Launching today
Cutrix
AI-powered video translation that preserves speaker's tone
822 followers
AI-powered video translation that preserves speaker's tone
822 followers
Stop settling for robotic dubbing. Cutrix uses an Agentic workflow to translate videos while preserving the speaker's original emotion and natural pacing. Experience hyper-natural alignment without the steep learning curve. Sign up for free credits today!
the intonation-mapping approach is interesting because it runs into a real wall with tonal languages. in Mandarin or Vietnamese, pitch contour isn't just emotional color, it's literally what word you're saying, the same syllable means something completely different depending on tone. if you map the source language's emotional pitch pattern onto a tonal target language, you risk fighting the actual linguistic tone system. is that something the timing/prosody agent accounts for, or is tonal-language output more of a known limitation right now
@galdayan sharp observation. you clearly know your stuff with tts! thank you for raising such a great technical point.
you're completely right about the pitch contour issue. to avoid forcing source pitch onto tonal languages, we don't just do linear acoustic mapping. instead, our engine uses multimodal understanding, analyzing audio emotion, text context, and video cues simultaneously. it tries to find the best balance to inject emotional intensity, like energy and pacing, while respecting the native tone rules.
it handles most cases well right now, but to be totally transparent, it's a really tough problem and we still have edge cases where the linguistic constraints fight back. we are actively optimizing this.
really appreciate the deep dive. ❤️
@tristan_huang the honesty about it being a hard, unsolved edge case is what makes this a good answer, most teams would've just said "we handle that" and moved on. multimodal cues (text context, video) as tiebreakers for emotional intensity is a smart way to sidestep the worst of it even if it's not a full solve. good luck with the tonal-language edge cases
@galdayan really appreciate that. it's a tough but fun problem to crack, and having users who understand the technical nuance makes it worth it! 😊
finally something that doesnt make every dub sound like a bored robot, the pacing on my spanish clip actually felt close to the original
@fahribryam5g4c this just made our day! killing the 'bored robot' vibe was exactly why we built Cutrix. the natural pacing you noticed in your Spanish clip is actually our timing agent working under the hood to map the exact timeline of the original audio. thrilled to hear it nailed the sync for you. 😊
Tried it on a Spanish interview clip and the pacing actually matched the original speaker's pauses, which is something most tools botch. Genuinely impressed by how natural it sounded.
@sedefs19749 nailed it😊. getting those micro pauses right is exactly what makes or breaks a natural conversation. most tools rush through the timeline, so we took extra care to finetune the alignment to prevent that. thrilled you noticed the difference. thanks for testing it out.❤️
Curious how it handles multiple speakers talking over each other or code-switching mid-sentence, does the emotional alignment still hold up when the audio gets messy like that?
@izannaciye99779 In scenarios involving rapid and frequent speaker switching, such as dialogues, distinguishing between different speakers is extremely difficult. Traditional methods, based on speech recognition, often have limited accuracy. We've chosen a different approach, considering both visual and auditory elements, much like distinguishing different speakers when watching a video. This seems to offer some help in such scenarios，our translation is also a context-aware translation, unlike traditional machine translation, our translations can better understand the context and the speaker's intention. However, AI translation cannot guarantee accuracy all the time; it only provides relatively accurate translations in most cases.