narrate-video

Generate professional voiceover narration for a video with audio-video sync using Azure TTS. Use this skill whenever the user wants to add narration, voiceover, commentary, or voice dubbing to any video file — even if they just say "add audio to this video" or "make a narrated version." Also trigger when the user has a screen recording, demo, tutorial, or presentation video that needs a voice track. Trigger on Chinese requests like "视频配音", "给视频加旁白", "录屏解说", "视频加语音", "视频添加声音", "生成视频旁白", "自动配音", "视频解说词".

Video Narration

Add professional voiceover to a video. Analyze the video, write or refine a timed script, generate speech via Azure TTS, and merge — producing a narrated video where audio and visuals stay in sync.

Input: $ARGUMENTS

Additional resources

• Voice table and speech rate estimates: references/voices.md
• Python script template: scripts/narration_script_template.py — copy into the video's directory as narration_script.py and fill in the placeholders

---

Phase 0: Setup

Language

Ask the user which language they want. Default to English. Look up the voice and speech rate in references/voices.md.

Environment

bash

# 1. Check Azure credentials exist (NEVER read or display their values)
scripts/check_env.py

# 2. Check tool dependencies
command -v ffmpeg && command -v ffprobe && command -v python3

# 3. Check Python dependencies
python3 -c "import azure.cognitiveservices.speech; import dotenv" 2>&1

If AZURE_SPEECH_KEY or AZURE_SPEECH_REGION is missing, ask the user to add them to ~/.narrate_video.env:

text

AZURE_SPEECH_KEY=your-key-here
AZURE_SPEECH_REGION=your-region-here

Then stop — the key is sensitive, only check whether it exists, never read or display its value.

---

Phase 1: Video Analysis

1.1 Metadata

bash

ffprobe -v quiet -print_format json -show_format -show_streams <video>

Record total duration, resolution, frame rate, and whether an audio track exists.

1.2 Scene extraction

Extract frames at 3–4 second intervals to identify scene transitions:

bash

mkdir -p /tmp/narration-frames
for t in $(seq 0 3 <duration>); do
    ffmpeg -y -ss $t -i <video> -frames:v 1 -q:v 2 /tmp/narration-frames/frame_${t}s.jpg 2>/dev/null
done

Review the frames (use Read tool to view images). For each scene transition, note the precise timestamp. Where timing is ambiguous, extract additional frames at 1–2 second intervals to pinpoint the exact moment.

1.3 Transition map

Build a scene transition table mapping timestamps to visual content:

text

0s   - Opening screen
3s   - User starts typing
8s   - System begins processing
34s  - Response appears

Narration describing something on screen should start after that content is already visible. Viewers notice when audio arrives before the visuals — it feels disorienting. Narrating slightly after the visual appears feels natural, like a presenter walking you through what you're seeing.

---

Phase 2: Script Writing

Format

Each narration segment is a (start_seconds, text) tuple:

python

SEGMENTS = [
    (0, "Opening narration here."),
    (8, "Next segment narration..."),
]

Writing guidance

Timing: Leave at least 1 second of silence between segments — this breathing room makes narration feel conversational rather than rushed. Use the speech rate from references/voices.md to estimate whether text fits: for English, multiply the window (in seconds) by 2.5 words/sec, then take 80% as the safe word count.

Flow: Each segment should connect logically to the next. Transition words ("And", "Now", "So") help, but vary them — three consecutive "And now" transitions sound robotic.

Adapting to input: If the user provided a draft, calibrate its timestamps against the scene analysis, trim text that overflows its time window, and polish the language — but preserve their intent and key points. Without a draft, write narration for each scene based on what's visible.

Pre-flight check

Before generating audio, verify each segment fits:

text

window = next_segment_start - this_segment_start
max_words = window * words_per_second * 0.8

If a segment is too long, shorten the text now — trimming words is much cheaper than regenerating audio.

---

Phase 3: Generate the Script

Copy scripts/narration_script_template.py into the video's directory as narration_script.py. Fill in:

• VOICE_NAME from the voice table
• INPUT_VIDEO and OUTPUT_VIDEO (relative paths only)
• SEGMENTS from Phase 2

Design notes

These choices come from debugging real production issues:

• normalize=0 on amix: ffmpeg's amix divides volume by input count by default. With 20 segments, output would be 1/20th volume — essentially silent.
• Discarding original audio: Even mixing original audio at 5% volume produces audible double-voice artifacts.
• Aborting on overlap: If any segment's audio extends past the next segment's start time, the script stops and reports the problem. Overlapping audio sounds broken.
• Skipping existing audio files: The script only generates audio for segments without an existing .mp3 file. If you change a segment's text, delete its seg_XXX.mp3 before re-running.

---

Phase 4: Run & Iterate

bash

python3 narration_script.py

If the timing report shows overlaps (gap < 0), decide whether to shorten the text or push the next segment's start time later. If you change text, delete the corresponding narration_segments/seg_XXX.mp3 first. If you only change start times, re-run directly.

Keep iterating until all gaps are non-negative.

---

Phase 5: Verification

Run all three checks after every successful build:

Volume

bash

ffmpeg -i <output> -ss 0 -t 30 -af "volumedetect" -vn -f null - 2>&1 | grep -E "mean_volume|max_volume"

Expect mean_volume between -25 and -15 dB, max between -10 and 0 dB. If mean is below -40 dB, the normalize=0 fix isn't applied — check the filter string.

Silence gaps

bash

ffmpeg -i <output> -af "silencedetect=noise=-30dB:d=0.3" -vn -f null - 2>&1 | grep -E "silence_(start|end)" | head -20

Confirm clean silence between segment transitions. Silence boundaries should match expected segment end/start times.

Audio-video sync

Extract frames at 5–8 key segment start times and view them:

bash

for t in <timestamps>; do
    ffmpeg -y -ss $t -i <output> -frames:v 1 -q:v 2 /tmp/verify_${t}s.jpg 2>/dev/null
done

The on-screen content should already be visible when the narration for that scene begins.

---

Troubleshooting

Symptom	Cause	Fix
Two voices playing	Original audio was mixed in	Only map [final] audio track, never 0:a
Audio nearly silent	amix divided volume by input count	Add :normalize=0 to amix parameters
Narration out of sync	Imprecise scene timestamps	Re-extract frames at 1–2s intervals around the problem area
Overlap at segment boundary	Previous segment runs too long	Shorten that segment's text or delay the next segment
Text changed but audio didn't	Old mp3 file still cached	Delete narration_segments/seg_XXX.mp3 and re-run
Audio cut off at video end	Last segment overflows video duration	Shorten to finish 3–4s before video ends