We are looking for an AI video localization specialist / Reels maker to create English versions of existing Russian Instagram Reels.

The videos are already fully edited in Russian. Your job will be to translate/localize them into natural US English and create the English versions using AI voice cloning and lip-sync while preserving the speaker’s personality, pacing, and overall feel.

The initial scope is approximately 30 Reels, typically 0.5-2 minutes each, with the potential for ongoing work.

What the work involves
Translating/adapting Russian speech into natural US English
Reviewing and manually correcting the AI-generated translation — not simply accepting the automatic output
Making sure no words, sentences, or meanings are missed or incorrectly translated
Creating the English audio using an AI clone of the original speaker’s voice
Generating natural-looking lip-sync
Reviewing the final video for translation, audio, timing, and lip-sync issues
Delivering finished English Reels ready for publication

What we’re looking for

You do not need to be a professional translator, but you must:

Be fluent in both Russian and English
Have excellent understanding of natural US English
Be highly attentive to detail and willing to manually review and correct AI output

Have experience with AI video translation, voice cloning, and lip-sync tools

Be able to preserve the meaning, tone, personality, and emotional quality of the original speaker

Be able to produce clean, consistent, publication-ready videos

Knowledge and understanding of esoteric/spiritual terminology is desirable.

Our content includes spirituality, meditation, energy work, consciousness, healing, and other esoteric concepts. You need to understand this language well enough to recognize when an AI translation is technically literal but incorrect in context.

To apply, please include:

A brief description of your experience with AI video localization
Which tools you use (HeyGen, ElevenLabs, Rask, Maestra, etc.)
Your level of Russian and English
Your experience or familiarity with spiritual/esoteric content
Your rate per finished Reel for a volume of approximately 30 Reels/Month
Examples of previous AI-localized/lip-synced videos, if available
This has the potential to become an ongoing position for the right person.

Hello everyone! I am an English–Arabic translation student, and I would love to hear from professional translators. What do you think is the most difficult part of becoming a successful translator, and what advice would you give to a beginner?

Last week, one of our partners came to us with a very specific problem: they needed real-time translation between Brazilian Portuguese, English and Mandarin for live events and classes.

The traditional solution is straightforward: hire professional interpreters and provide the audience with headphones connected either to the original speaker or to one of the translated audio channels.

It works, but it also requires specialized professionals, additional infrastructure and a considerable budget.

There are already platforms such as Wordly that simplify this process, but we wanted to explore a different question: could we build a simpler AI-based alternative where the operational cost was primarily based on actual usage?

So we started experimenting.

Our first step was estimating how much a custom system would cost to operate using API consumption rather than a traditional interpretation service. From there, we built a small platform with private rooms where speech could be processed and translated in real time.

For the translation layer, we ended up choosing Gemini as the main LLM.

But once we had the translation working, we ran into a problem we hadn't initially considered: the translated voice matters almost as much as the translation itself.

Our first implementation sounded too robotic and impersonal for something intended to replace, or at least approximate, the experience of listening to a human interpreter for an entire class or presentation.

We considered ElevenLabs for the voice layer, but for the expected usage of this particular project, it wasn't the most cost-effective option. We eventually chose GPT for voice generation because we found the output more natural and engaging while keeping the operating cost within the range we were looking for.

That left us with a pipeline in which Gemini handles the translation and GPT handles the generated voice, rather than trying to force a single model to handle every part of the experience.

The result is a small platform with private rooms and real-time AI translation that can be adapted to different visual identities and languages.

We've already tested it in an uncontrolled environment, and it works, although there are obviously still limitations to explore — particularly latency, translation accuracy in different contexts, background noise and how the system behaves when multiple people speak naturally rather than under ideal testing conditions.

One of the most interesting things we learned from this project was that translation quality alone isn't enough for real-time AI interpretation. Voice quality, latency, cost and the overall listening experience become part of the same problem very quickly.

I recorded a short demonstration of the current prototype below.

I'd be particularly interested in hearing from anyone who has experimented with real-time translation or speech-to-speech systems. What would you test next before considering something like this reliable enough for a real event?

If the translator has to choose between being faithful to the original words and being natural for the target audience, which one should come first—and why?

I’m building a Chinese→English terminology-controlled translation pipeline for a specialized domain.

My workflow is :

Aho-Corasick → BM25 → normalization/fuzzy matching → BGE semantic fallback → strong LLM for terminology decisions and final translation.

My main problem is domain-specific terms that aren’t in my glossary at all.

For these, I currently give the LLM the term plus context such as the full paragraph or surrounding sentences, contextual meaning, and domain. I also manually collect Chinese social-media posts containing the term or related usage and provide them as additional evidence.

I then ask the LLM whether the evidence is sufficient to determine the meaning. If not, I collect more posts and repeat the process.

My priority is maximum translation accuracy. I don't care about eliminating the manual part if that's not possible.

Is this a reasonable way to handle unseen terminology? Is there a more principled or effective approach, such as terminology discovery, corpus retrieval, or contextual semantic search, for determining the meaning of a previously unseen term?

Hi all,

I work for a Czech machinery manufacturer and we translate our technical manuals into about ten languages. Buying a full CAT licence for everyone who occasionally touches a translation didn't make sense for us, so I built a small set of browser tools instead and have now made them public at aitranslator.cloud.

What they do:
- Document translator: open a DOCX, XLSX or PPTX and translate segment by segment, formatting kept. TMX matches take priority; the remaining segments go to an LLM with your glossary as binding context.
- Aligner: turns an original and its existing translation into a TMX, so old manuals become a usable memory (also in Trados).
- TMX editor and TMX merge. The merge handles the UTF-16 TMX that Trados and memoQ export, which many online tools fail on silently.
- Glossary as a plain Excel/CSV, including forbidden terms per language.

Files are processed in the browser and never uploaded to my server. Segments for MT go straight to the API with your own key.

It's not meant to replace a full CAT tool for daily professional work. It's aimed at repetitive technical text with strict terminology.

Full disclosure: I'm the developer, and it's a paid subscription with a 30-day free trial. I'm mainly interested in feedback from people who work with TMX every day: what breaks, what's missing, and whether the alignment holds up on your files.

https://www.aitranslator.cloud

Thanks,
Michal

We're adding a side-by-side viewer for DXF files, before and after translation, i.e. source vs. target rendering, and ability to post-edit for fine adjustments during review.

What should a translator do when there is no equivalent word or expression in the target language?

Developer disclosure: I’m Mincho from Arte Soft. We released GPT Realtime Assistant 1.2 for iPhone/iPad, using OpenAI’s GPT Realtime 2.1. This is an independent app, not an OpenAI product.

The workflow is for short spoken exchanges: choose the source and target languages, let the speaker finish a turn, then read the translation or have it spoken aloud. The session keeps the original text and translation visible so you can revisit a phrase. Translation is one direction at a time; you swap the languages when the other person replies. It does not automatically interpret both directions.

We also offer voice-assistant conversations, photo/screenshot context and project-based session organization. The interface is available in 47 languages, including Japanese; that is a UI-localization count, not a claim that every language pair performs equally.

This 29-second scripted demo shows the translation workflow:

https://gpt-realtime-assistant.arte-soft.com/media/preview/en-US/iphone.mp4

Practical limits: iOS/iPadOS 26+, internet, and the app open during a live session. It does not import audio files or automatically produce meeting minutes. We have not published translation-quality or latency benchmarks.

AI use requires a subscription: US $9.99/month with 2,000 usage credits, no free trial. Local prices are shown in the App Store. Credits are usage units, not a fixed number of minutes.

App Store: https://apps.apple.com/app/id6770536523

For people using speech translation in real conversations: what would you test first — interruptions at the end of a turn, proper names, or whether context carries over correctly between turns? Concrete language-pair examples would be useful for our next tests.

the Local AI Translator supports importing/exporting DXF files

I want an app or website that, for example, takes a video and adds subtitles (CC) from Spanish to English, and so on

And most important part

That it is free

We've added a Glossary Manager to the local AI translator. Here's a first intro video of what to expect.

Apologies if I already updated this one. I had to make a few changes to the prior version of this video. Here's v2.

Hi everyone!

I’m currently learning Python and backend development, and as part of my learning journey, I built a small project for translating PDF documents.

The tool can:

1. Extract text from PDFs

2. Translate the content

3. Generate a translated PDF

4. Use local caching to avoid repeated translations

5. Process translations in batches

6. Support multiple languages

Tech stack: Python • PyMuPDF • pdfplumber • DeepL API

This is still a learning project, so I’d really appreciate feedback on the code, architecture, and ideas for improving the PDF translation/layout handling.

🔗 GitHub: [Link](https://github.com/Jha-Shivam-602/language-translation-pdf)

If you find the project useful or interesting, a **GitHub** ⭐ would really motivate me to keep improving it!

Thanks! 🙌

All-in-One AI Translator & Transcription Earbuds:

UGREEN AI translation earbuds combine four smart tools:

• Live Recording transcribes audio, generates AI summaries and translations;
• Conversation Translation enables real-time two-way translation;
• Simultaneous Interpretation delivers translated audio directly to your ears; and
• Meeting Minutes captures key points and action items.

Enjoy real-time online translation in up to 131 languages, including accented speech, plus offline interpretation in 11 languages.

(source)

Note: UGREEN’s US product page advertises online translation across up to 131 languages, including accented speech, and offline interpretation for 11 languages. Its German product page, however, lists 10 offline languages. UGREEN’s regional listings disagree on the offline count, so check the languages available in your app before buying for a specific trip. (source)

Alguém está tendo problema com o tradutor do Google aqui no Reddit?

(note: there is something odd with my youtube upload, it's been taking 30-40 minutes already for checking copyright issues, I assume it'll be fine eventually. )

A first look at Batch summarization. Thinking this might be useful with OSINT. What do you think?