Initial commit: YouTube Shorts maker application
Features: - Video download from TikTok/Douyin using yt-dlp - Audio transcription with OpenAI Whisper - GPT-4 translation (direct/summarize/rewrite modes) - Subtitle generation with ASS format - Video trimming with frame-accurate preview - BGM integration with volume control - Intro text overlay support - Thumbnail generation with text overlay Tech stack: - Backend: FastAPI, Python 3.11+ - Frontend: React, Vite, TailwindCSS - Video processing: FFmpeg - AI: OpenAI Whisper, GPT-4 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
kihong.kim
468
backend/app/services/translator.py
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468
backend/app/services/translator.py
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import re
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from typing import List, Tuple, Optional
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from openai import OpenAI
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from app.models.schemas import TranscriptSegment
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from app.config import settings
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def get_openai_client() -> OpenAI:
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"""Get OpenAI client."""
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return OpenAI(api_key=settings.OPENAI_API_KEY)
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class TranslationMode:
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"""Translation mode options."""
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DIRECT = "direct" # 직접 번역 (원본 구조 유지)
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SUMMARIZE = "summarize" # 요약 후 번역
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REWRITE = "rewrite" # 요약 + 한글 대본 재작성
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async def shorten_text(client: OpenAI, text: str, max_chars: int) -> str:
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"""
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Shorten a Korean text to fit within character limit.
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Args:
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client: OpenAI client
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text: Text to shorten
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max_chars: Maximum character count
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Returns:
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Shortened text
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"""
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try:
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response = client.chat.completions.create(
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model=settings.OPENAI_MODEL,
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messages=[
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{
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"role": "system",
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"content": f"""한국어 자막을 {max_chars}자 이내로 줄이세요.
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규칙:
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- 반드시 {max_chars}자 이하!
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- 핵심 의미만 유지
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- 자연스러운 한국어
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- 존댓말 유지
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- 출력은 줄인 문장만!
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예시:
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입력: "요리할 때마다 한 시간이 걸리셨죠?" (18자)
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제한: 10자
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출력: "시간 오래 걸리죠" (8자)
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입력: "채소 다듬는 데만 30분 걸리셨죠" (16자)
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제한: 10자
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출력: "채소만 30분" (6자)"""
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},
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{
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"role": "user",
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"content": f"입력: \"{text}\" ({len(text)}자)\n제한: {max_chars}자\n출력:"
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}
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],
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temperature=0.3,
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max_tokens=50,
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)
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shortened = response.choices[0].message.content.strip()
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# Remove quotes, parentheses, and extra characters
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shortened = shortened.strip('"\'""''')
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# Remove any trailing parenthetical notes like "(10자)"
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shortened = re.sub(r'\s*\([^)]*자\)\s*$', '', shortened)
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shortened = re.sub(r'\s*\(\d+자\)\s*$', '', shortened)
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# Remove any remaining quotes
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shortened = shortened.replace('"', '').replace('"', '').replace('"', '')
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shortened = shortened.replace("'", '').replace("'", '').replace("'", '')
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shortened = shortened.strip()
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# If still too long, truncate cleanly
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if len(shortened) > max_chars:
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shortened = shortened[:max_chars]
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return shortened
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except Exception as e:
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# Fallback: simple truncation
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if len(text) > max_chars:
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return text[:max_chars-1] + "…"
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return text
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async def translate_segments(
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segments: List[TranscriptSegment],
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target_language: str = "Korean",
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mode: str = TranslationMode.DIRECT,
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max_tokens: Optional[int] = None,
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) -> Tuple[bool, str, List[TranscriptSegment]]:
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"""
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Translate transcript segments to target language using OpenAI.
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Args:
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segments: List of transcript segments
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target_language: Target language for translation
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mode: Translation mode (direct, summarize, rewrite)
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max_tokens: Maximum output tokens (for cost control)
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Returns:
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Tuple of (success, message, translated_segments)
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"""
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if not settings.OPENAI_API_KEY:
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return False, "OpenAI API key not configured", segments
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try:
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client = get_openai_client()
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# Batch translate for efficiency
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texts = [seg.text for seg in segments]
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combined_text = "\n---\n".join(texts)
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# Calculate video duration for context
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total_duration = segments[-1].end if segments else 0
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# Calculate segment info for length guidance
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segment_info = []
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for i, seg in enumerate(segments):
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duration = seg.end - seg.start
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max_chars = int(duration * 5) # ~5 Korean chars per second (stricter for better sync)
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segment_info.append(f"[{i+1}] {duration:.1f}초 = 최대 {max_chars}자 (엄수!)")
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# Get custom prompt settings from config
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gpt_role = settings.GPT_ROLE or "친근한 유튜브 쇼츠 자막 작가"
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gpt_tone = settings.GPT_TONE or "존댓말"
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gpt_style = settings.GPT_STYLE or ""
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# Tone examples
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tone_examples = {
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"존댓말": '~해요, ~이에요, ~하죠',
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"반말": '~해, ~야, ~지',
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"격식체": '~합니다, ~입니다',
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}
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tone_example = tone_examples.get(gpt_tone, tone_examples["존댓말"])
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# Additional style instruction
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style_instruction = f"\n6. Style: {gpt_style}" if gpt_style else ""
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# Select prompt based on mode
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if mode == TranslationMode.REWRITE:
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# Build indexed timeline input with Chinese text
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# Use segment numbers to handle duplicate timestamps
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timeline_input = []
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for i, seg in enumerate(segments):
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mins = int(seg.start // 60)
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secs = int(seg.start % 60)
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timeline_input.append(f"[{i+1}] {mins}:{secs:02d} {seg.text}")
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system_prompt = f"""당신은 생활용품 유튜브 쇼츠 자막 작가입니다.
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중국어 원문의 "의미"만 참고하여, 한국인이 직접 말하는 것처럼 자연스러운 자막을 작성하세요.
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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🎯 핵심 원칙: 번역이 아니라 "재창작"
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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✅ 필수 규칙:
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1. 한 문장 = 한 가지 정보 (두 개 이상 금지)
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2. 중복 표현 절대 금지 ("편해요"가 이미 나왔으면 다시 안 씀)
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3. {gpt_tone} 사용 ({tone_example})
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4. 세그먼트 수 유지: 입력 {len(segments)}개 → 출력 {len(segments)}개
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5. 중국어 한자 금지, 순수 한글만
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❌ 금지 표현 (번역투):
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- "~할 수 있어요" → "~돼요", "~됩니다"
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- "매우/아주/정말" 남용 → 꼭 필요할 때만
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- "그것은/이것은" → "이거", "이건"
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- "~하는 것이" → 직접 표현으로
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- "편리해요/편해요" 반복 → 한 번만, 이후 다른 표현
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- "좋아요/좋고요" 반복 → 구체적 장점으로 대체
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🎵 쇼츠 리듬감:
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- 짧게 끊어서
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- 한 호흡에 하나씩
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- 시청자가 따라 읽을 수 있게
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📝 좋은 예시:
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원문: "이 작은 박스 디자인이 참 좋네요. 평소에 씨앗 먹을 때 간편하게 먹을 수 있어요."
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❌ 나쁜 번역: "이 작은 박스 디자인이 참 좋네요. 평소에 씨앗 먹을 때 간편하게 먹을 수 있어요."
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✅ 좋은 재창작: "이 작은 박스, 생각보다 정말 잘 만들었어요."
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원문: "테이블에 두거나 손에 들고 사용하기에도 좋고요. 침대에 누워서나 사무실에서도 간식이나 과일 먹기 정말 편해요."
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❌ 나쁜 번역: "테이블에 두거나 손에 들고 사용하기에도 좋고요. 침대에 누워서나 사무실에서도 간식이나 과일 먹기 정말 편해요."
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✅ 좋은 재창작 (2개로 분리):
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- "테이블 위에서도, 침대에서도, 사무실에서도 사용하기 좋고"
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- "과일 씻고 물기 빼는 데도 활용 가능합니다."
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원문: "가정에서 필수 아이템이에요. 정말 유용하죠. 꼭 하나씩 가져야 할 제품이에요."
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❌ 나쁜 번역: 그대로 3문장
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✅ 좋은 재창작: "집에 하나 있으면 은근히 자주 쓰게 됩니다."{style_instruction}
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출력 형식:
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[번호] 시간 자막 내용
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⚠️ 입력과 동일한 세그먼트 수({len(segments)}개)를 출력하세요!
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⚠️ 각 [번호]는 입력과 1:1 대응해야 합니다!"""
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# Use indexed timeline format for user content
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combined_text = "[중국어 원문]\n\n" + "\n".join(timeline_input)
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elif mode == TranslationMode.SUMMARIZE:
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system_prompt = f"""You are: {gpt_role}
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Task: Translate Chinese to SHORT Korean subtitles.
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Length limits (자막 싱크!):
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{chr(10).join(segment_info)}
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Rules:
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1. Use {gpt_tone} ({tone_example})
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2. Summarize to core meaning - be BRIEF
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3. Max one short sentence per segment
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4. {len(segments)} segments separated by '---'{style_instruction}"""
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else: # DIRECT mode
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system_prompt = f"""You are: {gpt_role}
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Task: Translate Chinese to Korean subtitles.
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Length limits (자막 싱크!):
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{chr(10).join(segment_info)}
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Rules:
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1. Use {gpt_tone} ({tone_example})
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2. Keep translations SHORT and readable
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3. {len(segments)} segments separated by '---'{style_instruction}"""
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# Build API request
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request_params = {
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"model": settings.OPENAI_MODEL,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": combined_text}
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],
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"temperature": 0.65 if mode == TranslationMode.REWRITE else 0.3,
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}
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# Add max_tokens if specified (for cost control)
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effective_max_tokens = max_tokens or settings.TRANSLATION_MAX_TOKENS
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if effective_max_tokens:
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# Use higher token limit for REWRITE mode
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if mode == TranslationMode.REWRITE:
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request_params["max_tokens"] = max(effective_max_tokens, 700)
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else:
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request_params["max_tokens"] = effective_max_tokens
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response = client.chat.completions.create(**request_params)
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translated_text = response.choices[0].message.content
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# Parse based on mode
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if mode == TranslationMode.REWRITE:
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# Parse indexed timeline format: "[1] 0:00 자막\n[2] 0:02 자막\n..."
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indexed_pattern = re.compile(r'^\[(\d+)\]\s*\d+:\d{2}\s+(.+)$', re.MULTILINE)
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matches = indexed_pattern.findall(translated_text)
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# Create mapping from segment index to translation
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translations_by_index = {}
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for idx, text in matches:
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translations_by_index[int(idx)] = text.strip()
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# Map translations back to segments by index (1-based)
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for i, seg in enumerate(segments):
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seg_num = i + 1 # 1-based index
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if seg_num in translations_by_index:
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seg.translated = translations_by_index[seg_num]
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else:
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# No matching translation found - try fallback to old timestamp-based parsing
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seg.translated = ""
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# Fallback: if no indexed matches, try old timestamp format
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if not matches:
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print("[Warning] No indexed format found, falling back to timestamp parsing")
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timeline_pattern = re.compile(r'^(\d+):(\d{2})\s+(.+)$', re.MULTILINE)
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timestamp_matches = timeline_pattern.findall(translated_text)
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# Create mapping from timestamp to translation
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translations_by_time = {}
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for mins, secs, text in timestamp_matches:
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time_sec = int(mins) * 60 + int(secs)
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translations_by_time[time_sec] = text.strip()
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# Track used translations to prevent duplicates
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used_translations = set()
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# Map translations back to segments by matching start times
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for seg in segments:
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start_sec = int(seg.start)
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matched_time = None
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# Try exact match first
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if start_sec in translations_by_time and start_sec not in used_translations:
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matched_time = start_sec
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else:
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# Try to find closest UNUSED match within 1 second
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for t in range(start_sec - 1, start_sec + 2):
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if t in translations_by_time and t not in used_translations:
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matched_time = t
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break
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if matched_time is not None:
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seg.translated = translations_by_time[matched_time]
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used_translations.add(matched_time)
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else:
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seg.translated = ""
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else:
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# Original parsing for other modes
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translated_parts = translated_text.split("---")
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for i, seg in enumerate(segments):
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if i < len(translated_parts):
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seg.translated = translated_parts[i].strip()
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else:
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seg.translated = seg.text # Fallback to original
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# Calculate token usage for logging
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usage = response.usage
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token_info = f"(tokens: {usage.prompt_tokens}+{usage.completion_tokens}={usage.total_tokens})"
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# Post-processing: Shorten segments that exceed character limit
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# Skip for REWRITE mode - the prompt handles length naturally
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shortened_count = 0
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if mode != TranslationMode.REWRITE:
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chars_per_sec = 5
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for i, seg in enumerate(segments):
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if seg.translated:
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duration = seg.end - seg.start
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max_chars = int(duration * chars_per_sec)
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current_len = len(seg.translated)
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if current_len > max_chars * 1.3 and max_chars >= 5:
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seg.translated = await shorten_text(client, seg.translated, max_chars)
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shortened_count += 1
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print(f"[Shorten] Seg {i+1}: {current_len}→{len(seg.translated)}자 (제한:{max_chars}자)")
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shorten_info = f" [축약:{shortened_count}개]" if shortened_count > 0 else ""
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return True, f"Translation complete [{mode}] {token_info}{shorten_info}", segments
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except Exception as e:
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return False, f"Translation error: {str(e)}", segments
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async def generate_shorts_script(
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segments: List[TranscriptSegment],
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style: str = "engaging",
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max_tokens: int = 500,
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) -> Tuple[bool, str, Optional[str]]:
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"""
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Generate a completely new Korean Shorts script from Chinese transcript.
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Args:
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segments: Original transcript segments
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style: Script style (engaging, informative, funny, dramatic)
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max_tokens: Maximum output tokens
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Returns:
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Tuple of (success, message, script)
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"""
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if not settings.OPENAI_API_KEY:
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return False, "OpenAI API key not configured", None
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try:
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client = get_openai_client()
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# Combine all text
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full_text = " ".join([seg.text for seg in segments])
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total_duration = segments[-1].end if segments else 0
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style_guides = {
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"engaging": "Use hooks, questions, and emotional expressions. Start with attention-grabbing line.",
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"informative": "Focus on facts and clear explanations. Use simple, direct language.",
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"funny": "Add humor, wordplay, and light-hearted tone. Include relatable jokes.",
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"dramatic": "Build tension and suspense. Use impactful short sentences.",
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}
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|
||||
style_guide = style_guides.get(style, style_guides["engaging"])
|
||||
|
||||
system_prompt = f"""You are a viral Korean YouTube Shorts script writer.
|
||||
|
||||
Create a COMPLETELY ORIGINAL Korean script inspired by the Chinese video content.
|
||||
|
||||
=== CRITICAL: ANTI-PLAGIARISM RULES ===
|
||||
- This is NOT translation - it's ORIGINAL CONTENT CREATION
|
||||
- NEVER copy sentence structures, word order, or phrasing from original
|
||||
- Extract only the CORE IDEA, then write YOUR OWN script from scratch
|
||||
- Imagine you're a Korean creator who just learned this interesting fact
|
||||
- Add your own personality, reactions, and Korean cultural context
|
||||
=======================================
|
||||
|
||||
Video duration: ~{int(total_duration)} seconds
|
||||
Style: {style}
|
||||
Guide: {style_guide}
|
||||
|
||||
Output format:
|
||||
[0:00] 첫 번째 대사
|
||||
[0:03] 두 번째 대사
|
||||
...
|
||||
|
||||
Requirements:
|
||||
- Write in POLITE FORMAL KOREAN (존댓말/경어) - friendly but respectful
|
||||
- Each line: 2-3 seconds when spoken aloud
|
||||
- Start with a HOOK that grabs attention
|
||||
- Use polite Korean expressions: "이거 아세요?", "정말 신기하죠", "근데 여기서 중요한 건요"
|
||||
- End with engagement: question, call-to-action, or surprise
|
||||
- Make it feel like ORIGINAL Korean content, not a translation"""
|
||||
|
||||
response = client.chat.completions.create(
|
||||
model=settings.OPENAI_MODEL,
|
||||
messages=[
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": f"Chinese transcript:\n{full_text}"}
|
||||
],
|
||||
temperature=0.7,
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
|
||||
script = response.choices[0].message.content
|
||||
usage = response.usage
|
||||
token_info = f"(tokens: {usage.total_tokens})"
|
||||
|
||||
return True, f"Script generated [{style}] {token_info}", script
|
||||
|
||||
except Exception as e:
|
||||
return False, f"Script generation error: {str(e)}", None
|
||||
|
||||
|
||||
async def translate_single(
|
||||
text: str,
|
||||
target_language: str = "Korean",
|
||||
max_tokens: Optional[int] = None,
|
||||
) -> Tuple[bool, str]:
|
||||
"""Translate a single text."""
|
||||
if not settings.OPENAI_API_KEY:
|
||||
return False, text
|
||||
|
||||
try:
|
||||
client = get_openai_client()
|
||||
|
||||
request_params = {
|
||||
"model": settings.OPENAI_MODEL,
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Translate to {target_language}. Only output the translation, nothing else."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": text
|
||||
}
|
||||
],
|
||||
"temperature": 0.3,
|
||||
}
|
||||
|
||||
if max_tokens:
|
||||
request_params["max_tokens"] = max_tokens
|
||||
|
||||
response = client.chat.completions.create(**request_params)
|
||||
|
||||
translated = response.choices[0].message.content
|
||||
return True, translated.strip()
|
||||
|
||||
except Exception as e:
|
||||
return False, text
|
||||
Reference in New Issue
Block a user