narrate-video（讲述视频）

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

# 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:

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

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:

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:

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:

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:

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

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

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

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:

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