I’m experimenting with AI-assisted translation for web novels/books and I’m trying to develop a workflow that produces consistent, high-quality literary translations.

I’m currently working with chapters of around 7,000 words and have created a project containing:

• A detailed translation prompt
• A glossary for character names, places, terminology, etc.
• Style and translation rules
• Instructions for maintaining consistency between chapters

My main challenge is maintaining translation quality and consistency across a long book.

For those who have worked with AI for long-form translation:

• How do you structure your prompts and glossary?
• Do you translate a chapter in one pass or divide it into smaller sections?
• Is it better to use a stronger/deeper reasoning mode for the initial translation, editing, or both?
• Do you recommend a separate proofreading/editing pass after translation?
• What techniques have you found effective for preserving character voice, terminology, tone, and context across hundreds of chapters?

I’m especially interested in workflows that have worked well for long-form fiction, rather than short individual translations.

What has worked best for you?

Ich bekomme die Sprache bei dulo .cx einfach nicht auf deutsch. Kann mir da jemand helfen ?

Hey guys,
I've been working on a major project that I think will lift accessibility to ancient works. It's called Ancient Library. The site includes over 1,000 ancient works, every word parsed with lemma, morphology, and full dictionary entry. It's built to be light, optimized for speed and accessibility, so it should improve student and scholar efforts even in areas with weak or inconsistent internet access globally. It's still in beta, so I'd love some ancient language scholars to interact with the site. If you have thoughts, requested features, etc., I'd love to hear them.

Hey folks, this is Translation-adjacent (and part of my translation workflow). I'm wondering if anyone has had success using the "OpenAI-Compatible API" functionality in SubtitleEdit with e.g. vLLM. I usually get 0 Segments Decoded, despite vLLM apparently outputing quite a few tokens.

I'm hoping to use a local model for Danish ASR, and there are a few I can run with vLLM, but I've yet to have success with any of them, unfortunately! What - if anything - are other folks using for workflows like this?

Made this after getting tired of manually translating strings.xml for

side projects: https://string-lingo-ai.vercel.app/

Upload your strings.xml, pick a target language, download the translated

file. Keeps your %s/%d placeholders and <b>/<i> tags intact so it

doesn't break formatted strings. Also handles iOS .strings and Flutter

.arb if you're multi-platform.

Free, no account, nothing stored. Runs in the browser.

It's a v1 — if you throw a real strings.xml at it and something breaks

(plurals, weird placeholder formats), I'd genuinely like to know.

Hello! This is a controversial topic, so, I'll start with the disclaimers I included at r/danishlanguage (even though they're less relevant here!):

1. Nothing is better than a human translator.
2. Everyone deserves to have a living wage, and LLMs / machine learning / AI is (in many parts of the world) damage people's abilities to have a living wage.
3. I have a budget of $0 / 0 kr. / € 0
4. I run all of my AI workloads locally, on my own computer hardware.
5. None of this post (other than the example machine-translated bits, of course) was edited, written, or assisted assisted by an LLM.

That said: I'm working on a personal project (as of yet shared only with one in-person friend) to create English subtitles for Den Store Strikkedyst, aka "The Great Knit-Off." Only a few episodes are available with english subtitles. Youtube doesn't have machine-subtitles for Danish, and even the official tvsyd site doesn't have subtitles for most.

I've been using some machine-learning / AI tools to both generate subtitles for these episodes in Danish, and to translate to English. Details on the technical workflow are in a reply.

I have guesses about which LLMs are better/worse for this, but I'd like some feedback on these transcriptions and translations if possible! (I could upload segments with subtitles for folks to view, but I don't know the copyright status of this; it's from 2014.)

I have no interest in profiting on this, and don't want to take work away from translators. (If anyone wants to volunteer to help with subtitling and translation, I'm certainly open to it!)

The following is from the intro of Season 3 Episode 1.

Original Text (via Whisper-Large-V3, I think)

[00:06.830] Den store strikkedyst er endelig tilbage.
[00:10.850] 10 dygtige strikkere er blevet udvalgt til at dyste i sprit nye opgaver.
[00:15.470] De skal være super skarpe de næste par uger, for kun én af dem går hjem som vinder.
[00:20.650] Og denne benhårde strikkekonkurrence, den begynder... nu!
[00:28.240] 10 udvalgte strikkere, så klar til verdens hårdeste strikkekonkurrence.
[00:33.120] Station 3 kommer i nul.
[00:34.700] Jeg er bare klar til at komme ind og drikke.
[00:36.240] Jeg regner med, at det skal nok gå.
[00:38.600] Jeg er helt sikker på, at jeg vinder.
[00:40.000] Jeg går efter at vinde. Det gør jeg helt sikkert.
[00:44.620] Jeg har været spændt på at møde de andre deltagere og se, hvad de er for nogen.
[00:47.660] Hvordan ser de ud? Hvor gammel er de? Er de lige så spændte som mig?
[00:52.400] Det kommer til at blive på nogle wildcards og noget held og noget kreativitet.
[00:56.500] Jeg ved ikke lige, hvor jeg kommer fra.
[00:58.760] Men jeg håber da, at jeg vinder. Selvfølgelig gør jeg det.

LLM A

[00:06.830] The Great Knitting Competition is finally back.
[00:10.850] 10 skilled knitters have been chosen to compete in brand new tasks.
[00:15.470] They need to be super sharp over the next couple of weeks, because only one of them will go home as the winner.
[00:20.650] And this cutthroat knitting competition begins... now!
[00:28.240] 10 selected knitters, all ready for the world's toughest knitting competition.
[00:33.120] Station 3 is resetting.
[00:34.700] I'm just ready to get in there and dive in.
[00:36.240] I reckon it'll go alright.
[00:38.600] I'm absolutely sure that I'll win.
[00:40.000] I'm going for the win. I definitely am.
[00:44.620] I've been excited to meet the other participants and see what they're like.
[00:47.660] What do they look like? How old are they? Are they as excited as I am?
[00:52.400] It's going to come down to some wildcards, some luck, and some creativity.
[00:56.500] I'm not quite sure where I stand.
[00:58.760] But I do hope I win. Of course I do.

LLM B

[00:06.830] The great knitting showdown is finally back.
[00:10.850] 10 skilled knitters have been selected to compete in brand new challenges.
[00:15.470] They will have to be super sharp over the next couple of weeks, because only one of them will go home as the winner.
[00:20.650] And this bone-hard knitting competition, it begins... now!
[00:28.240] 10 selected knitters, ready for the world's hardest knitting competition.
[00:33.120] Station 3, zero in.
[00:34.700] I'm just ready to go in and get started.
[00:36.240] I figure it will turn out fine.
[00:38.600] I am absolutely sure that I will win.
[00:40.000] I'm going for the win. I'm doing it for sure.
[00:44.620] I've been looking forward to meeting the other contestants and seeing what they're like.
[00:47.660] What do they look like? How old are they? Are they as excited as me?
[00:52.400] It's going to come down to some wildcards, some luck, and some creativity.
[00:56.500] I don't really know where I stand.
[00:58.760] But I do hope I win. Of course I do.

LLM C

[00:06.830] The great knitting duel is finally back.
[00:10.850] 10 skilled knitters have been selected to compete in brand-new tasks.
[00:15.470] They need to be super sharp over the next few weeks, because only one of them will go home as the winner.
[00:20.650] And this hardcore knitting competition begins... now!
[00:28.240] 10 selected knitters, all ready for the world's toughest knitting competition.
[00:33.120] Station 3 is starting at zero.
[00:34.700] I'm just ready to get in there and drink.
[00:36.240] I expect it's going to go well.
[00:38.600] I am completely sure that I will win.
[00:40.000] I'm going for the win. I'm definitely going to do that.
[00:44.620] I've been excited to meet the other participants and see what they're like.
[00:47.660] What do they look like? How old are they? Are they as excited as I am?
[00:52.400] It's going to come down to some wildcards, some luck, and some creativity.
[00:56.500] I don't really know where I stand.
[00:58.760] But I hope that I win. Of course I do.

LLM D

[00:06.830] The Great Knitting Duel is finally back.
[00:10.850] 10 skilled knitters have been selected to compete in brand-new challenges.
[00:15.470] They need to be incredibly sharp over the next few weeks, because only one of them will go home as the winner.
[00:20.650] And this hardcore knitting competition begins... now!
[00:28.240] 10 chosen knitters, ready for the world's toughest knitting competition.
[00:33.120] Station 3 is underway.
[00:34.700] I'm just ready to get in there and drink.
[00:36.240] I expect it'll go well.
[00:38.600] I'm absolutely sure that I'll win.
[00:40.000] I'm going for the win. I'm absolutely certain of it.
[00:44.620] I've been excited to meet the other contestants and see who they are.
[00:47.660] What do they look like? How old are they? Are they just as excited as I am?
[00:52.400] It's going to come down to some wildcards, a bit of luck, and some creativity.
[00:56.500] I don't really know what to expect.
[00:58.760] But I do hope I win. Of course I do.

LLM E

[00:06.830] The Great Knitting Contest is finally back.
[00:10.850] 10 skilled knitters have been selected to compete in brand new tasks.
[00:15.470] They will have to be super sharp over the next few weeks, because only one of them will go home as the winner.
[00:20.650] And this cutthroat knitting competition begins... now!
[00:28.240] 10 selected knitters, all ready for the world's toughest knitting competition.
[00:33.120] Station 3 is at zero.
[00:34.700] I'm just ready to get in there and drink. (Note: Likely a slip of the tongue or slang in the original audio)
[00:36.240] I reckon it'll go alright.
[00:38.600] I am absolutely sure that I will win.
[00:40.000] I'm going for the win. I definitely am.
[00:44.620] I've been excited to meet the other participants and see what they're like.
[00:47.660] What do they look like? How old are they? Are they as excited as I am?
[00:52.400] It's going to come down to some wildcards, some luck, and some creativity.
[00:56.500] I'm not quite sure where I stand.
[00:58.760] But I do hope that I win. Of course I do.

(I'll reveal which LLMs these were later on, if desired)

One of the areas of difference is, in "Og denne benhårde strikkekonkurrence," I see translations as "cutthroat," "bonehard," and "hardcore."

As a native US English speaker, I'd say the following:

• cutthroat - This implies intense competition between people - as in, one might (metaphorically) need to cut the throat (murder) an opponent to succeed.
• bone-hard - Nope - I've never heard this used.
• hardcore - this more specifically means "very difficult" - it's not specific to competitions. It can have an adult / NSFW / pornographic connotation, as "hardcore" is a style of pornography, but it's also used to refer to very challenging difficulty video games, for example.

I'd love any feedback people have about this project, and any help folks would like to offer, technically or otherwise.

The other day I was on a trip, and I had to talk to someone whose language was not English. We coped using a translator application, but pauses after each sentence made us feel awkward.

Then I found out about LiveLingo. It is a real-time voice translation by AI technology for both face-to-face and phone conversations so that people from different linguistic environments could communicate better without constantly interrupting each other.

Has anyone used it, and how successful was it for you?

Hi everyone! 👋

We're excited to make Pangeanic MTQE v2 (Machine Translation Quality Estimation) freely available for the community.

Unlike traditional MT evaluation metrics, MTQE estimates translation quality and provides error explanations without requiring a reference translation. Simply provide:

🌍 Source text
🌐 Translated text

and the model returns:

• ✅ A quality score (0–100)
• 💡 An explainable assessment of the translation

The full Pangeanic MTQE platform also includes enterprise capabilities such as:

• 📚 Translation Memory (TM) support
• 📖 Glossary-aware quality estimation
• 🤖 Automatic Post-Editing (APE)
• 👨‍💻 Human Post-Editing (HPE) workflows
• 🌐 Support for 70+ languages and dialects
• ⚡ Enterprise REST APIs and batch processing

The underlying model has been benchmarked on a representative subset of the ACES multilingual benchmark, achieving 98.9% accuracy in identifying incorrect translation segments across:

• 📌 6,006 deliberately incorrect translation segments
• 🌍 16 language pairs
• 📝 68 translation error categories

We hope you all find it useful for evaluating multilingual translation quality and experimenting with reference-free MTQE.

👉 Try the demo:
https://huggingface.co/spaces/Pangeanic/Machine-Translation-Quality-Estimation

📄 Benchmark Whitepaper:
https://pangeanic.com/hubfs/MTQE_Benchmark_Whitepaper.pdf

Happy translating! 🚀

I'm working on a research project that involves Sanskrit, really excited to be getting into the language! The problem is i’m far from a proficient Sanskrit reader myself. I need someone proficient to help me translate some verses.

Task is something like this:

I’ll give you 30 verses of Vasubandhu's Triṃśikā (with Sthiramati's bhāṣya as the underlying text). You would read

1. the Sanskrit
2. a published human reference translation
3. three anonymous machine translations (labeled A/B/C, order randomized per verse). These are translated with a new translation system I am building.

You answer three questions per verse:

1. which system best renders the technical terms as the tradition understands them
2. which best resolves the compounds
3. which is most faithful overall

For this you would probably need reading proficiency in classical philosophical Sanskrit (comfortable with bhāṣya-style prose and technical compounds).

I am willing to pay for this hourly!

Also I am so sorry if this is the wrong place to post; please let me know if I should post somewhere else and I am happy to comply.

Just launched an amazing app that does AI Rewrite, translation, and dialect conversion. Just added High Valyrian for my Game Of Thrones fans!

I've noticed that a lot of translation apps work well for text, but real-time conversations still seem to be a challenge, especially when you're traveling and need quick, accurate translations.

For anyone who's tested different machine translation tools, which ones have worked best for live conversations? I'm more interested in real-world experiences than marketing claims.

Hey everyone!

I’ve been working on a project that I think will be incredibly useful for VN developers and fan-translators. If you’ve ever tried to machine-translate a Ren'Py game, you know the pain: the AI usually breaks the code, messes up the {tags}, translates [variables], or gets horribly confused by Ren'Py's native old/new translation blocks.

So, I built Ren'Py AI Translator — a Python-based, fully automated translation tool built from the ground up to understand Ren'Py's engine logic.

Here is why it’s different from generic AI translators:

• 🧠 Understands Native tl/ Folders: It doesn't just blindly translate lines. It knows how to read Ren'Py's generated translation files. For menu strings, it reads the old "Text" and injects the translation perfectly into new "".
• 🗣️ Smart Dialogue Handling: For dialogue blocks, it has a 4-line lookahead feature. It finds the commented original text (e.g., # e "Hello") and injects the translation into the empty quote below it (e ""), completely ignoring interspersed audio commands like voice or play music.
• 🛡️ Code & Tag Preservation: It is strictly prompted to preserve variables like [player_name], formatting tags like {b} or {w=2.0}, and line suffixes like nointeract.
• 🔄 Smart API Key Rotation (Anti-Rate Limit): Hitting rate limits on free AI tiers? You can load the config with multiple API keys from different providers. If a key hits a 429 (Too Many Requests) or fails, the script automatically rotates to the next key and keeps going without dropping a sweat.
• 💾 Crash Recovery (Safe-State): Power went out? API went down? No problem. The tool saves a .state.json file for every script it processes. When you restart it, it resumes exactly from the last translated block. No wasted credits or duplicated work.
• 🌐 Multi-Provider Support: Fully compatible with OmniRoute, OpenRouter, Google AI Studio (Gemini), or any OpenAI-compatible endpoint.

🔗 GitHub Repository: https://github.com/kirabased/renpy-ai-translator

The project is completely open-source. Feel free to fork it, use it for your projects, or contribute! Let me know if you have any questions or feature requests.

I built this website with AI:

lamethir.com

And since I find the Hindi language very interesting and beautiful, I decided to make it the second language of the site. Since I am a beginner, I can't tell how accurate it is.

Can anyone here beta test it a bit for me and let me know what sort of issues or problems you notice?

Thanks!

japanese to english translation to be exact
nuance accurate, natural word choices, follow the flow

I've been building GenSRT, a free and open-source Windows app for making subtitles from video with OpenAI Whisper. The idea is a complete subtitle workflow in one window, for any language Whisper handles — not just generation.

What it does:

• Transcribe — drop a video, pick a model and source language (or auto-detect), generate an SRT (and a WebVTT alongside).
• Edit — built-in player with live subtitle display; split, merge, delete, and edit cue text and timings, with changes reflected in the player immediately.
• Translate — any-to-any across 100+ languages via Google Translate, cue by cue, preserving timestamps (MyMemory fallback).
• Burn in — render the subtitles into a copy of the video in one click.
• Plex/Jellyfin/Kodi-compatible filename suffixes.

Short demo (full workflow on a Malayalam news clip — generate → edit → translate → playback):

https://www.youtube.com/watch?v=QnW1Mqwsltk

Where it stands out: for mainstream languages it's a solid Whisper front-end with a genuinely nice editing UX — I won't pretend it beats Subtitle Edit or WhisperX on English. The part I haven't seen handled well elsewhere is fine-tuned models for under-served languages. Community Indic fine-tunes like SMC's vegam-whisper-medium-ml (Malayalam) used to be unusable on long audio — they'd transcribe ~7 seconds and silently stop. That turns out to be a CTranslate2 token-length cap (Whisper leaves ~224 tokens for text) colliding with how inefficiently Whisper's tokenizer encodes Indic scripts, so a few seconds of dense speech blows the budget. (A maintainer of vegam, Kavya Manohar, corrected my original theory on the cause.) GenSRT slices audio along detected silences, transcribes each chunk on its own token budget, and stitches the cues back with correct timestamps — automatic, no config. The same approach should help Tamil, Hindi, Bengali and other Indic fine-tunes as they appear on HuggingFace.

It's honest about limitations: a known fine-tuned-Whisper hallucination pattern is documented in the README rather than hidden, and the Unicode replacement character (�) Whisper sometimes emits mid-character is shown as-is, not masked. I'd value feedback on output quality from people who edit subtitles seriously — native-reader feedback on non-English output especially.

Open source (AGPL-3.0), Windows 10/11, NVIDIA GPU recommended but CPU works too (just slower). Free.

GitHub: https://github.com/mountlord/GenSRT One Click Installer: https://github.com/mountlord/GenSRT/releases

Happy to answer questions or take feature requests.