Video Clipping Reference

# Best video up to 1080p + best audio, merged
yt-dlp -f "bv[height<=1080]+ba/b[height<=1080]" --restrict-filenames -o "source.%(ext)s" "URL"

# 720p max (smaller, faster)
yt-dlp -f "bv[height<=720]+ba/b[height<=720]" --restrict-filenames -o "source.%(ext)s" "URL"

# Audio only (for transcription-only workflows)
yt-dlp -x --audio-format wav --restrict-filenames -o "audio.%(ext)s" "URL"

# Get full metadata as JSON (duration, title, chapters, available subs)
yt-dlp --dump-json "URL"

# Key fields: duration, title, description, chapters, subtitles, automatic_captions

# Download auto-generated subtitles in json3 format (word-level timing)
yt-dlp --write-auto-subs --sub-lang en --sub-format json3 --skip-download --restrict-filenames -o "source" "URL"

# Download manual subtitles if available
yt-dlp --write-subs --sub-lang en --sub-format srt --skip-download --restrict-filenames -o "source" "URL"

# List available subtitle languages
yt-dlp --list-subs "URL"

# Extract mono 16kHz WAV (whisper's preferred input format)
ffmpeg -i source.mp4 -vn -ar 16000 -ac 1 -y audio.wav

# Standard transcription with word-level timestamps
whisper audio.wav --model small --output_format json --word_timestamps true --language en

# Faster alternative (same flags, 4x speed)
whisper-ctranslate2 audio.wav --model small --output_format json --word_timestamps true --language en

{
  "text": "full transcript text...",
  "segments": [
    {
      "id": 0,
      "start": 0.0,
      "end": 4.52,
      "text": " Hello everyone, welcome back.",
      "words": [
        {"word": " Hello", "start": 0.0, "end": 0.32, "probability": 0.95},
        {"word": " everyone,", "start": 0.32, "end": 0.78, "probability": 0.91},
        {"word": " welcome", "start": 0.78, "end": 1.14, "probability": 0.98},
        {"word": " back.", "start": 1.14, "end": 1.52, "probability": 0.97}
      ]
    }
  ]
}

{
  "events": [
    {
      "tStartMs": 1230,
      "dDurationMs": 5000,
      "segs": [
        {"utf8": "hello ", "tOffsetMs": 0},
        {"utf8": "world ", "tOffsetMs": 200},
        {"utf8": "how ", "tOffsetMs": 450},
        {"utf8": "are you", "tOffsetMs": 700}
      ]
    }
  ]
}

1
00:00:00,000 --> 00:00:02,500
First line of caption text

2
00:00:02,500 --> 00:00:05,100
Second line of caption text

ffmpeg -i clip.mp4 -vf "subtitles=clip.ass:force_style='FontSize=22,FontName=Arial,Bold=1,PrimaryColour=&H00FFFFFF,OutlineColour=&H00000000,Outline=2,Shadow=1,Alignment=2,MarginV=40'" -c:a copy output.mp4

ffmpeg -i input.mp4 -filter:v "select='gt(scene,0.3)',showinfo" -f null - 2>&1

ffmpeg -i input.mp4 -af "silencedetect=noise=-30dB:d=1.5" -f null - 2>&1

# Re-encoded (accurate cuts)
ffmpeg -ss 00:01:30 -to 00:02:15 -i input.mp4 -c:v libx264 -c:a aac -preset fast -crf 23 -movflags +faststart -y clip.mp4

# Lossless copy (fast but may have keyframe alignment issues)
ffmpeg -ss 00:01:30 -to 00:02:15 -i input.mp4 -c copy -y clip.mp4

# Center crop (when source is 16:9)
ffmpeg -i input.mp4 -vf "crop=ih*9/16:ih:(iw-ih*9/16)/2:0,scale=1080:1920" -c:a copy output.mp4

# Scale with letterbox padding (preserves full frame)
ffmpeg -i input.mp4 -vf "scale=1080:1920:force_original_aspect_ratio=decrease,pad=1080:1920:(ow-iw)/2:(oh-ih)/2:black" -c:a copy output.mp4

# SRT subtitles with styling (use relative path or forward-slash absolute path)
ffmpeg -i input.mp4 -vf "subtitles=subs.srt:force_style='FontSize=22,FontName=Arial,PrimaryColour=&H00FFFFFF,OutlineColour=&H00000000,Outline=2,Alignment=2,MarginV=40'" -c:a copy output.mp4

# Simple text overlay
ffmpeg -i input.mp4 -vf "drawtext=text='Caption':fontsize=48:fontcolor=white:borderw=3:bordercolor=black:x=(w-text_w)/2:y=h-th-40" output.mp4

# At specific time (2 seconds in)
ffmpeg -i input.mp4 -ss 2 -frames:v 1 -q:v 2 -y thumb.jpg

# Best keyframe
ffmpeg -i input.mp4 -vf "select='eq(pict_type,I)',scale=1280:720" -frames:v 1 thumb.jpg

# Contact sheet
ffmpeg -i input.mp4 -vf "fps=1/10,scale=320:-1,tile=4x4" contact.jpg

# Full metadata (JSON)
ffprobe -v quiet -print_format json -show_format -show_streams input.mp4

# Duration only
ffprobe -v error -show_entries format=duration -of csv=p=0 input.mp4

# Resolution
ffprobe -v error -select_streams v:0 -show_entries stream=width,height -of csv=p=0 input.mp4

curl -s -X POST "https://api.groq.com/openai/v1/audio/transcriptions" \
  -H "Authorization: Bearer $GROQ_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@audio.wav" \
  -F "model=whisper-large-v3" \
  -F "response_format=verbose_json" \
  -F "timestamp_granularities[]=word" \
  -o transcript_raw.json

curl -s -X POST "https://api.openai.com/v1/audio/transcriptions" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@audio.wav" \
  -F "model=whisper-1" \
  -F "response_format=verbose_json" \
  -F "timestamp_granularities[]=word" \
  -o transcript_raw.json

curl -s -X POST "https://api.deepgram.com/v1/listen?model=nova-2&smart_format=true&utterances=true&punctuate=true" \
  -H "Authorization: Token $DEEPGRAM_API_KEY" \
  -H "Content-Type: audio/wav" \
  --data-binary @audio.wav \
  -o transcript_raw.json

# List available voices
edge-tts --list-voices

# Generate speech
edge-tts --text "Your caption text here" --voice en-US-AriaNeural --write-media tts_output.mp3

# Other good voices: en-US-GuyNeural, en-GB-SoniaNeural, en-AU-NatashaNeural

curl -s -X POST "https://api.openai.com/v1/audio/speech" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"tts-1","input":"Your text here","voice":"alloy"}' \
  --output tts_output.mp3