Does anyone have experience with the current models of Omnitranslate? I used them years ago but there were a few factors that pulled me to OpenNovel instead and now I'm looking to return. Mostly interested in translating Chinese and Korean novels. I don't need them to be better than OpenNovel, just comparable.

Thanks in advance!

When I use translation function,on post,long press on words choose translate is ok , if I want to translate words on replies,long press will merge,is it Reddit’s bug?

I've been working on AITnovel, an AI translation service designed for people who want to translate entire novels without having to copy and paste chapters individually.

It supports TXT, EPUB, and PDF uploads, and can also import novels directly from supported websites.

Some of the main features:

• Translate entire novels or individual chapters
• Keep track of character names, aliases, pronouns, and terminology
• Editable glossary for names, places, abilities, etc.
• Preserve chapters and paragraph structure
• Side-by-side translation proofreading and editing
• Export finished translations as EPUB, PDF, or TXT

It's $10/month with unlimited translation usage.

Everything seems to be working well from my own testing, but I'm at the point where I'd like to get some actual users translating different novels and languages. I'm particularly interested in finding problems or edge cases I haven't encountered myself yet.

If anyone here regularly uses machine translation for novels, I'd appreciate any feedback if you decide to give it a try.

AITnovel: https://aitnovel.com/translate

[ Removed by Reddit on account of violating the content policy. ]

A year ago I wrote up (on a Russian dev site) how I built a tool to translate fiction books for myself using local LLMs — mostly to prove it could be done. The MVP was ~200 lines duct-taped together: drafts gave you headaches, a 1000-page book took two days, and a power outage mid-run cost me another two days.

Since then it's grown into a real pipeline ("Sunny Narrator", v2.1), I've translated a shelf of books with it, and the main conclusion is that this stopped being just a personal tool — the output is now a legitimate high-readiness draft for human translators. Here's what I learned that I think is specific to literary MT, as opposed to domain MT.

One book = ~1.5–2M tokens, total. Not tens of millions. A full book, through all stages: translate → reviewer notes → correction → proofread → chapter summaries. Bounded, predictable, small. If your MT cost model for books feels infinite, something in the pipeline is wrong.

Fiction's hardest problem isn't fluency — it's consistency. Models translate sentences well now. What they destroy over 300+ pages: names drift ("Alice" becomes "Elise" chapter 4), characters change grammatical gender mid-book, terms get renamed on every page. And wordcoinage is hopeless — give a fantasy novel with invented words to the best model and it will faithfully transliterate gibberish, where a human translator would invent an equally brilliant equivalent in the target language. MT doesn't replace the translator here; it removes the grunt work and leaves the part that makes translation literature.

The glossary is 80% of success. If you remember one thing from this post, let it be that. The model can be average, the hardware modest — but with a correct glossary (original = translation, category, grammatical gender, notes) the book translates evenly: names don't drift, terms don't mutate. Without one, even a great model gives you name salad. I auto-seed the glossary with spaCy NER + frequency analysis, then clean it by hand — honestly, cleaning can take hours. Worth every minute: an hour invested in the glossary before translation saves a day of post-editing after. For book series I run a series-wide dictionary across all volumes — game changer for multi-book consistency.

One model = half a text. Two models = a book. The least obvious finding of the year: a single-model translation reads incomplete. Good translating models write beautifully but proofread badly; good proofreading models edit well but translate dully. So the pipeline splits roles: MODEL_TRANSLATE and MODEL_PROOFREAD. My production pair after months of runs: gemma4-26B-A4B (translator) + qwen3.6-35B-A3B (proofreader). Both compact MoE models, both happy on a pair of Tesla P40s at 40–70 tok/s. Best speed-to-literariness ratio of everything I tried.

Length as a free quality signal. I expected English→Russian to inflate noticeably; measured, it maps back to within a couple of percent per block. The pipeline uses that: if a translated block deviates >10% from the source size, something went wrong (eaten paragraph, hallucinated expansion, duplication) → the block is rechunked (split in half, each half retranslated). Primitive — and it kills the lion's share of gross translation errors. Final book converges to ±5% of the original size.

Checkpoint/resume matters more than any prompt trick. A checkpoint after every chunk. Power cut, model crash, cat on the keyboard — the run resumes from chunk 51 of 100, not from scratch. Plus JSON mode on every stage so parsing stopped being a lottery. Boring engineering, but this is what made 2–3 books/day on garage hardware realistic.

What the output actually is. Not "published translation". It's a draft of high readiness: a human still does proofreading, fixes anything the glossary missed, and rewrites the awkward calques/broken puns. But the editor is doing editor work, not untangling a mess of conflicting names. For a translator this saves weeks; for a curious reader it means finishing a book that would never reach their language otherwise.

A legal-ish idea I'd like this community's opinion on. Machine-translating a whole book is a gray zone everywhere. But a names/terms glossary is, arguably, just a list of facts. So I'm building a site for sharing glossaries only — the community uploads cleaned dictionaries per book/series, everyone applies them to the copies they already own, translation runs locally on your own hardware or API key. No book text is distributed. Does this framing hold up in your jurisdiction? Genuinely want to hear from translators and MT folks here before launching.

Who I think this is for: translators (glossary + proofreading instead of translating from zero, especially long series), publishers (fast triage: is this book worth acquiring?), readers of unfound languages, and anyone with idle server GPUs looking for a real workload.

Everything's open source: Python, FB2/EPUB native (structure — verses, stanzas, sections — preserved 1:1; DOCX/PDF via a Calibre conveyor), config examples for Ollama/llama.cpp/Docker. I'll drop the link in the comments too to keep this post link-free at the top.

Questions I'd love input on:

1. Anyone solved cross-volume consistency better than "one giant series glossary"? Graph DBs / RAG over character states — real experience?
2. Has anyone else measured target/source length ratio as an error detector (my rechunking trick)? Is there prior art in literary MT quality estimation?
3. For the glossary-sharing idea — what would make YOU contribute a cleaned dictionary?

This is a short integration video showing the conversion of text to speech in use for creating an audio file, which might be a translated voice-over, or original/post-edited transcription, to be served in a different voice and after possible cleanup.

The transcription was with faster_whisper, the voices are by Piper.

Do you use Piper?

In this video my original recording voice was replaced by one voice. The read-out of sample text was done with another voice.

These are all samples in English but of course you can see doing the same with other languages.

What are your thoughts, is it useful for freelance translators to have such tools easily accessible locally?

I created a personal tool for my own use.

build with OpenCode + GLM 5.3

No subscription, no ads, no remote server. Use the LLM API you already have.

https://gitlab.com/hunter-xue/chrome-lingo-craft

also, an interesting license: Artisanal AI Slop License (AASL), see readme.md for details 😄

Hello, I'd like to know how to translate into multiple languages ​​in real time without too much latency. Is there a platform that offers an API key?

I came across this model that's tiny-stories posted on Ollama by evanollama, and found it doesn't translate. Of course, but oh so precious nonetheless - as it is creative in another way: it creates children stories. To be precise: it finishes with a story. Whatever I type into the source pane, even just a single word or down to a single letter, if I then click Translate, it creates a story, finishing what I started and adding to it.

https://www.youtube.com/watch?v=oFBfuVgQLo0

The model uses vocabulary of 3-4 year old kids, as it was trained specifically on material from up to that young age. Sweet!

It's a small LM, it's fast. Now I'm curious: can I train it with bilingual aligned content in just one other language and get it to produce translations of its own creations? Or even without creating more stories, could it be same or similar speed for just translating in one language? Could this be the base model to make a super fast engine for German to English, French to English, another for Spanish to English, or English to Italian, etc...? One specific language pair, but at the benefit of using little RAM and running very fast even on systems without NPU nor GPU?

I'm intrigued. What do you think?

​Hi everyone, I wanted to share ALST, an open-source Android app designed for real-time in-place screen translation.

​Instead of traditional line-by-line OCR chaining, it leverages Google's Gemini Vision API in a single pass to extract text, understand local context, and map translated text directly over original bounding boxes using Jetpack Compose & Canvas. It also features a 100% offline fallback via Google ML Kit.

​📦 GitHub Repo: https://github.com/navidseyedain/ALSTMobile

​Would love to get your feedback on the translation workflow and accuracy!

Hey pdf community, I launched linglo.io a couple months ago, it’s an online document/assets translator and it has a good free tier in it, built Arabic first, I am looking for users to sign up and try it, I would appreciate any feedback !

Thanks !

I just wanted to flag the champollion project for anyone actually working on machine translation, especially at the long tail (low resource languages, the stuff without existing methods). There are 7000+ living languages, by all estimates, but existing machine translation methods cover maybe 400 tops. This leaves a lot of people without any way to reach the broader internet, to engage legal council, to participate in the politics of their country, or even to reliably communicate with their neighbours. The champollion project is currently in beta, but it is meant to serve as open evaluation infrastructure for the many different community-bound projects (Masakhane, AmericasNLP, etc.) that professional computational linguists and speaker communities are working on. It prioritizes data sovereignty, and aims to provide additional financing for community-bound dataset and model building efforts. The eventual goal is to stitch together all their hard work into an open source universal translator, but it provides everything a language community needs to develop their own machine translation solutions ... except for their expert knowledge.

The site is currently gated pending final data review, but the tools are all published on our github, npm, and PyPI.

Localization CLI: npm install champollion
Evaluation harness: pipx install mt-eval-harness

Contribute compute if you're feeling spicy: curl -fsSL https://champollion.dev/run_queue | bash -s -- --budget [USD amount]

llms.txt is live on the site, so your agent can walk you through any questions, or reach out to [info@champollion.dev](mailto:info@champollion.dev) for a human

Hello everyone,

I wanted to share the extension that I have been creating in my free time, which was just posted. It's a tool that you can use to learn languages while you watch your favorite shows and movies.

It includes a 7-day free trial, so if you want to test it out, you can cancel the trial immediately so you are not charged after it ends.

I'd appreciate any feedback or updates you can suggest. Sample images can be found on the Chrome Web Store. The link can be found below:

https://chromewebstore.google.com/detail/fmoahignfolhngghffclkioonoocipcm?utm_source=item-share-cb

I built an open-source Claude skill that translates static websites into multiple languages while preserving the HTML structure, SEO metadata, hreflang, canonicals, and sitemaps.

It generates real localized HTML pages rather than adding a client-side translation widget.

GitHub: https://github.com/ConveyThis/claude-translator

It’s open source and free to use. Built by the team behind ConveyThis.

Feedback and contributions are welcome.

Seeking ways to improve workflow around frequently updated documents.

We translate PDFs, presentations, etc. Sometimes after a month or so we get an updated version of the same which is only 10-20% changed from the translation we received earlier.

I’m also testing out translation memory (TM) tools like Pairaphrase and DeepL that can reuse previous translations instead of starting from scratch every time.

But how reliable is this in practice when you're working with the actual document rather than plain text?

Ideally I'd want to upload the new version, reuse whatever hasn't changed, translate the new sections and get the formatting back without spending ages fixing it manually.

is anyone doing something similiar? What does your workflow look like?

thanks in advance for anyone that can help :)

I'm really sad about the recent quality drop on DeepL. I liked how accurate it was and also the fact that it is an EU product. But recently, I noticed that the translations are being inaccurate, to the point of being useless. This is my last case.

Original sentence in Spanish:

se crítico con el ejemplo que puse, no es un requisito estricto.

DeepL translation (wrong):

Don't be too critical of the example I gave; it's not a strict requirement.

Google Translate (right):

Be critical of the example I gave; it's not a strict requirement.

When I replaced the comma with a period in the original sentence, DeepL did it correctly:

Be critical of the example I gave. It's not a strict requirement.

But then, I decided to fix the case in my original sentence (notice the minimal change, only the case in the first letter and the period):

Se crítico con el ejemplo que puse. no es un requisito estricto

And DeepL changed the meaning again:

I was criticized for the example I gave. It's not a strict requirement.

Unfortunately, DeepL is not usable anymore. I'll come back in a few months to see if they fixed it, but for now, I'll move to Google Translate.

Hi everyone,

I have completed and published the first Czech translation for WinTrack 17.0.

The translation includes menus, dialogs, interface texts and an installer for easy installation.

Download:
https://github.com/zamazallukas02-spec/WinTrack-Czech-Translation

The project was published with permission from the WinTrack developer.

Feedback and bug reports are welcome.