshold counts as silence. For broadcast-grade VAD or speaker diarization you need an ML model, which this tool deliberately doesn't ship.
- No transcription — the output is timestamps and labels only.
- The file needs an audio track: a silent screen recording errors out with a clear message rather than reporting "all silence".
- Via chat/CLI the input is capped at 10 MiB — compress or trim longer footage first (in the browser the practical limit is your device's memory).
- A clip that is entirely below the threshold reports zero speech segments plus a hint to lower the threshold — that's the tool refusing to guess, not a failure.
- Segments are reported as detected; very long speech runs are not auto-split into chunks (no max-duration option).
FAQ
Does this transcribe what is said?
No. It only detects when speech happens, not what is said. That's the point: the timestamps are useful on their own (cut lists, subtitle timing, silence audits) and the detection runs entirely in your browser with no ML model download. If you need words, run a transcriber afterwards — the SRT output gives it perfectly timed empty slots.
How is this different from the silence-removal tools?
Same detection, different deliverable. The silence-cut tools re-encode your video with the quiet parts removed; this one only reports the timestamps and leaves the video untouched. Use it when you want to inspect first, feed an editor or script, or keep the original file — the CSV rows paste straight into a cutter's keep/remove list.
What exactly do "speech" and "non-speech" mean here?
A segment is speech when its audio stays louder than the silence threshold (measured in the 200–3000 Hz voice band by default) for at least the min-burst duration. Non-speech is everything else: silence, room tone, and anything quieter than the threshold. It's an energy detector — a barking dog at full volume in the voice band will be labeled speech, and a whisper under the threshold won't.
Which output format should I pick?
Report for reading: a summary (segment counts, seconds, speech percentage) plus a numbered list. CSV for spreadsheets and scripts. SRT when the segments should line up with subtitle tooling — each speech segment becomes one correctly-timed entry. Audacity label track to eyeball the detection against the waveform: import it in Audacity via the label-track import and the segments appear under the audio.
Why does my video report 100% speech (or 100% silence)?
The threshold doesn't match the recording's level. Background music or constant noise above -30 dB makes everything "speech" — raise the threshold toward -20 dB, or leave Voice band only on so out-of-band noise is ignored. A quiet speaker far from the microphone can sit entirely below -30 dB — lower the threshold to -45 dB or beyond. The dB scale is relative to full scale: 0 dB is the loudest possible sample.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool video-speech-segment-detector 'url=https://example.com/input' 'threshold_db=-30' 'min_silence=0.5' 'min_speech=0.25' 'pad=0' 'voice_band=true' 'segments=both' 'output=report'New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/video-speech-segment-detector/?url=https://example.com/input&threshold_db=-30&min_silence=0.5&min_speech=0.25&pad=0&voice_band=true&segments=both&output=reportMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.