[{"data":1,"prerenderedAt":1077},["ShallowReactive",2],{"blog-en-video-localization-workflow-guide":3},{"id":4,"title":5,"body":6,"category":1065,"cover":1066,"date":1067,"description":1068,"extension":1069,"lang":1070,"meta":1071,"navigation":1072,"path":1073,"seo":1074,"stem":1075,"__hash__":1076},"content\u002Fblog\u002Fen\u002Fvideo-localization-workflow-guide.md","Video Localization in 2026: The Tool Stack That Actually Works",{"type":7,"value":8,"toc":1038},"minimark",[9,13,18,22,25,29,32,138,144,148,151,156,159,276,282,286,289,422,437,443,447,450,599,605,611,615,622,717,723,727,730,823,827,830,834,845,855,858,862,868,876,879,883,889,897,900,904,907,935,939,943,946,950,953,957,960,964,967,971,974,978],[10,11,5],"h1",{"id":12},"video-localization-in-2026-the-tool-stack-that-actually-works",[14,15,17],"h2",{"id":16},"what-localization-actually-means-and-why-most-people-get-it-wrong","What \"localization\" actually means (and why most people get it wrong)",[19,20,21],"p",{},"Localization is not translation with a fancier name. Translation handles the words. Localization handles everything the words are embedded in — the timing, the visuals, the cultural cues, the platform-specific formatting. When a creator dubs their English explainer into Spanish, but leaves the on-screen text in English and the examples referencing Black Friday, what they've done is translation, not localization. The Spanish-speaking viewer still feels like a second-class audience member.",[19,23,24],{},"This article maps out the real tool stack for video localization in 2026 — the one I've seen work across hundreds of videos, from solo creators shipping weekly YouTube content to teams running multi-language TikTok operations. Every recommendation comes with trade-offs, because there is no single \"best\" tool. There's only the right tool for your volume, budget, and quality bar.",[14,26,28],{"id":27},"the-four-tiers-of-localization-effort","The four tiers of localization effort",[19,30,31],{},"Before picking tools, decide what you're actually trying to pull off. Most teams over-invest in the wrong tier.",[33,34,35,57],"table",{},[36,37,38],"thead",{},[39,40,41,45,48,51,54],"tr",{},[42,43,44],"th",{},"Tier",[42,46,47],{},"What you do",[42,49,50],{},"Cost per 10-min video",[42,52,53],{},"Turnaround",[42,55,56],{},"When to use",[58,59,60,81,100,119],"tbody",{},[39,61,62,69,72,75,78],{},[63,64,65],"td",{},[66,67,68],"strong",{},"T1: Subtitle-only",[63,70,71],{},"Translate SRT\u002FVTT, burned-in or sidecar",[63,73,74],{},"$0–15",[63,76,77],{},"15–30 min",[63,79,80],{},"Internal videos, low-stakes social",[39,82,83,88,91,94,97],{},[63,84,85],{},[66,86,87],{},"T2: AI dub + subs",[63,89,90],{},"Machine translation + AI voiceover + translated subs",[63,92,93],{},"$5–50",[63,95,96],{},"30–90 min",[63,98,99],{},"Most YouTube, social, tutorials",[39,101,102,107,110,113,116],{},[63,103,104],{},[66,105,106],{},"T3: AI + human QC",[63,108,109],{},"AI pipeline + professional linguist review + manual timing tweaks",[63,111,112],{},"$100–400",[63,114,115],{},"1–3 days",[63,117,118],{},"Brand content, product demos, courses",[39,120,121,126,129,132,135],{},[63,122,123],{},[66,124,125],{},"T4: Full production",[63,127,128],{},"Human translation + voice actors + audio post + visual rebuild",[63,130,131],{},"$2,000–8,000",[63,133,134],{},"1–4 weeks",[63,136,137],{},"TV commercials, theatrical, flagship brand films",[19,139,140,143],{},[66,141,142],{},"The mistake I see most often",": a team producing 30 TikTok videos a week thinks they need T3. They don't. T2 with a good glossary and a 5-minute human spot-check covers 90% of social content. Save T3 for the content that actually moves revenue.",[14,145,147],{"id":146},"the-pipeline-step-by-step","The pipeline, step by step",[19,149,150],{},"Every localization workflow goes through five stages. The tools you pick at each stage determine your throughput ceiling and your quality floor.",[152,153,155],"h3",{"id":154},"stage-1-transcription-speech-text","Stage 1: Transcription (speech → text)",[19,157,158],{},"Accuracy here sets the ceiling for everything downstream. If the transcript has a 5% word error rate, your translation inherits that noise and amplifies it.",[33,160,161,180],{},[36,162,163],{},[39,164,165,168,171,174,177],{},[42,166,167],{},"Tool",[42,169,170],{},"Best for",[42,172,173],{},"Accuracy (English)",[42,175,176],{},"Pricing",[42,178,179],{},"Notes",[58,181,182,201,220,239,258],{},[39,183,184,189,192,195,198],{},[63,185,186],{},[66,187,188],{},"OpenAI Whisper (large-v3)",[63,190,191],{},"Maximum accuracy, locally hosted",[63,193,194],{},"96%+",[63,196,197],{},"Free (self-host) \u002F $0.006\u002Fmin (API)",[63,199,200],{},"Gold standard; needs GPU for real-time",[39,202,203,208,211,214,217],{},[63,204,205],{},[66,206,207],{},"Descript",[63,209,210],{},"Creator-friendly UX, built-in editing",[63,212,213],{},"94%+",[63,215,216],{},"$24\u002Fmo (includes editing suite)",[63,218,219],{},"Best all-in-one for solo creators; transcription + editing in one app",[39,221,222,227,230,233,236],{},[63,223,224],{},[66,225,226],{},"Adobe Premiere Pro (Speech to Text)",[63,228,229],{},"Editors already in Adobe ecosystem",[63,231,232],{},"92%+",[63,234,235],{},"Included in Creative Cloud ($59.99\u002Fmo)",[63,237,238],{},"Convenient if you're editing there anyway",[39,240,241,246,249,252,255],{},[63,242,243],{},[66,244,245],{},"Rev",[63,247,248],{},"Human-verified transcription when accuracy is non-negotiable",[63,250,251],{},"99%+",[63,253,254],{},"$1.50\u002Fmin",[63,256,257],{},"Still the go-to for legal\u002Fcompliance content",[39,259,260,265,268,270,273],{},[63,261,262],{},[66,263,264],{},"Deepgram (Nova-2)",[63,266,267],{},"API-first, lowest latency",[63,269,213],{},[63,271,272],{},"$0.0043\u002Fmin",[63,274,275],{},"Good for real-time pipelines; solid multi-accent English",[19,277,278,281],{},[66,279,280],{},"The play",": If you edit in Descript or Premiere, use their built-in transcription — the workflow integration is worth the 1–2% accuracy drop. For a standalone transcription service in a code pipeline, Whisper API or Deepgram. For content where a single mistranscribed word could cause legal trouble, pay for Rev.",[152,283,285],{"id":284},"stage-2-translation-text-localized-text","Stage 2: Translation (text → localized text)",[19,287,288],{},"This is where the quality spread is widest. The gap between \"good enough for social\" and \"brand-safe for a campaign\" is roughly 20x in cost.",[33,290,291,311],{},[36,292,293],{},[39,294,295,297,300,303,305,308],{},[42,296,167],{},[42,298,299],{},"Quality",[42,301,302],{},"Speed",[42,304,176],{},[42,306,307],{},"Glossaries",[42,309,310],{},"Best scenario",[58,312,313,335,357,379,400],{},[39,314,315,320,323,326,329,332],{},[63,316,317],{},[66,318,319],{},"DeepL Pro",[63,321,322],{},"Excellent for European languages",[63,324,325],{},"Near-instant",[63,327,328],{},"$25\u002Fmo (unlimited)",[63,330,331],{},"Yes",[63,333,334],{},"Structured content: tutorials, docs, product",[39,336,337,342,345,348,351,354],{},[63,338,339],{},[66,340,341],{},"GPT-4o \u002F Claude",[63,343,344],{},"Excellent+ for conversational content",[63,346,347],{},"Seconds",[63,349,350],{},"~$10–15\u002Fmillion tokens",[63,352,353],{},"Via prompt",[63,355,356],{},"Interviews, Vlogs, comedy — content where context matters",[39,358,359,364,367,370,373,376],{},[63,360,361],{},[66,362,363],{},"Google Cloud Translation",[63,365,366],{},"Good",[63,368,369],{},"Instant",[63,371,372],{},"$20\u002Fmillion chars",[63,374,375],{},"Yes (Adaptive)",[63,377,378],{},"High volume, broad language coverage",[39,380,381,386,389,392,395,397],{},[63,382,383],{},[66,384,385],{},"DeepL + human review",[63,387,388],{},"Near-perfect",[63,390,391],{},"1–2 days",[63,393,394],{},"$0.08–0.15\u002Fword",[63,396,331],{},[63,398,399],{},"Brand campaigns, pitch videos",[39,401,402,407,410,413,416,419],{},[63,403,404],{},[66,405,406],{},"Professional agency",[63,408,409],{},"Best",[63,411,412],{},"3–7 days",[63,414,415],{},"$0.15–0.35\u002Fword",[63,417,418],{},"Managed",[63,420,421],{},"Enterprise, regulated industries",[19,423,424,427,428,432,433,436],{},[66,425,426],{},"The nuance nobody talks about",": LLMs (GPT-4o, Claude) outperform dedicated translation APIs on ",[429,430,431],"em",{},"conversational"," content — interviews, podcasts, unscripted dialogue — because they understand context and can rewrite for naturalness. But on ",[429,434,435],{},"structured"," content (product specs, step-by-step tutorials, legal disclaimers), DeepL is more consistent and costs a fraction. Use the right engine for the content, not the same engine for everything.",[19,438,439,442],{},[66,440,441],{},"Non-negotiable practice",": Build a glossary before you translate anything. Brand names, product names, technical terms, and recurring phrases locked to exact translations. Without a glossary, you'll call your product three different things across three videos, and your audience will notice.",[152,444,446],{"id":445},"stage-3-voiceover-text-speech","Stage 3: Voiceover (text → speech)",[19,448,449],{},"AI voiceover is the fastest-moving part of the stack. The naturalness gap between synthetic and human voices has shrunk dramatically since 2024.",[33,451,452,471],{},[36,453,454],{},[39,455,456,458,461,464,467,469],{},[42,457,167],{},[42,459,460],{},"Naturalness",[42,462,463],{},"Languages",[42,465,466],{},"Voice cloning",[42,468,176],{},[42,470,170],{},[58,472,473,495,516,537,558,579],{},[39,474,475,480,483,486,489,492],{},[63,476,477],{},[66,478,479],{},"ElevenLabs",[63,481,482],{},"Best in class",[63,484,485],{},"29",[63,487,488],{},"Yes (pro plan)",[63,490,491],{},"$5–99\u002Fmo",[63,493,494],{},"Premium AI voiceover; emotional range is unmatched",[39,496,497,502,505,508,510,513],{},[63,498,499],{},[66,500,501],{},"Play.ht",[63,503,504],{},"Very good",[63,506,507],{},"30+",[63,509,331],{},[63,511,512],{},"$31.20\u002Fmo",[63,514,515],{},"Conversational content; good pacing defaults",[39,517,518,523,525,528,531,534],{},[63,519,520],{},[66,521,522],{},"Murf.ai",[63,524,366],{},[63,526,527],{},"20+",[63,529,530],{},"No",[63,532,533],{},"$23\u002Fmo",[63,535,536],{},"Corporate\u002Ftraining voiceover with built-in editing",[39,538,539,544,546,549,552,555],{},[63,540,541],{},[66,542,543],{},"Azure Speech (TTS)",[63,545,366],{},[63,547,548],{},"140+",[63,550,551],{},"Custom voice (enterprise)",[63,553,554],{},"Pay-as-you-go (~$0.016\u002Fmin)",[63,556,557],{},"Scale: when you need 30 languages with consistent output",[39,559,560,565,567,570,573,576],{},[63,561,562],{},[66,563,564],{},"Descript (Overdub)",[63,566,366],{},[63,568,569],{},"English only",[63,571,572],{},"Your voice only",[63,574,575],{},"Included in Business ($33\u002Fmo)",[63,577,578],{},"Fixing mistakes without re-recording; not for full dubbing",[39,580,581,586,588,591,593,596],{},[63,582,583],{},[66,584,585],{},"Cutrix (integrated)",[63,587,504],{},[63,589,590],{},"50+",[63,592,331],{},[63,594,595],{},"From $9.90\u002Fmo",[63,597,598],{},"End-to-end: translation + voiceover + timing in one platform",[19,600,601,604],{},[66,602,603],{},"The timing problem",": Different languages take different amounts of time to say the same thing. English → Spanish typically expands 20–30%. English → Japanese can shrink 10–15%. If you don't adjust the audio timing, your dub will drift out of sync. Tools like Cutrix and HeyGen handle this automatically by adjusting speech rate within natural-sounding bounds. If you're using a raw TTS API (ElevenLabs, Azure), you'll need to handle timing yourself — usually by tweaking pauses or slight rate adjustments.",[19,606,607,610],{},[66,608,609],{},"Lip-sync",": If your video shows the speaker's face and you want the dub to match mouth movements — that's lip-sync, and it's a separate capability. ElevenLabs has a basic version. Cutrix and HeyGen offer it as part of their video translation suites. It adds cost and processing time but makes a significant difference for talking-head content.",[152,612,614],{"id":613},"stage-4-visual-localization","Stage 4: Visual localization",[19,616,617,618,621],{},"The text ",[429,619,620],{},"inside"," your video — title cards, lower thirds, UI overlays, chart labels — needs to change too. This is the most labor-intensive stage for most teams.",[33,623,624,641],{},[36,625,626],{},[39,627,628,631,633,636,639],{},[42,629,630],{},"Approach",[42,632,299],{},[42,634,635],{},"Effort",[42,637,638],{},"Cost",[42,640,179],{},[58,642,643,661,679,698],{},[39,644,645,650,652,655,658],{},[63,646,647],{},[66,648,649],{},"Template-based (After Effects mogrt)",[63,651,409],{},[63,653,654],{},"High upfront, low per-video",[63,656,657],{},"$ (once built)",[63,659,660],{},"Build once, swap text layers per language. Worth it for recurring formats",[39,662,663,668,670,673,676],{},[63,664,665],{},[66,666,667],{},"Manual replacement (Premiere\u002FDaVinci)",[63,669,366],{},[63,671,672],{},"High per-video",[63,674,675],{},"$$",[63,677,678],{},"Viable for \u003C 5 on-screen text elements",[39,680,681,686,689,692,695],{},[63,682,683],{},[66,684,685],{},"AI inpainting + re-render",[63,687,688],{},"Inconsistent",[63,690,691],{},"Low",[63,693,694],{},"$–$$",[63,696,697],{},"Tools are improving but still produce artifacts on complex backgrounds",[39,699,700,705,708,711,714],{},[63,701,702],{},[66,703,704],{},"Burn captions only",[63,706,707],{},"Acceptable",[63,709,710],{},"Very low",[63,712,713],{},"$",[63,715,716],{},"Accept that on-screen text stays in source language; rely on burned captions",[19,718,719,722],{},[66,720,721],{},"The honest advice",": If you're producing at volume, invest in template-based workflows. Build your graphics in After Effects with replaceable text layers. It's upfront work that pays back every time you localize. If you're a solo creator doing one video a week, manual replacement in your editor is fine. AI inpainting tools are not yet reliable enough for brand content — give them another 12–18 months.",[152,724,726],{"id":725},"stage-5-distribution-format","Stage 5: Distribution format",[19,728,729],{},"Different platforms want different things:",[33,731,732,750],{},[36,733,734],{},[39,735,736,739,742,745,748],{},[42,737,738],{},"Platform",[42,740,741],{},"Subtitle format",[42,743,744],{},"Audio",[42,746,747],{},"Aspect ratio",[42,749,179],{},[58,751,752,771,789,805],{},[39,753,754,759,762,765,768],{},[63,755,756],{},[66,757,758],{},"YouTube",[63,760,761],{},"Sidecar SRT (recommended) or burned",[63,763,764],{},"Stereo AAC",[63,766,767],{},"16:9",[63,769,770],{},"Sidecar lets viewers toggle; YouTube auto-translates SRT titles",[39,772,773,778,781,783,786],{},[63,774,775],{},[66,776,777],{},"TikTok",[63,779,780],{},"Burned-in required",[63,782,764],{},[63,784,785],{},"9:16",[63,787,788],{},"Captions must be on-screen; auto-caption can supplement",[39,790,791,796,798,800,802],{},[63,792,793],{},[66,794,795],{},"Instagram Reels",[63,797,780],{},[63,799,764],{},[63,801,785],{},[63,803,804],{},"Same as TikTok; separate upload per language",[39,806,807,812,815,817,820],{},[63,808,809],{},[66,810,811],{},"LinkedIn",[63,813,814],{},"SRT supported",[63,816,764],{},[63,818,819],{},"16:9 or 1:1",[63,821,822],{},"SRT support is newer; test before committing",[14,824,826],{"id":825},"the-three-tool-stacks-id-actually-recommend","The three tool stacks I'd actually recommend",[19,828,829],{},"After testing most combinations, here's what I'd suggest for three common profiles:",[152,831,833],{"id":832},"solo-creator-15-videosweek","Solo creator (1–5 videos\u002Fweek)",[835,836,841],"pre",{"className":837,"code":839,"language":840},[838],"language-text","Descript (transcribe + edit) → DeepL (translate) → ElevenLabs (voiceover) → Descript\u002FPremiere (assemble + export)\n","text",[842,843,839],"code",{"__ignoreMap":844},"",[19,846,847,850,851,854],{},[66,848,849],{},"Monthly cost",": ~$80–120 | ",[66,852,853],{},"Time per 10-min video",": ~45 minutes",[19,856,857],{},"This stack prioritizes workflow integration. Descript handles transcription and editing in one place. DeepL handles translation with a glossary. ElevenLabs handles voiceover. You're stitching tools together, but each one is best-in-class for its role.",[152,859,861],{"id":860},"small-team-1040-videosweek","Small team (10–40 videos\u002Fweek)",[835,863,866],{"className":864,"code":865,"language":840},[838],"Whisper API (transcribe) → DeepL Pro + GPT-4o for conversational segments (translate) → Azure TTS (voiceover at scale) → FFmpeg\u002FPython (automated assembly)\n",[842,867,865],{"__ignoreMap":844},[19,869,870,872,873,875],{},[66,871,849],{},": ~$300–800 | ",[66,874,853],{},": ~20 minutes (mostly automated)",[19,877,878],{},"At this volume, you need a scripted pipeline. Write a Python script that calls Whisper for transcription, DeepL for translation, Azure for TTS, and FFmpeg for assembly. A human reviews the output for quality, but doesn't touch every step. This is where engineering effort pays back.",[152,880,882],{"id":881},"team-doing-50-videosweek-or-needing-lip-sync","Team doing 50+ videos\u002Fweek or needing lip-sync",[835,884,887],{"className":885,"code":886,"language":840},[838],"Cutrix \u002F HeyGen (end-to-end platform) → Human QC on key segments\n",[842,888,886],{"__ignoreMap":844},[19,890,891,893,894,896],{},[66,892,849],{},": ~$200–1,500 (platform subscription + API) | ",[66,895,853],{},": ~10 minutes (upload + review)",[19,898,899],{},"At scale, an integrated platform becomes the better choice. You trade some per-step flexibility for not having to maintain the pipeline yourself. Cutrix covers the full chain (transcription → translation → voiceover → timing alignment) with built-in lip-sync and supports 50+ languages. HeyGen adds AI avatar capabilities if your content involves on-screen presenters. The key difference from the DIY approach: you spend your time reviewing output quality instead of debugging pipeline code.",[14,901,903],{"id":902},"what-to-measure-so-you-know-if-this-is-working","What to measure (so you know if this is working)",[19,905,906],{},"Most teams skip measurement and just hope the localized content performs. Don't do that. Track these:",[908,909,910,917,923,929],"ul",{},[911,912,913,916],"li",{},[66,914,915],{},"View-through rate by language",": If Spanish-dubbed videos have a 40% drop-off after 15 seconds but English originals don't, your dub probably sounds stilted",[911,918,919,922],{},[66,920,921],{},"Subtitle toggle rate",": On YouTube, if 80% of viewers in a non-English market have captions ON, your dub quality may be the issue — they're reading, not listening",[911,924,925,928],{},[66,926,927],{},"Comment sentiment by language",": Are Spanish comments praising the content but English comments silent? The localization might be creating a barrier",[911,930,931,934],{},[66,932,933],{},"Per-language CTR",": If your thumbnail text is localized but CTR varies wildly by language, your translated titles may not be compelling",[14,936,938],{"id":937},"faq","FAQ",[152,940,942],{"id":941},"how-much-does-video-localization-cost-in-2026","How much does video localization cost in 2026?",[19,944,945],{},"For AI-powered localization (T2 quality), expect $5–50 per 10-minute video depending on your tool stack and whether you self-host or use APIs. For professional human localization (T4), budget $2,000–8,000 per 10 minutes. Most creator teams operate in the $100–400\u002Fmonth range using AI tools with light human review.",[152,947,949],{"id":948},"whats-the-difference-between-elevenlabs-dubbing-and-a-dedicated-video-localization-platform","What's the difference between ElevenLabs dubbing and a dedicated video localization platform?",[19,951,952],{},"ElevenLabs is a voice AI company — their core strength is voice synthesis and cloning. Their dubbing product translates and generates voiceovers, but it doesn't handle on-screen text replacement, subtitle formatting, or platform-specific exports. A dedicated video localization platform (Cutrix, HeyGen) covers the full chain: transcription → translation → voiceover with timing alignment → subtitle generation → export. If you only need voiceover, ElevenLabs is excellent. If you need the whole video localized, a platform saves you from stitching together 4–5 tools.",[152,954,956],{"id":955},"should-i-localize-into-every-language-or-pick-a-few","Should I localize into every language or pick a few?",[19,958,959],{},"Pick 2–3 languages where you have actual audience data or market intent. Spanish, German, and Japanese cover large addressable markets for most English-origin content. Going to 8+ languages before you've validated that localization drives engagement in any of them is premature optimization. Start with one language, measure, then expand.",[152,961,963],{"id":962},"can-i-use-descript-for-the-whole-thing","Can I use Descript for the whole thing?",[19,965,966],{},"Descript is excellent for transcription and editing, good for basic voiceover (Overdub), but weak for translation. Their translation feature is serviceable for rough cuts but not production-ready for most languages. Use Descript for the editing workflow, but bring in DeepL or GPT-4o for translation, and consider ElevenLabs or a platform for voiceover if quality matters.",[152,968,970],{"id":969},"is-ai-lip-sync-ready-for-production","Is AI lip-sync ready for production?",[19,972,973],{},"For talking-head content where the speaker is directly facing the camera: yes, with caveats. Cutrix and HeyGen both offer lip-sync that looks natural in most lighting conditions and speaking styles. It works best with clear, front-facing shots and moderate speech pace. Rapid cuts, side profiles, or low-light footage will produce visible artifacts. For content where the speaker is off-camera or shown intermittently, skip lip-sync entirely — the cost and processing time aren't justified.",[14,975,977],{"id":976},"references","References",[908,979,980,989,996,1003,1010,1017,1024,1031],{},[911,981,982],{},[983,984,988],"a",{"href":985,"rel":986},"https:\u002F\u002Felevenlabs.io",[987],"nofollow","ElevenLabs — Dubbing & Voice AI",[911,990,991],{},[983,992,995],{"href":993,"rel":994},"https:\u002F\u002Fwww.descript.com",[987],"Descript — All-in-one video editing",[911,997,998],{},[983,999,1002],{"href":1000,"rel":1001},"https:\u002F\u002Fwww.deepl.com\u002Fpro-api",[987],"DeepL API",[911,1004,1005],{},[983,1006,1009],{"href":1007,"rel":1008},"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper",[987],"OpenAI Whisper",[911,1011,1012],{},[983,1013,1016],{"href":1014,"rel":1015},"https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fproducts\u002Fai-services\u002Fai-speech",[987],"Azure AI Speech",[911,1018,1019],{},[983,1020,1023],{"href":1021,"rel":1022},"https:\u002F\u002Fwww.cutrix.cc",[987],"Cutrix — AI Video Translation & Dubbing",[911,1025,1026],{},[983,1027,1030],{"href":1028,"rel":1029},"https:\u002F\u002Fwww.heygen.com",[987],"HeyGen — AI Video Translation",[911,1032,1033],{},[983,1034,1037],{"href":1035,"rel":1036},"https:\u002F\u002Fwww.rev.com",[987],"Rev — Professional Transcription & Captions",{"title":844,"searchDepth":1039,"depth":1039,"links":1040},2,[1041,1042,1043,1051,1056,1057,1064],{"id":16,"depth":1039,"text":17},{"id":27,"depth":1039,"text":28},{"id":146,"depth":1039,"text":147,"children":1044},[1045,1047,1048,1049,1050],{"id":154,"depth":1046,"text":155},3,{"id":284,"depth":1046,"text":285},{"id":445,"depth":1046,"text":446},{"id":613,"depth":1046,"text":614},{"id":725,"depth":1046,"text":726},{"id":825,"depth":1039,"text":826,"children":1052},[1053,1054,1055],{"id":832,"depth":1046,"text":833},{"id":860,"depth":1046,"text":861},{"id":881,"depth":1046,"text":882},{"id":902,"depth":1039,"text":903},{"id":937,"depth":1039,"text":938,"children":1058},[1059,1060,1061,1062,1063],{"id":941,"depth":1046,"text":942},{"id":948,"depth":1046,"text":949},{"id":955,"depth":1046,"text":956},{"id":962,"depth":1046,"text":963},{"id":969,"depth":1046,"text":970},{"id":976,"depth":1039,"text":977},"Tutorial","https:\u002F\u002Fweujie-assets-1304902766.cos.ap-guangzhou.myqcloud.com\u002Fblog\u002Fcovers\u002Fvideo-localization-workflow-guide.jpg","2026-05-20","A practical tool stack for video localization in 2026—from transcription and translation to voiceover, cultural adaptation, and multi-platform publishing.","md","en",{},true,"\u002Fblog\u002Fen\u002Fvideo-localization-workflow-guide",{"title":5,"description":1068},"blog\u002Fen\u002Fvideo-localization-workflow-guide","QxLJ7ELGLVspbVeVi9gpe0wU8LkSombHU1_pcJMQQVI",1779258279882]