{"id":39402,"date":"2026-02-20T17:00:39","date_gmt":"2026-02-20T14:00:39","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/"},"modified":"2026-02-20T17:00:39","modified_gmt":"2026-02-20T14:00:39","slug":"video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/","title":{"rendered":"Video Kay\u0131tlar\u0131na Yapay Zeka Destekli \u00d6zetler ve B\u00f6l\u00fcmler Nas\u0131l Ekledik?"},"content":{"rendered":"
Video Kay\u0131tlar\u0131na Yapay Zeka Destekli \u00d6zetler ve B\u00f6l\u00fcmler Nas\u0131l Ekledik?<\/h2>\n\n
G\u00fcn\u00fcm\u00fcz dijital d\u00fcnyas\u0131nda video i\u00e7erik t\u00fcketimi h\u0131zla artarken, uzun s\u00fcreli video kay\u0131tlar\u0131n\u0131n y\u00f6netimi ve i\u00e7erisindeki kritik bilgilere eri\u015fim giderek zorla\u015fmaktad\u0131r. Bu makalede, bu zorlu\u011fun \u00fcstesinden gelmek i\u00e7in yapay zeka teknolojilerini kullanarak video kay\u0131tlar\u0131na otomatik \u00f6zetler ve b\u00f6l\u00fcmler ekleme s\u00fcrecimizi detayl\u0131 bir \u015fekilde anlataca\u011f\u0131z. Amac\u0131m\u0131z, kullan\u0131c\u0131lar\u0131n uzun videolar\u0131 izlemeden anahtar bilgilere h\u0131zla ula\u015fmas\u0131n\u0131 sa\u011flamak ve i\u00e7erik ke\u015ffini kolayla\u015ft\u0131rmakt\u0131r.<\/p>\n\n
Projenin Temelleri ve Hedefleri<\/h3>\n
Herhangi bir teknik projenin ba\u015flang\u0131c\u0131nda, temel hedefleri ve kar\u015f\u0131lanmas\u0131 gereken ihtiya\u00e7lar\u0131 net bir \u015fekilde tan\u0131mlamak kritik \u00f6neme sahiptir. Bu projenin arkas\u0131ndaki motivasyon, uzun video i\u00e7eriklerinin (toplant\u0131lar, dersler, webinarlar vb.) verimlili\u011fini art\u0131rmakt\u0131.<\/p>\n\n
Uzun Videolar\u0131n Getirdi\u011fi Zorluklar<\/h4>\n
Ortalama bir kullan\u0131c\u0131n\u0131n dikkat s\u00fcresi k\u0131s\u0131tl\u0131d\u0131r ve saatler s\u00fcren bir videoyu ba\u015ftan sona izlemek \u00e7o\u011fu zaman m\u00fcmk\u00fcn veya verimli de\u011fildir. Kullan\u0131c\u0131lar belirli bir konuyu ararken videoyu ileri sarmak zorunda kal\u0131r, bu da zaman kayb\u0131na ve \u00f6nemli noktalar\u0131n ka\u00e7\u0131r\u0131lmas\u0131na neden olabilir. Bu durum, e\u011fitim, kurumsal ileti\u015fim ve i\u00e7erik pazarlamas\u0131 gibi alanlarda ciddi verimlilik sorunlar\u0131 yarat\u0131r.<\/p>\n\n
Projenin Ana Hedefleri<\/h4>\n
Bu zorluklar\u0131 a\u015fmak i\u00e7in \u00fc\u00e7 temel hedef belirledik:<\/p>\n
    \n    
  • Otomatik \u00d6zetleme:<\/strong> Videonun ana temas\u0131n\u0131 ve kilit noktalar\u0131n\u0131 yakalayan k\u0131sa, bilgilendirici metin \u00f6zetleri olu\u015fturmak.<\/li>\n    
  • Otomatik B\u00f6l\u00fcmleme:<\/strong> Videoyu mant\u0131ksal b\u00f6l\u00fcmlere ay\u0131rmak ve her b\u00f6l\u00fcm i\u00e7in a\u00e7\u0131klay\u0131c\u0131 ba\u015fl\u0131klar ile zaman damgalar\u0131 sa\u011flamak.<\/li>\n    
  • Kullan\u0131c\u0131 Deneyimini \u0130yile\u015ftirme:<\/strong> Kullan\u0131c\u0131lar\u0131n arad\u0131klar\u0131 bilgiye daha h\u0131zl\u0131 ula\u015fmas\u0131n\u0131 sa\u011flayarak genel video t\u00fcketim deneyimini optimize etmek.<\/li>\n<\/ul>\n\n
    Video Kay\u0131tlar\u0131n\u0131n \u0130\u015flenmesi ve Metne D\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi<\/h3>\n
    Yapay zeka destekli \u00f6zetleme ve b\u00f6l\u00fcmleme yapabilmek i\u00e7in \u00f6ncelikle videonun i\u00e7eri\u011fini metin format\u0131na d\u00f6n\u00fc\u015ft\u00fcrmemiz gerekmektedir. Bu a\u015fama, t\u00fcm s\u00fcrecin temelini olu\u015fturur.<\/p>\n\n
    Ses \u00c7\u0131karma ve \u00d6n \u0130\u015fleme<\/h4>\n
    Video dosyalar\u0131 genellikle hem g\u00f6rsel hem de i\u015fitsel verileri i\u00e7erir. Bizim i\u00e7in \u00f6nemli olan, konu\u015fmalar\u0131 analiz etmek oldu\u011fundan, videodan sesi ay\u0131klamak ilk ad\u0131md\u0131r. Bu i\u015flem i\u00e7in FFmpeg gibi a\u00e7\u0131k kaynakl\u0131 ara\u00e7lar veya Python'daki moviepy<\/code> k\u00fct\u00fcphanesi kullan\u0131labilir. \u00c7\u0131kar\u0131lan ses dosyalar\u0131 (genellikle MP3 veya WAV format\u0131nda) daha sonra konu\u015fmadan metne d\u00f6n\u00fc\u015ft\u00fcrme (Speech-to-Text - STT) servislerine g\u00f6nderilmek \u00fczere haz\u0131rlan\u0131r.<\/p>\n
    \nfrom moviepy.editor import VideoFileClip\n\ndef extract_audio(video_path, audio_output_path):\n    video = VideoFileClip(video_path)\n    video.audio.write_audiofile(audio_output_path)\n    video.close()\n    print(f\"Ses '{audio_output_path}' konumuna \u00e7\u0131kar\u0131ld\u0131.\")\n\n# \u00d6rnek kullan\u0131m:\n# extract_audio(\"input_video.mp4\", \"output_audio.mp3\")\n<\/code><\/pre>\n
    Konu\u015fmadan Metne (Speech-to-Text - STT) Teknolojileri<\/h4>\n
    Ses dosyas\u0131n\u0131 metne d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in \u00e7e\u015fitli STT servisleri mevcuttur. Bu servisler, derin \u00f6\u011frenme modelleri kullanarak insan konu\u015fmas\u0131n\u0131 y\u00fcksek do\u011frulukla yaz\u0131l\u0131 metne \u00e7evirir. Se\u00e7im yaparken do\u011fruluk oran\u0131, desteklenen diller, maliyet ve \u00f6l\u00e7eklenebilirlik gibi fakt\u00f6rleri g\u00f6z \u00f6n\u00fcnde bulundurduk. Ba\u015fl\u0131ca se\u00e7enekler \u015funlard\u0131r:<\/p>\n
      \n
    • Google Cloud Speech-to-Text:<\/strong> Y\u00fcksek do\u011fruluk ve geni\u015f dil deste\u011fi sunar.<\/li>\n
    • AWS Transcribe:<\/strong> \u00d6l\u00e7eklenebilirlik ve di\u011fer AWS servisleriyle entegrasyon avantaj\u0131.<\/li>\n
    • Azure Speech Service:<\/strong> Microsoft ekosistemiyle uyumlu, \u00f6zelle\u015ftirme se\u00e7enekleri sunar.<\/li>\n
    • OpenAI Whisper:<\/strong> A\u00e7\u0131k kaynakl\u0131 ve \u00e7o\u011fu durumda olduk\u00e7a y\u00fcksek do\u011fruluk sunan g\u00fc\u00e7l\u00fc bir model. Yerel olarak veya API \u00fczerinden kullan\u0131labilir.<\/li>\n<\/ul>\n
      Projemizde ba\u015flang\u0131\u00e7ta Google Cloud Speech-to-Text API'sini kulland\u0131k ve daha sonra OpenAI Whisper'\u0131n API versiyonunu da test ettik. Whisper, \u00f6zellikle farkl\u0131 aksan ve ses kalitelerinde bile etkileyici sonu\u00e7lar verdi.<\/p>\n
      \n# Google Cloud Speech-to-Text \u00f6rne\u011fi (basitle\u015ftirilmi\u015f)\nfrom google.cloud import speech\n\ndef transcribe_audio(audio_path, language_code=\"tr-TR\"):\n    client = speech.SpeechClient()\n    with open(audio_path, \"rb\") as audio_file:\n        content = audio_file.read()\n\n    audio = speech.RecognitionAudio(content=content)\n    config = speech.RecognitionConfig(\n        encoding=speech.RecognitionConfig.AudioEncoding.MP3,\n        sample_rate_hertz=44100, # Ses dosyas\u0131n\u0131n \u00f6rnekleme h\u0131z\u0131na g\u00f6re ayarlanmal\u0131\n        language_code=language_code,\n        enable_automatic_punctuation=True,\n        enable_word_time_offsets=True # Kelime zaman damgalar\u0131 i\u00e7in\n    )\n\n    response = client.recognize(config=config, audio=audio)\n    full_transcript = \"\"\n    word_timestamps = []\n    for result in response.results:\n        full_transcript += result.alternatives[0].transcript + \" \"\n        for word_info in result.alternatives[0].words:\n            word_timestamps.append({\n                \"word\": word_info.word,\n                \"start_time\": word_info.start_time.total_seconds(),\n                \"end_time\": word_info.end_time.total_seconds()\n            })\n    return full_transcript, word_timestamps\n\n# \u00d6rnek kullan\u0131m:\n# transcript, timestamps = transcribe_audio(\"output_audio.mp3\")\n# print(transcript)\n<\/code><\/pre>\n
      Yapay Zeka Destekli \u00d6zetleme Mekanizmalar\u0131<\/h3>\n
      Metin transkripti elde edildikten sonra, bu uzun metni anlaml\u0131 ve k\u0131sa \u00f6zetlere d\u00f6n\u00fc\u015ft\u00fcrme a\u015famas\u0131na ge\u00e7ebiliriz. Bu s\u00fcre\u00e7, Do\u011fal Dil \u0130\u015fleme (NLP) tekniklerini ve modellerini kullanmay\u0131 gerektirir.<\/p>\n
      \u00d6zetleme T\u00fcrleri: \u00c7\u0131kar\u0131msal ve Soyutlay\u0131c\u0131<\/h4>\n
      Metin \u00f6zetleme iki ana kategoriye ayr\u0131l\u0131r:<\/p>\n
        \n
      • \u00c7\u0131kar\u0131msal (Extractive) \u00d6zetleme:<\/strong> Orijinal metinden en \u00f6nemli c\u00fcmleleri veya c\u00fcmle par\u00e7alar\u0131n\u0131 se\u00e7erek bir \u00f6zet olu\u015fturur. Bu y\u00f6ntem, genellikle daha az karma\u015f\u0131kt\u0131r ve orijinal metnin c\u00fcmlelerini do\u011frudan kulland\u0131\u011f\u0131 i\u00e7in gramer ve anlamsal tutarl\u0131l\u0131k a\u00e7\u0131s\u0131ndan daha g\u00fcvenlidir.<\/li>\n
      • Soyutlay\u0131c\u0131 (Abstractive) \u00d6zetleme:<\/strong> Orijinal metni anlar ve bu anlay\u0131\u015fa dayanarak tamamen yeni c\u00fcmleler olu\u015fturur. \u0130nsanlar\u0131n \u00f6zetleme \u015fekline daha yak\u0131nd\u0131r ancak NLP modellerinin metni derinlemesine anlamas\u0131n\u0131 ve yeni, ba\u011flaml\u0131 c\u00fcmleler \u00fcretmesini gerektirdi\u011fi i\u00e7in teknik olarak daha zordur. Genellikle daha ak\u0131c\u0131 ve \u00f6zg\u00fcn \u00f6zetler \u00fcretir.<\/li>\n<\/ul>\n
        Projemizin ilk a\u015famalar\u0131nda \u00e7\u0131kar\u0131msal \u00f6zetleme y\u00f6ntemlerini tercih ettik. Daha sonra, daha geli\u015fmi\u015f ve ak\u0131c\u0131 \u00f6zetler elde etmek i\u00e7in soyutlay\u0131c\u0131 y\u00f6ntemlere ge\u00e7i\u015f yapt\u0131k.<\/p>\n
        NLP Modelleri ve K\u00fct\u00fcphaneleri<\/h4>\n
        \u00d6zetleme i\u00e7in Hugging Face Transformers k\u00fct\u00fcphanesi ve bu k\u00fct\u00fcphanede bulunan \u00f6nceden e\u011fitilmi\u015f modeller (\u00f6rne\u011fin, BART, T5, Pegasus) olduk\u00e7a faydal\u0131d\u0131r. \u00d6zellikle T\u00fcrk\u00e7e i\u00e7in \u00f6zelle\u015ftirilmi\u015f veya \u00e7ok dilli modeller (mBART) tercih edilebilir. Bu modeller, genellikle \"encoder-decoder\" mimarisine sahip olup, metni anlama ve yeni metin \u00fcretme konusunda ba\u015far\u0131l\u0131d\u0131r.<\/p>\n
        \nfrom transformers import pipeline\n\n# Soyutlay\u0131c\u0131 \u00f6zetleme i\u00e7in bir model y\u00fckleme (T\u00fcrk\u00e7e destekli)\n# T\u00fcrk\u00e7e i\u00e7in \u00f6zelle\u015ftirilmi\u015f bir model veya \u00e7ok dilli bir model se\u00e7ilebilir\n# \u00d6rnek: 't5-base' veya 'google\/mt5-small' gibi modeller T\u00fcrk\u00e7e i\u00e7in fine-tuned edilebilir.\n# Bu \u00f6rnekte genel bir model kullan\u0131lm\u0131\u015ft\u0131r. Ger\u00e7ek uygulamada T\u00fcrk\u00e7e i\u00e7in optimize edilmi\u015f bir model tercih edilmelidir.\nsummarizer = pipeline(\"summarization\", model=\"facebook\/mbart-large-50-one-to-many-mmt\")\n\ndef generate_summary(text, max_length=150, min_length=50):\n    # Modelin bekledi\u011fi input format\u0131na g\u00f6re ayar yap\u0131lmal\u0131\n    # mbart-large-50-one-to-many-mmt modeli i\u00e7in hedef dil belirtmek gerekebilir\n    summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']\n    return summary\n\n# \u00d6rnek kullan\u0131m:\n# long_transcript = \"...\" # Uzun transkript metni\n# video_summary = generate_summary(long_transcript)\n# print(video_summary)\n<\/code><\/pre>\n
        Anahtar C\u00fcmle\/Kelime \u00c7\u0131kar\u0131m\u0131 ve \u00d6zetleme Algoritmalar\u0131<\/h4>\n
        \u00c7\u0131kar\u0131msal \u00f6zetleme i\u00e7in TextRank veya Luhn algoritmas\u0131 gibi y\u00f6ntemler kullan\u0131labilir. Bu algoritmalar, metindeki c\u00fcmlelerin \u00f6nemini belirlemek i\u00e7in kelime s\u0131kl\u0131\u011f\u0131, c\u00fcmle pozisyonu ve di\u011fer dilbilimsel \u00f6zelliklerden faydalan\u0131r. Soyutlay\u0131c\u0131 \u00f6zetleme ise genellikle daha karma\u015f\u0131k sinir a\u011f\u0131 modelleri (Transformer tabanl\u0131) taraf\u0131ndan ger\u00e7ekle\u015ftirilir.<\/p>\n
        \u00d6zetleme s\u00fcrecinde, transkriptin tamam\u0131n\u0131 tek seferde \u00f6zetlemek yerine, videoyu mant\u0131ksal par\u00e7alara ay\u0131r\u0131p her par\u00e7a i\u00e7in ayr\u0131 \u00f6zetler olu\u015fturmak, daha detayl\u0131 ve ba\u011flam odakl\u0131 \u00f6zetler elde etmemizi sa\u011flar. Bu, ayn\u0131 zamanda b\u00f6l\u00fcmleme s\u00fcrecine de katk\u0131da bulunur.<\/p>\n
        Otomatik B\u00f6l\u00fcmleme ve Zaman Damgalar\u0131<\/h3>\n
        Video \u00f6zetlerinin yan\u0131 s\u0131ra, videoyu mant\u0131ksal b\u00f6l\u00fcmlere ay\u0131rmak ve bu b\u00f6l\u00fcmlere zaman damgalar\u0131 eklemek, kullan\u0131c\u0131lar\u0131n belirli konulara do\u011frudan atlamas\u0131n\u0131 sa\u011flar. Bu, i\u00e7erik ke\u015ffi ve navigasyon a\u00e7\u0131s\u0131ndan b\u00fcy\u00fck bir kolayl\u0131k sunar.<\/p>\n
        Konu Tespiti ve Segmentasyon<\/h4>\n
        B\u00f6l\u00fcmleme, transkript metni \u00fczerinde konu de\u011fi\u015fikli\u011fi tespiti yaparak ger\u00e7ekle\u015ftirilir. Bu, genellikle a\u015fa\u011f\u0131daki y\u00f6ntemlerle yap\u0131l\u0131r:<\/p>\n
          \n
        • Metinsel Benzerlik Analizi:<\/strong> Metni k\u00fc\u00e7\u00fck par\u00e7alara (\u00f6rne\u011fin, 30-60 saniyelik konu\u015fma bloklar\u0131) ay\u0131r\u0131p, kom\u015fu bloklar aras\u0131ndaki anlamsal benzerli\u011fi \u00f6l\u00e7mek. Benzerlikteki ani d\u00fc\u015f\u00fc\u015fler, yeni bir konunun ba\u015flad\u0131\u011f\u0131na i\u015faret edebilir.<\/li>\n
        • Anahtar Kelime Yo\u011funlu\u011fu:<\/strong> Belirli anahtar kelimelerin veya terimlerin kullan\u0131m s\u0131kl\u0131\u011f\u0131ndaki de\u011fi\u015fiklikleri izlemek.<\/li>\n
        • Konu Modelleme (Topic Modeling):<\/strong> LDA (Latent Dirichlet Allocation) veya NMF (Non-negative Matrix Factorization) gibi algoritmalarla metindeki ana konular\u0131 belirlemek ve bu konular\u0131n zaman i\u00e7indeki de\u011fi\u015fimini izlemek.<\/li>\n<\/ul>\n
          Biz, metinsel benzerlik analizi ve anahtar kelime yo\u011funlu\u011funu birle\u015ftirerek daha sa\u011flam bir konu tespiti mekanizmas\u0131 geli\u015ftirdik. Her segment i\u00e7in bir \u00f6zet ve potansiyel bir ba\u015fl\u0131k olu\u015fturulur.<\/p>\n
          Zaman Damgalar\u0131n\u0131n Entegrasyonu<\/h4>\n
          STT servislerinden ald\u0131\u011f\u0131m\u0131z kelime bazl\u0131 zaman damgalar\u0131, b\u00f6l\u00fcmleme i\u00e7in kritik \u00f6neme sahiptir. Bir b\u00f6l\u00fcm\u00fcn ba\u015flang\u0131c\u0131 tespit edildi\u011finde, o b\u00f6l\u00fcmdeki ilk kelimenin zaman damgas\u0131, b\u00f6l\u00fcm\u00fcn ba\u015flang\u0131\u00e7 zaman\u0131 olarak kaydedilir. Bu sayede, kullan\u0131c\u0131lar bir b\u00f6l\u00fcm ba\u015fl\u0131\u011f\u0131na t\u0131klad\u0131\u011f\u0131nda videonun tam olarak ilgili k\u0131sm\u0131na y\u00f6nlendirilebilir.<\/p>\n
          Her b\u00f6l\u00fcm i\u00e7in bir ba\u015flang\u0131\u00e7 zaman\u0131, biti\u015f zaman\u0131 (bir sonraki b\u00f6l\u00fcm\u00fcn ba\u015flang\u0131c\u0131 veya videonun sonu), bir ba\u015fl\u0131k ve k\u0131sa bir \u00f6zet olu\u015fturulur. Bu bilgiler, videonun meta verisi olarak depolan\u0131r.<\/p>\n
          \n# Basit bir konu de\u011fi\u015fikli\u011fi tespiti (\u00f6rnek ama\u00e7l\u0131)\n# Ger\u00e7ek uygulamada daha sofistike NLP modelleri kullan\u0131l\u0131r\ndef detect_chapters(word_timestamps, transcript, segment_length_seconds=120):\n    chapters = []\n    current_chapter_start_time = 0.0\n    current_chapter_text = \"\"\n    chapter_count = 1\n\n    for i, word_info in enumerate(word_timestamps):\n        current_chapter_text += word_info[\"word\"] + \" \"\n        \n        # Belirli bir segment uzunlu\u011funa ula\u015f\u0131ld\u0131\u011f\u0131nda veya\n        # metinsel benzerlik analizi ile konu de\u011fi\u015fikli\u011fi tespit edildi\u011finde\n        if word_info[\"end_time\"] - current_chapter_start_time >= segment_length_seconds or \\\n           (i > 0 and i % 500 == 0): # Basit bir metin uzunlu\u011fu kontrol\u00fc\n            \n            # Bu noktada current_chapter_text i\u00e7in \u00f6zet ve ba\u015fl\u0131k \u00fcretilir\n            # \u00d6rnek olarak sadece ba\u015fl\u0131k ve ba\u015flang\u0131\u00e7 zaman\u0131 ekliyoruz\n            chapter_title = f\"B\u00f6l\u00fcm {chapter_count}: Konu Analizi\" # Ger\u00e7ekte AI ile olu\u015fturulur\n            chapter_summary = generate_summary(current_chapter_text) # AI ile \u00f6zetlenir\n\n            chapters.append({\n                \"title\": chapter_title,\n                \"summary\": chapter_summary,\n                \"start_time\": current_chapter_start_time,\n                \"end_time\": word_info[\"end_time\"]\n            })\n            current_chapter_start_time = word_info[\"end_time\"]\n            current_chapter_text = \"\"\n            chapter_count += 1\n    \n    # Son b\u00f6l\u00fcm\u00fc ekle\n    if current_chapter_text:\n        chapter_title = f\"B\u00f6l\u00fcm {chapter_count}: Sonu\u00e7\"\n        chapter_summary = generate_summary(current_chapter_text)\n        chapters.append({\n            \"title\": chapter_title,\n            \"summary\": chapter_summary,\n            \"start_time\": current_chapter_start_time,\n            \"end_time\": word_timestamps[-1][\"end_time\"] if word_timestamps else current_chapter_start_time\n        })\n    return chapters\n\n# \u00d6rnek kullan\u0131m:\n# video_chapters = detect_chapters(timestamps, transcript)\n# for chapter in video_chapters:\n#     print(f\"Ba\u015fl\u0131k: {chapter['title']}, Ba\u015flang\u0131\u00e7: {chapter['start_time']:.2f}s, \u00d6zet: {chapter['summary']}\")\n<\/code><\/pre>\n
          Sistem Mimarisi ve Entegrasyon<\/h3>\n
          T\u00fcm bu s\u00fcre\u00e7leri sorunsuz bir \u015fekilde bir araya getirmek i\u00e7in mod\u00fcler ve \u00f6l\u00e7eklenebilir bir sistem mimarisi tasarlad\u0131k.<\/p>\n
          Veri Ak\u0131\u015f\u0131 ve Mikroservis Yakla\u015f\u0131m\u0131<\/h4>\n
          Sistem, bir mikroservis mimarisiyle tasarland\u0131. Her bir ana i\u015flev (video y\u00fckleme, ses \u00e7\u0131karma, STT, \u00f6zetleme, b\u00f6l\u00fcmleme) ayr\u0131 bir servis olarak \u00e7al\u0131\u015f\u0131r. Bu, her servisin ba\u011f\u0131ms\u0131z olarak \u00f6l\u00e7eklenmesine ve geli\u015ftirilmesine olanak tan\u0131r.<\/p>\n
            \n
          1. Video Y\u00fckleme:<\/strong> Kullan\u0131c\u0131lar videolar\u0131 web aray\u00fcz\u00fc \u00fczerinden y\u00fckler. Videolar bir bulut depolama servisine (\u00f6rn. AWS S3, Google Cloud Storage) kaydedilir.<\/li>\n
          2. Kuyruk Sistemi:<\/strong> Y\u00fcklenen videolar\u0131n i\u015flenmesi i\u00e7in bir mesaj kuyru\u011fu (\u00f6rn. RabbitMQ, AWS SQS) kullan\u0131l\u0131r. Bu, yo\u011funluk anlar\u0131nda sistemin t\u0131kanmas\u0131n\u0131 \u00f6nler.<\/li>\n
          3. Ses \u00c7\u0131karma Servisi:<\/strong> Kuyruktan video bilgilerini al\u0131r, videodan sesi \u00e7\u0131kar\u0131r ve ses dosyas\u0131n\u0131 depolamaya kaydeder.<\/li>\n
          4. STT Servisi:<\/strong> Ses dosyas\u0131n\u0131 al\u0131r, se\u00e7ilen STT API'sine g\u00f6nderir ve transkript ile kelime zaman damgalar\u0131n\u0131 bir veritaban\u0131na kaydeder.<\/li>\n
          5. NLP \u0130\u015fleme Servisi:<\/strong> Transkripti veritaban\u0131ndan \u00e7eker, \u00f6zetleme ve b\u00f6l\u00fcmleme algoritmalar\u0131n\u0131 \u00e7al\u0131\u015ft\u0131r\u0131r. Olu\u015fturulan \u00f6zetleri, b\u00f6l\u00fcm ba\u015fl\u0131klar\u0131n\u0131 ve zaman damgalar\u0131n\u0131 veritaban\u0131na kaydeder.<\/li>\n
          6. API Gateway ve Kullan\u0131c\u0131 Aray\u00fcz\u00fc:<\/strong> Kullan\u0131c\u0131 aray\u00fcz\u00fc (React\/Vue.js) bir API Gateway (\u00f6rn. FastAPI, Flask) arac\u0131l\u0131\u011f\u0131yla arka u\u00e7 servisleriyle ileti\u015fim kurar. Kullan\u0131c\u0131lar \u00f6zetleri ve b\u00f6l\u00fcmleri bu aray\u00fcz \u00fczerinden g\u00f6r\u00fcnt\u00fcler.<\/li>\n<\/ol>\n
            Kullan\u0131lan Teknolojiler<\/h4>\n
              \n
            • Programlama Dili:<\/strong> Python (veri i\u015fleme, NLP, API geli\u015ftirme i\u00e7in).<\/li>\n
            • Web \u00c7er\u00e7evesi:<\/strong> FastAPI (y\u00fcksek performansl\u0131 API'ler i\u00e7in) veya Flask (daha basit API'ler i\u00e7in).<\/li>\n
            • Bulut Platformu:<\/strong> AWS (S3, SQS, EC2\/Lambda) veya Google Cloud (Cloud Storage, Pub\/Sub, Compute Engine\/Cloud Functions).<\/li>\n
            • Veritaban\u0131:<\/strong> PostgreSQL (yap\u0131land\u0131r\u0131lm\u0131\u015f veriler i\u00e7in), MongoDB (esnek \u015fema gerektiren durumlar i\u00e7in).<\/li>\n
            • NLP K\u00fct\u00fcphaneleri:<\/strong> Hugging Face Transformers, spaCy, NLTK.<\/li>\n<\/ul>\n
              Kar\u015f\u0131la\u015f\u0131lan Zorluklar ve \u00c7\u00f6z\u00fcmler<\/h3>\n
              Bu t\u00fcr karma\u015f\u0131k bir yapay zeka projesinde bir\u00e7ok zorlukla kar\u015f\u0131la\u015ft\u0131k. Her bir zorlu\u011fa y\u00f6nelik geli\u015ftirdi\u011fimiz \u00e7\u00f6z\u00fcmler, projenin ba\u015far\u0131s\u0131 i\u00e7in kritikti.<\/p>\n
              STT Do\u011frulu\u011fu ve Ses Kalitesi<\/h4>\n
              Zorluk:<\/strong> Arka plan g\u00fcr\u00fclt\u00fcs\u00fc, farkl\u0131 aksanlar, birden fazla konu\u015fmac\u0131 ve d\u00fc\u015f\u00fck ses kalitesi, STT servislerinin do\u011frulu\u011funu \u00f6nemli \u00f6l\u00e7\u00fcde etkileyebilir.<\/p>\n
              \u00c7\u00f6z\u00fcm:<\/strong><\/p>\n
                \n
              • Birden fazla STT servisini test ettik ve projemiz i\u00e7in en uygun olan\u0131 (\u00f6rn. OpenAI Whisper) se\u00e7tik.<\/li>\n
              • Ses \u00f6n i\u015fleme ad\u0131mlar\u0131 ekledik: g\u00fcr\u00fclt\u00fc azaltma, ses normalizasyonu.<\/li>\n
              • Konu\u015fmac\u0131 diarizasyonu (konu\u015fmac\u0131 ayr\u0131m\u0131) uygulayarak, her konu\u015fmac\u0131n\u0131n s\u00f6yledi\u011fi metni ayr\u0131 ayr\u0131 etiketledik, bu da \u00f6zetleme ve b\u00f6l\u00fcmleme i\u00e7in ba\u011flam\u0131 zenginle\u015ftirdi.<\/li>\n<\/ul>\n
                \u00d6zetleme Kalitesi ve Ba\u011flam Tutarl\u0131l\u0131\u011f\u0131<\/h4>\n
                Zorluk:<\/strong> \u00d6zellikle uzun transkriptlerde, \u00f6zetlerin ana ba\u011flam\u0131 kaybetmesi veya anlams\u0131z c\u00fcmleler i\u00e7ermesi riski vard\u0131. Soyutlay\u0131c\u0131 modeller bazen \"hal\u00fcsinasyon\" (ger\u00e7ekte olmayan bilgiyi \u00fcretme) e\u011filimi g\u00f6sterebilir.<\/p>\n
                \u00c7\u00f6z\u00fcm:<\/strong><\/p>\n
                  \n
                • \u00d6zetleme modellerini T\u00fcrk\u00e7e veri setleri \u00fczerinde fine-tuning (ince ayar) yaparak performanslar\u0131n\u0131 art\u0131rd\u0131k.<\/li>\n
                • \u00c7\u0131kar\u0131msal ve soyutlay\u0131c\u0131 y\u00f6ntemleri birle\u015ftiren hibrit yakla\u015f\u0131mlar denedik.<\/li>\n
                • Kullan\u0131c\u0131 geri bildirimlerini toplayarak \u00f6zetleme algoritmalar\u0131n\u0131 s\u00fcrekli iyile\u015ftirdik. \u00d6zetlerin uzunlu\u011funu ve yo\u011funlu\u011funu ayarlanabilir hale getirdik.<\/li>\n<\/ul>\n
                  Performans ve \u00d6l\u00e7eklenebilirlik<\/h4>\n
                  Zorluk:<\/strong> Uzun videolar\u0131n i\u015flenmesi zaman al\u0131c\u0131 ve kaynak yo\u011fun bir s\u00fcre\u00e7tir. Artan kullan\u0131c\u0131 talebiyle birlikte sistemin \u00f6l\u00e7eklenebilirli\u011fini sa\u011flamak \u00f6nemliydi.<\/p>\n
                  \u00c7\u00f6z\u00fcm:<\/strong><\/p>\n
                    \n
                  • Mikroservis mimarisi ve mesaj kuyruklar\u0131 kullanarak i\u015f y\u00fck\u00fcn\u00fc da\u011f\u0131tt\u0131k.<\/li>\n
                  • Bulut servislerinin otomatik \u00f6l\u00e7eklendirme \u00f6zelliklerinden faydaland\u0131k (\u00f6rn. AWS Auto Scaling Groups, Kubernetes).<\/li>\n
                  • GPU h\u0131zland\u0131rmal\u0131 sanal makineler kullanarak NLP modellerinin \u00e7\u0131kar\u0131m s\u00fcrelerini optimize ettik.<\/li>\n
                  • \u00d6nbellekleme mekanizmalar\u0131 uygulayarak s\u0131k eri\u015filen \u00f6zet ve b\u00f6l\u00fcm verilerini daha h\u0131zl\u0131 sunduk.<\/li>\n<\/ul>\n
                    Sonu\u00e7<\/h3>\n
                    Video kay\u0131tlar\u0131na yapay zeka destekli \u00f6zetler ve b\u00f6l\u00fcmler ekleme projesi, hem teknik olarak zorlay\u0131c\u0131 hem de kullan\u0131c\u0131lar i\u00e7in olduk\u00e7a de\u011ferli bir \u00e7\u00f6z\u00fcm sundu. Bu entegrasyon sayesinde, uzun video i\u00e7eriklerinin t\u00fcketimi daha verimli, bilgilendirici ve keyifli hale geldi. Kullan\u0131c\u0131lar art\u0131k saatler s\u00fcren videolar\u0131 izlemek yerine, birka\u00e7 dakika i\u00e7inde anahtar bilgilere ula\u015fabiliyor veya ilgilendikleri b\u00f6l\u00fcme do\u011frudan atlayabiliyorlar.<\/p>\n
                    Gelecekte, duygusal analiz, konu\u015fmac\u0131 tan\u0131ma ve \u00e7ok dilli \u00f6zetleme gibi daha geli\u015fmi\u015f \u00f6zellikler ekleyerek sistemi daha da zenginle\u015ftirmeyi hedefliyoruz. Yapay zeka teknolojilerinin video i\u00e7erik y\u00f6netimi alan\u0131ndaki potansiyeli s\u0131n\u0131rs\u0131zd\u0131r ve bu proje, bu potansiyelin sadece k\u00fc\u00e7\u00fck bir ba\u015flang\u0131c\u0131d\u0131r.<\/p>\n
                    SSS (S\u0131k Sorulan Sorular)<\/h3>\n
                    H3.1. Yapay zeka \u00f6zetleri ne kadar do\u011fru oluyor?<\/h4>\n
                    \u00d6zetlerin do\u011frulu\u011fu, kullan\u0131lan STT servisinin kalitesine, NLP modelinin karma\u015f\u0131kl\u0131\u011f\u0131na ve videonun i\u00e7eri\u011fine ba\u011fl\u0131d\u0131r. Geli\u015fmi\u015f modeller ve fine-tuning ile %85-95 aras\u0131nda bir do\u011fruluk oran\u0131na ula\u015fmak m\u00fcmk\u00fcnd\u00fcr. Ancak, \u00f6zellikle \u00e7ok teknik veya ni\u015f konularda her zaman %100 m\u00fckemmellik garanti edilemez.<\/p>\n
                    H3.2. B\u00f6l\u00fcmler otomatik olarak nas\u0131l belirleniyor?<\/h4>\n
                    B\u00f6l\u00fcmler, videonun transkript metni \u00fczerinde yap\u0131lan anlamsal analizlerle belirlenir. Metindeki konu de\u011fi\u015fiklikleri, anahtar kelime yo\u011funluklar\u0131 ve c\u00fcmleler aras\u0131 benzerlikler gibi fakt\u00f6rler de\u011ferlendirilerek mant\u0131ksal segmentler olu\u015fturulur. Her segmentin ba\u015flang\u0131c\u0131, STT servisinden al\u0131nan kelime zaman damgalar\u0131 ile e\u015fle\u015ftirilir.<\/p>\n
                    H3.3. Hangi dilleri destekliyorsunuz?<\/h4>\n
                    Kulland\u0131\u011f\u0131m\u0131z STT ve NLP modelleri (\u00f6zellikle OpenAI Whisper ve mBART gibi \u00e7ok dilli modeller), T\u00fcrk\u00e7e dahil olmak \u00fczere bir\u00e7ok farkl\u0131 dili desteklemektedir. Projemizin ana oda\u011f\u0131 T\u00fcrk\u00e7e olsa da, kolayca farkl\u0131 dillere uyarlanabilir bir yap\u0131ya sahibiz.<\/p>\n
                    H3.4. Bu sistemin kurulum maliyeti y\u00fcksek mi?<\/h4>\n
                    Maliyet, kullan\u0131lan bulut servislerine (STT API \u00fccretleri, i\u015flem g\u00fcc\u00fc, depolama), se\u00e7ilen NLP modellerine ve i\u015flenecek video hacmine g\u00f6re de\u011fi\u015fir. K\u00fc\u00e7\u00fck \u00f6l\u00e7ekli projeler i\u00e7in uygun maliyetli \u00e7\u00f6z\u00fcmler bulunurken, b\u00fcy\u00fck \u00f6l\u00e7ekli ve y\u00fcksek hacimli kullan\u0131mlar i\u00e7in maliyetler artabilir. Ancak, sa\u011flad\u0131\u011f\u0131 verimlilik ve zaman tasarrufu g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, yat\u0131r\u0131m\u0131n geri d\u00f6n\u00fc\u015f\u00fc genellikle olduk\u00e7a y\u00fcksektir.<\/p>\n
                    H3.5. Kullan\u0131c\u0131lar \u00f6zetleri veya b\u00f6l\u00fcmleri d\u00fczenleyebilir mi?<\/h4>\n
                    Evet, kullan\u0131c\u0131 aray\u00fcz\u00fcm\u00fcz \u00fczerinden olu\u015fturulan \u00f6zet ve b\u00f6l\u00fcm ba\u015fl\u0131klar\u0131n\u0131 manuel olarak d\u00fczenleme veya yeni b\u00f6l\u00fcmler ekleme imkan\u0131 sunuyoruz. Yapay zeka taraf\u0131ndan \u00fcretilen i\u00e7erikler bir ba\u015flang\u0131\u00e7 noktas\u0131 olarak kabul edilir ve kullan\u0131c\u0131lar\u0131n son dokunu\u015flar\u0131n\u0131 yapmalar\u0131na olanak tan\u0131r.<\/p>\n
                    <\/code><\/p>\n","protected":false},"excerpt":{"rendered":"G\u00fcn\u00fcm\u00fcz dijital d\u00fcnyas\u0131nda video i\u00e7erik t\u00fcketimi h\u0131zla artarken, uzun s\u00fcreli video kay\u0131tlar\u0131n\u0131n y\u00f6netimi ve i\u00e7erisindeki kritik bilgilere eri\u015f…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-39402","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nVideo Kay\u0131tlar\u0131na Yapay Zeka Destekli \u00d6zetler ve B\u00f6l\u00fcmler Nas\u0131l Ekledik? - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Video Kay\u0131tlar\u0131na Yapay Zeka Destekli \u00d6zetler ve B\u00f6l\u00fcmler Nas\u0131l Ekledik?\" \/>\n<meta property=\"og:description\" content=\"G\u00fcn\u00fcm\u00fcz dijital d\u00fcnyas\u0131nda video i\u00e7erik t\u00fcketimi h\u0131zla artarken, uzun s\u00fcreli video kay\u0131tlar\u0131n\u0131n y\u00f6netimi ve i\u00e7erisindeki kritik bilgilere eri\u015f...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-20T14:00:39+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"15 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Video Kay\u0131tlar\u0131na Yapay Zeka Destekli \u00d6zetler ve B\u00f6l\u00fcmler Nas\u0131l Ekledik?\",\"datePublished\":\"2026-02-20T14:00:39+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/\"},\"wordCount\":2016,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/video-kayitlarina-yapay-zeka-destekli-ozetler-ve-bolumler-nasil-ekledik\/\",\"name\":\"Video Kay\u0131tlar\u0131na Yapay Zeka Destekli \u00d6zetler ve B\u00f6l\u00fcmler Nas\u0131l Ekledik? 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