66be55ba8a
- korean_preprocessor: 발음 사전 176 → 206개 (200+ 달성) - video_engine: SoraEngine 완전 제거 (2026-03-24 서비스 종료) - smart_video_router: veo3/seedance2 빈 문자열 반환 → ffmpeg_slides 폴백 - cli/init: gemini_web 서비스 설정 질문 추가 (user_profile 일치) - caption_renderer, tts_engine, video_assembler: --test 스탠드얼론 블록 추가 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
443 lines
13 KiB
Python
443 lines
13 KiB
Python
"""
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bots/prompt_layer/korean_preprocessor.py
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Korean TTS text preprocessing.
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Functions:
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- preprocess_korean(text): apply pronunciation map + number conversion
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- insert_pauses(script): insert SSML/marker pauses by sentence type
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"""
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import re
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import logging
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logger = logging.getLogger(__name__)
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# English/acronym → Korean phonetic pronunciation
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# 200+ entries covering tech, finance, social media, brands, etc.
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PRONUNCIATION_MAP = {
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# AI/Tech terms
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'AI': '에이아이',
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'API': '에이피아이',
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'GPT': '지피티',
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'ChatGPT': '챗지피티',
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'Claude': '클로드',
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'GitHub': '깃허브',
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'OpenAI': '오픈에이아이',
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'YouTube': '유튜브',
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'TikTok': '틱톡',
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'SEO': '에스이오',
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'SaaS': '사스',
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'UI': '유아이',
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'UX': '유엑스',
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'LLM': '엘엘엠',
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'NFT': '엔에프티',
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'DeFi': '디파이',
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'IoT': '아이오티',
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'AR': '에이알',
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'VR': '브이알',
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'ML': '머신러닝',
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'NLP': '엔엘피',
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'DevOps': '데브옵스',
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'SQL': '에스큐엘',
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'HTML': '에이치티엠엘',
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'CSS': '씨에스에스',
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'JSON': '제이슨',
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'URL': '유알엘',
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'HTTP': '에이치티티피',
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'HTTPS': '에이치티티피에스',
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'PC': '피씨',
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'CPU': '씨피유',
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'GPU': '지피유',
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'RAM': '램',
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'SSD': '에스에스디',
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'USB': '유에스비',
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'WiFi': '와이파이',
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'Bluetooth': '블루투스',
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'iOS': '아이오에스',
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'Android': '안드로이드',
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'App': '앱',
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'IT': '아이티',
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'ICT': '아이씨티',
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'SNS': '에스엔에스',
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'KPI': '케이피아이',
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'ROI': '알오아이',
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'B2B': '비투비',
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'B2C': '비투씨',
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'MVP': '엠브이피',
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'OKR': '오케이알',
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'CTO': '씨티오',
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'CEO': '씨이오',
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'CFO': '씨에프오',
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'HR': '에이치알',
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'PR': '피알',
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'IR': '아이알',
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# Social/Platforms
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'Instagram': '인스타그램',
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'Facebook': '페이스북',
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'Twitter': '트위터',
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'LinkedIn': '링크드인',
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'Netflix': '넷플릭스',
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'Spotify': '스포티파이',
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'Uber': '우버',
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'Airbnb': '에어비앤비',
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'Amazon': '아마존',
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'Google': '구글',
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'Apple': '애플',
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'Microsoft': '마이크로소프트',
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'Samsung': '삼성',
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'LG': '엘지',
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'SK': '에스케이',
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'KT': '케이티',
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# Finance
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'ETF': '이티에프',
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'IPO': '아이피오',
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'S&P': '에스앤피',
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'NASDAQ': '나스닥',
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'KOSPI': '코스피',
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'KOSDAQ': '코스닥',
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'GDP': '지디피',
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'IMF': '아이엠에프',
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'ECB': '이씨비',
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'Fed': '연준',
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'P/E': '주가수익비율',
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# Health/Science
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'DNA': '디엔에이',
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'RNA': '알엔에이',
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'BMI': '비엠아이',
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'COVID': '코비드',
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'PCR': '피씨알',
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# Education/Certification
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'MBA': '엠비에이',
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'PhD': '박사',
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'IELTS': '아이엘츠',
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'TOEIC': '토익',
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'TOEFL': '토플',
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# Measurement units
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'km': '킬로미터',
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'kg': '킬로그램',
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'MB': '메가바이트',
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'GB': '기가바이트',
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'TB': '테라바이트',
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'Hz': '헤르츠',
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'MHz': '메가헤르츠',
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'GHz': '기가헤르츠',
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# Media/Entertainment
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'OTT': '오티티',
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'VOD': '브이오디',
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'BGM': '비지엠',
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'OST': '오에스티',
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'DJ': '디제이',
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'MC': '엠씨',
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'PD': '피디',
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'CP': '씨피',
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# Common English words used in Korean context
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'App Store': '앱 스토어',
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'Play Store': '플레이 스토어',
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'ChatBot': '챗봇',
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'Web3': '웹쓰리',
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'Metaverse': '메타버스',
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'Blockchain': '블록체인',
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'Crypto': '크립토',
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'Bitcoin': '비트코인',
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'Ethereum': '이더리움',
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'Cloud': '클라우드',
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'Big Data': '빅데이터',
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'Startup': '스타트업',
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'Fintech': '핀테크',
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'Edtech': '에드테크',
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'Healthtech': '헬스테크',
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'PropTech': '프롭테크',
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'LegalTech': '리걸테크',
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'FOMO': '포모',
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'YOLO': '욜로',
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'MZ': '엠제트',
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# More tech
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'Python': '파이썬',
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'JavaScript': '자바스크립트',
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'TypeScript': '타입스크립트',
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'React': '리액트',
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'Node.js': '노드제이에스',
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'Docker': '도커',
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'Kubernetes': '쿠버네티스',
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'AWS': '에이더블유에스',
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'GCP': '지씨피',
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'Azure': '애저',
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'Slack': '슬랙',
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'Zoom': '줌',
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'Discord': '디스코드',
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'Notion': '노션',
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'Figma': '피그마',
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'Canva': '캔바',
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# Business/Strategy
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'OEM': '오이엠',
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'ODM': '오디엠',
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'SCM': '에스씨엠',
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'ERP': '이알피',
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'CRM': '씨알엠',
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# More social media
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'Reels': '릴스',
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'Stories': '스토리',
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'Live': '라이브',
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'Feed': '피드',
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'DM': '디엠',
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'PM': '피엠',
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'QA': '큐에이',
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# Content
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'Blog': '블로그',
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'Vlog': '브이로그',
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'Podcast': '팟캐스트',
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'Newsletter': '뉴스레터',
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'Shorts': '쇼츠',
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'Reel': '릴',
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# Misc
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'OK': '오케이',
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'NO': '노',
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'YES': '예스',
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'WOW': '와우',
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'LOL': '엘오엘',
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'BTW': '그런데',
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'FYI': '참고로',
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'ASAP': '최대한 빨리',
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'FAQ': '자주 묻는 질문',
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'Q&A': '질의응답',
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'A/S': '에이에스',
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'DIY': '디아이와이',
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'PPT': '피피티',
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'PDF': '피디에프',
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'ZIP': '집',
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# AI/LLM extended
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'Gemini': '제미나이',
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'Grok': '그록',
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'Copilot': '코파일럿',
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'Perplexity': '퍼플렉시티',
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'Midjourney': '미드저니',
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'Stable Diffusion': '스테이블 디퓨전',
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'DALL-E': '달리',
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'Sora': '소라',
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'Kling': '클링',
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'Runway': '런웨이',
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# Dev tools / infra
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'Git': '깃',
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'Linux': '리눅스',
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'Ubuntu': '우분투',
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'Windows': '윈도우',
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'macOS': '맥오에스',
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'Terminal': '터미널',
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'CI/CD': '씨아이씨디',
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'API Gateway': '에이피아이 게이트웨이',
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# Finance extended
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'PER': '주가수익비율',
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'PBR': '주가순자산비율',
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'EPS': '주당순이익',
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'ROE': '자기자본이익률',
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'CAGR': '연평균성장률',
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# E-commerce / marketing
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'CPC': '클릭당비용',
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'CPM': '천회노출당비용',
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'CTA': '씨티에이',
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'CTR': '클릭률',
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'ROAS': '광고수익률',
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'LTV': '고객생애가치',
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}
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# Pause durations in milliseconds by sentence type
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DYNAMIC_PAUSES = {
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'hook_after': 500, # ms — impact emphasis after hook
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'question_after': 400, # thinking time after question
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'normal_after': 300, # standard sentence end
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'section_break': 600, # body → closer transition
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'comma': 150, # comma pause
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'exclamation': 200, # exclamation mark pause
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}
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# Number → Korean word conversion rules
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_NUM_TO_KO = {
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0: '영', 1: '일', 2: '이', 3: '삼', 4: '사', 5: '오',
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6: '육', 7: '칠', 8: '팔', 9: '구', 10: '십',
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100: '백', 1000: '천', 10000: '만',
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}
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# Counter words for common units (for better number reading)
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_COUNTER_MAP = {
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'개': ('개', False), # items
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'명': ('명', False), # people
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'번': ('번', False), # times
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'배': ('배', False), # times/multiples
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'위': ('위', False), # rank
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'가지': ('가지', True), # types (use sino-Korean)
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'초': ('초', False), # seconds
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'분': ('분', False), # minutes
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'시간': ('시간', False), # hours
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'일': ('일', False), # days
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'월': ('월', False), # months
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'년': ('년', False), # years
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'%': ('퍼센트', False), # percent
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}
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def preprocess_korean(text: str) -> str:
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"""
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Apply pronunciation map and number conversion to Korean text.
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1. Replace English/acronym terms with Korean phonetics
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2. Convert Arabic numerals with counter words to Korean
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Returns processed text ready for TTS.
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"""
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# Apply pronunciation map (longer strings first to avoid partial replacement)
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sorted_map = sorted(PRONUNCIATION_MAP.items(), key=lambda x: -len(x[0]))
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for en, ko in sorted_map:
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# Word boundary replacement to avoid partial matches
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text = re.sub(r'(?<![가-힣\w])' + re.escape(en) + r'(?![가-힣\w])', ko, text)
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# Convert numbers
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text = _convert_numbers(text)
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return text
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def _convert_numbers(text: str) -> str:
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"""
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Convert Arabic numerals in Korean context.
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e.g.: "3가지" → "세 가지", "100%" → "백 퍼센트"
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"""
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# Handle percentage
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text = re.sub(r'(\d+)%', lambda m: _num_to_korean(int(m.group(1))) + ' 퍼센트', text)
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# Handle number + counter word
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for counter, (ko_counter, use_sino) in _COUNTER_MAP.items():
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if counter == '%':
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continue
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pattern = r'(\d+)\s*' + re.escape(counter)
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def replace(m, kc=ko_counter):
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n = int(m.group(1))
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return _num_to_korean(n) + ' ' + kc
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text = re.sub(pattern, replace, text)
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return text
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def _num_to_korean(n: int) -> str:
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"""Convert integer to Korean sino-Korean numeral string."""
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if n == 0:
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return '영'
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if n < 0:
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return '마이너스 ' + _num_to_korean(-n)
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result = ''
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if n >= 10000:
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man = n // 10000
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result += _num_to_korean(man) + '만'
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n %= 10000
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if n >= 1000:
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cheon = n // 1000
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result += ('' if cheon == 1 else _num_to_korean(cheon)) + '천'
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n %= 1000
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if n >= 100:
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baek = n // 100
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result += ('' if baek == 1 else _num_to_korean(baek)) + '백'
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n %= 100
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if n >= 10:
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sip = n // 10
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result += ('' if sip == 1 else _num_to_korean(sip)) + '십'
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n %= 10
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if n > 0:
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result += _NUM_TO_KO[n]
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return result
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def insert_pauses(script: dict, engine: str = 'ssml') -> dict:
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"""
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Insert pause markers into script by sentence type.
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engine='ssml': insert SSML <break> tags (for ElevenLabs, Google TTS)
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engine='marker': insert [[PAUSE_Xms]] text markers (for Edge TTS, others)
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Returns modified script dict with pauses inserted.
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"""
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result = dict(script)
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hook = script.get('hook', '')
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body = script.get('body', [])
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closer = script.get('closer', '')
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# Add pause after hook
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if hook:
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pause_ms = DYNAMIC_PAUSES['hook_after']
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result['hook'] = hook + _pause_marker(pause_ms, engine)
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# Add pauses within body sentences
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processed_body = []
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for i, sentence in enumerate(body):
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processed = _add_inline_pauses(sentence, engine)
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# Add section break before closer transition
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if i == len(body) - 1:
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processed += _pause_marker(DYNAMIC_PAUSES['section_break'], engine)
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else:
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processed += _pause_marker(DYNAMIC_PAUSES['normal_after'], engine)
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processed_body.append(processed)
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result['body'] = processed_body
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return result
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def _add_inline_pauses(sentence: str, engine: str) -> str:
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"""Add pauses at commas and after exclamation marks."""
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# Comma pauses
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sentence = re.sub(
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r',\s*',
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',' + _pause_marker(DYNAMIC_PAUSES['comma'], engine),
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sentence
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)
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# Question mark pauses
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sentence = re.sub(
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r'\?\s*',
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'?' + _pause_marker(DYNAMIC_PAUSES['question_after'], engine),
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sentence
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)
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# Exclamation pauses
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sentence = re.sub(
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r'!\s*',
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'!' + _pause_marker(DYNAMIC_PAUSES['exclamation'], engine),
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sentence
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)
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return sentence
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def _pause_marker(ms: int, engine: str) -> str:
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"""Generate engine-appropriate pause marker."""
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if engine == 'ssml':
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return f'<break time="{ms}ms"/>'
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else:
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return f' [[PAUSE_{ms}ms]] '
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# ── Standalone test ──────────────────────────────────────────────
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if __name__ == '__main__':
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import sys
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if '--test' in sys.argv:
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print("=== Korean Preprocessor Test ===")
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test_texts = [
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"AI와 ChatGPT가 SEO를 바꾸고 있어요",
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"3가지 방법으로 100%의 수익을 낼 수 있습니다",
|
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"YouTube와 TikTok에서 SNS 마케팅하기",
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"GPT API를 사용한 SaaS 창업",
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]
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for text in test_texts:
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result = preprocess_korean(text)
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print(f"원문: {text}")
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print(f"처리: {result}")
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|
print()
|
|
|
|
# Test pause insertion
|
|
test_script = {
|
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'hook': '이거 모르면 손해입니다!',
|
|
'body': ['첫 번째, AI를 활용하면 10배 빠릅니다.', '두 번째, 자동화가 핵심입니다.'],
|
|
'closer': '지금 바로 시작하세요.'
|
|
}
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|
processed = insert_pauses(test_script, engine='marker')
|
|
print("=== Pause Insertion Test ===")
|
|
for k, v in processed.items():
|
|
print(f"{k}: {v}")
|