Once these tweaks are made, the article will be ready for publication on your website, newsletter, or content hub. Happy writing!
Because I don’t have direct access to the video file itself, I’ve populated the template with the kinds of information you’ll typically want to extract, along with brief guidance on how to obtain each section.
You can fill in the placeholders (highlighted in bold) with the actual data from your own inspection or by using the suggested tools/methods. MIDV-354.mp4
| Aspect | Findings | Extraction Method |
|--------|----------|-------------------|
| Audio codec / channels | <e.g., AAC‑LC, stereo, 48 kHz> | ffprobe (see above) |
| Duration (audio) | <same as video or trimmed> | ffprobe |
| Loudness (LUFS) | <e.g., –16 LUFS (broadcast‑norm) > | ffmpeg -i MIDV‑354.mp4 -filter:a loudnorm=I=-16:TP=-1.5:LRA=11 -f null - |
| Speech detection | <Percentage of time containing speech, number of speech segments> | pyannote.audio or webrtcvad |
| Speech‑to‑text transcription | <Full transcript with timestamps> | Whisper (OpenAI), Google Speech‑to‑Text, or Azure Speech Services |
| Speaker diarization | <Speaker‑A, Speaker‑B, … with timestamps> | pyannote.audio diarization pipeline |
| Non‑speech sounds | <e.g., “car horns (3×), applause (5 s), dog bark (2 s)> | Audacity visual inspection or librosa + sound‑event detection model |
| Music detection | <Background music present? Genre, mood> | Essentia music‑classifier or openl3 embeddings + clustering |
| Topic | Observation | Suggested Action |
|-------|-------------|------------------|
| Overall quality | <e.g., “HD video, clear audio, minor compression artifacts near 00:12:30”> | If distribution requires higher fidelity, consider lossless re‑encode of the problematic segment |
| Content suitability | <e.g., “Appropriate for marketing, no explicit material”> | No edits needed |
| Compliance | <e.g., “Contains faces – ensure GDPR consent, blur if necessary”> | Apply face‑blur filter (ffmpeg boxblur on detected face coordinates) |
| Potential reuse | <e.g., “Good for training object‑detection on pedestrians and bicycles”> | Export annotated frames (COCO JSON) |
| Archival | <e.g., “Store original 4K master; keep derived 1080p MP4 for web”> | Create checksum‑verified archive (e.g., .tar.gz + SHA‑256) |
| Further analysis | <e.g., “Run activity‑recognition model to label “walking” vs “running” segments”> | Use pretrained I3D or SlowFast models; produce CSV of labeled intervals | Once these tweaks are made, the article will
| Item | Details | How to Obtain |
|------|---------|----------------|
| File name | MIDV‑354.mp4 | – |
| File size | <size in MB/GB> | ls -lh MIDV‑354.mp4 (Linux/macOS) or file properties (Windows) |
| Duration | <hh:mm:ss> | Media players (VLC, MPV) or ffprobe -i MIDV‑354.mp4 -show_entries format=duration -v quiet -of csv="p=0" |
| Resolution | <width×height> (e.g., 1920×1080) | ffprobe -v error -select_streams v:0 -show_entries stream=width,height -of csv=p=0 MIDV‑354.mp4 |
| Frame rate | <fps> | ffprobe -v error -select_streams v:0 -show_entries stream=r_frame_rate -of default=noprint_wrappers=1:nokey=1 MIDV‑354.mp4 |
| Codec(s) | Video: <codec name>
Audio: <codec name> | ffprobe -v error -show_streams -select_streams v:0,a:0 MIDV‑354.mp4 |
| Bitrate | <kbps> | Same ffprobe command above (bit_rate field) |
| Container format | MP4 (ISO‑BMFF) | ffprobe output |
| Creation / modification date | <date> | stat MIDV‑354.mp4 (Linux/macOS) or file properties |
| MD5 / SHA‑256 checksum | <checksum> | md5sum MIDV‑354.mp4 or sha256sum MIDV‑354.mp4 |
(Add as many as needed.)
| Aspect | Findings | Extraction Method |
|--------|----------|-------------------|
| Scene detection | <Number of scenes, brief description of each (e.g., “Indoor office → outdoor street → night skyline”> | Use PySceneDetect (scenedetect) or FFmpeg’s select filter to dump key‑frame thumbnails |
| Key frames | <List of timestamps + thumbnail images (e.g., 00:00:05, 00:02:12, …)> | ffmpeg -i MIDV‑354.mp4 -vf "select='eq(pict_type\,I)'" -vsync vfr -frame_pts true keyframe_%04d.jpg |
| Dominant colors | <e.g., “Cool blues (45 %), warm oranges (30 %), neutrals (25 %)> | ffmpeg + colorthief or Python’s scikit‑image (skimage.color) |
| Detected objects | <e.g., “Person (x times), Car (y times), Dog (z times), etc.”> | Run an object detector (YOLOv8, Detectron2) on extracted frames; summarize counts |
| Facial analysis | <Number of unique faces, demographics, emotions if relevant> | insightface or deepface; optionally blur faces for privacy |
| Text/OCR | <Any visible on‑screen text, timestamps, subtitles, signs…> | Tesseract OCR on frames where text is present |
| Motion / activity | <E.g., “Walking, running, vehicle traffic, camera pans, zooms”> | Use optical‑flow or activity‑recognition models (e.g., I3D) |
| Special effects / overlays | <Graphics, logos, watermarks, subtitles> | Visual inspection + frame differencing |
7z a -t7z -p -mhe=on MIDV-354.7z MIDV-354.mp4