Precise Bilingual Subtitle

Precise Bilingual Subtitle Production

Produce hardcoded bilingual (English + Chinese) video subtitles with Whisper word-level timing, not YouTube's unreliable auto-captions. Supports full visual customization: font, size, color, stroke, and bottom margin.

When to use this skill

• YouTube auto-captions have visible timing drift (the most common failure)
• User wants yellow, large, or otherwise custom-colored subtitles
• Subtitle timing must be frame-accurate and match lip movements
• Chinese YouTube subtitles hit HTTP 429 and cannot be downloaded
• User wants English-on-top / Chinese-on-bottom bilingual layout

Core insight: why YouTube auto-captions fail

YouTube auto-generated captions are aligned to the entire video stream at once, not word-by-word. Result: systematic offset, drift over time, and poor sync with speech. Local Whisper transcribes with per-word timestamps from the extracted audio, producing timing that is reliable enough for professional delivery.

Proven workflow

Phase 1: Extract audio and transcribe with Whisper

# 1. Extract mono 16kHz audio from the source video
ffmpeg -y -i source.mp4 -vn -ac 1 -ar 16000 audio.wav

# 2. Run Whisper with word-level timing (turbo model, ~30s for 5-min clip)
whisper audio.wav --model turbo --language en --task transcribe \
  --output_format srt --output_dir .

Whisper's SRT output has per-word-level timestamps — each entry is 1-3 words with precise start/end times. This is the foundation of accurate subtitle sync.

Phase 2: Group raw fragments into readable subtitle chunks

Raw Whisper output has hundreds of tiny fragments. Group them into readable subtitle blocks:

import re

def group_whisper_srt(srt_path, min_words=12):
    """Group raw Whisper fragments into readable subtitle chunks."""
    # Parse SRT...
    grouped = []
    buf, start, end = [], None, None
    
    for item in raw_items:
        if start is None:
            start = item['start']
        end = item['end']
        buf.append(item['text'])
        
        wc = len(' '.join(buf).split())
        # Group by word count OR sentence-ending punctuation
        if wc >= min_words or item['text'].strip().endswith(('.', '?', '!', ':', '."')):
            merged = ' '.join(buf)
            merged = re.sub(r'\s+([,.;?!])', r'\1', merged)
            grouped.append({'start': start, 'end': end, 'en': merged})
            buf, start, end = [], None, None
    
    # Don't forget remaining
    if buf:
        grouped.append({'start': start, 'end': end, 'en': ' '.join(buf)})
    
    return grouped

Key parameters:

• min_words=12: produces ~70-80 subtitles for a 5-min video — readable without being choppy
• Sentence-ending punctuation triggers a split regardless of word count
• Result: each subtitle is 2-7 seconds, matching natural speech rhythm

Phase 3: Translate to Chinese

Use deep-translator (Google Translate backend, no API key needed):

from deep_translator import GoogleTranslator

en_texts = [g['en'] for g in grouped]
translator = GoogleTranslator(source='en', target='zh-CN')
zh_results = translator.translate_batch(en_texts)

This works reliably on macOS with pip install deep-translator. No OpenAI key, no API billing.

Phase 4: Build bilingual SRT

SRT format uses \N for newline within a subtitle event:

1
00:00:00,000 --> 00:00:06,500
English text on top line\N中文翻译在下面一行

Build each entry:

for i, g in enumerate(grouped):
    lines.append(str(i+1))
    lines.append(f"{g['start']} --> {g['end']}")
    lines.append(f"{g['en']}\\N{zh_results[i]}")
    lines.append('')

Phase 5: Hardcode subtitles with custom styling

Use the patched hardcode_bilingual_srt.py script from video-bilingual-subtitle-delivery:

python3 hardcode_bilingual_srt.py \
  --video source.mp4 \
  --srt bilingual.srt \
  --output final_output.mp4 \
  --font-size 34 \
  --zh-text-color '255,235,59,255' \
  --text-color '255,255,255,255' \
  --stroke-color '0,0,0,255' \
  --bottom-margin 56 \
  --font '/System/Library/Fonts/STHeiti Light.ttc'

Visual parameters and their effects:

Color presets tested on this machine:

• Consulting / Professional Dual-Color (Tested Default): Chinese Yellow (255,235,59,255), English White (255,255,255,255), Size 34, Bottom Margin 56
• Classic Yellow on black: --text-color 255,255,0,255 --stroke-color 0,0,0,255
• White on black: --text-color 255,255,255,255 --stroke-color 0,0,0,255

Phase 6: Fix the concat duration bug

The hardcode script's ffconcat method can produce output videos slightly longer than the source. Always trim the final output:

# Get source duration
SRC_DUR=$(ffprobe -v error -show_entries format=duration -of csv=p=0 source.mp4)

# Trim output to match source exactly
ffmpeg -y -i output_raw.mp4 -t $SRC_DUR -c copy output_final.mp4

This is critical — without trimming, the output may be 5-160s longer than the source due to ffconcat rounding.

Complete one-shot pipeline

#!/bin/bash
# All-in-one: YouTube URL → yellow bilingual hardcoded MP4
URL="$1"
OUTDIR="$2"

# Download
yt-dlp -f 'bv*[height<=720][ext=mp4]+ba[ext=m4a]/b[height<=720]' -o "$OUTDIR/source.mp4" "$URL"

# Extract audio + transcribe
ffmpeg -y -i "$OUTDIR/source.mp4" -vn -ac 1 -ar 16000 "$OUTDIR/audio.wav"
whisper "$OUTDIR/audio.wav" --model turbo --language en --output_format srt --output_dir "$OUTDIR/"

# Group, translate, build SRT (use the Python script from this skill)
python3 group_and_translate.py "$OUTDIR/audio.srt" "$OUTDIR/bilingual.srt"

# Hardcode
python3 hardcode_bilingual_srt.py \
  --video "$OUTDIR/source.mp4" \
  --srt "$OUTDIR/bilingual.srt" \
  --output "$OUTDIR/final_raw.mp4" \
  --font-size 34 \
  --zh-text-color '255,235,59,255' \
  --text-color '255,255,255,255' \
  --stroke-color '0,0,0,255' \
  --bottom-margin 56

# Trim
SRC_DUR=$(ffprobe -v error -show_entries format=duration -of csv=p=0 "$OUTDIR/source.mp4")
ffmpeg -y -i "$OUTDIR/final_raw.mp4" -t "$SRC_DUR" -c copy "$OUTDIR/final.mp4"

Important pitfalls

Pitfall 1: YouTube auto-captions always drift

Never trust YouTube auto-caption timestamps for subtitle burning. Even when they look "close enough," the offset accumulates. Always use local Whisper.

Pitfall 2: Whisper model choice

• turbo: ~30s for 5-min clip, good enough quality for English → ✅ default
• medium: ~2-3x slower, slightly better for accented speech
• large: Overkill for subtitle production, use only for archival transcription

Pitfall 3: Chinese font registration

• STHeiti Light.ttc (macOS system font): ✅ tested, handles both EN and ZH
• Arial Unicode.ttf: ✅ fallback, works but less elegant for Chinese
• PingFang SC: ❌ may fail to register in PIL due to outline format issues

Pitfall 4: Concat duration bug

The hardcode script's ffconcat approach produces an overlay MOV that may be longer than the source. Always post-process with ffmpeg -t <source_duration> -c copy to trim the output to the exact source length. Without this fix, the final video will have extra blank frames at the end.

Pitfall 5: Chinese subtitle download 429

YouTube's Chinese auto-translated subtitle tracks frequently hit HTTP 429 even when --list-subs shows them as available. Don't waste time retrying — use the translation fallback (deep_translator) immediately.

Deliverable checklist

• Source video downloaded (practical resolution, ≤720p unless HQ needed)
• Audio extracted (mono 16kHz)
• Whisper transcription complete (raw SRT saved)
• Fragments grouped into readable subtitle chunks (12+ words per chunk)
• Chinese translation complete (deep_translator batch)
• Bilingual SRT built with \N separator
• Hardcode rendered with desired font/color/size
• Output trimmed to match source duration exactly
• Chinese characters render correctly (no garbling)
• Timing verified (subtitles appear/disappear with speech)

Dependencies

pip install openai-whisper deep-translator pysubs2 Pillow
# macOS system fonts: STHeiti Light.ttc or Arial Unicode.ttf (pre-installed)