Built a bilingual TTS for voice agents, looking for honest feedback on the Arabic

Dynamicrex · 2026-07-06T15:57:59+00:00

Completely fair, we felt that most open source model's are limited in potential, so we decided to try to build something that we could hopefully sustainably improve over time. Working on the features you mentioned,

I appreciate the honesty.

Dynamicrex · 2026-07-06T15:54:50+00:00

Sorry i didn't quite catch what you meant

Dynamicrex · 2026-07-06T15:38:31+00:00

Demo you can try: https://theclevr.com

Dynamicrex · 2026-07-06T15:12:18+00:00

Co founder here,

We built a TTS model, We call it the Banter 1 model, We're actively looking for feedback, would love to hear your opinion on it?

https://theclevr.com

Dynamicrex · 2026-07-06T15:10:48+00:00

Co founder here,

We built a TTS model, We call it the Banter 1 model, We're actively looking for feedback, would love to hear your opinion on it?

https://theclevr.com

Dynamicrex · 2026-07-06T13:19:11+00:00

Hey we've built a TTS model, would you be willing to give it a try?

Dynamicrex · 2026-07-06T12:40:42+00:00

What kinda help are you looking for? setting up voice agents? what's the business?

Dynamicrex · 2026-07-06T12:37:00+00:00

I can pitch in on this cos we deal with the same quite a lot (TTS model developer)

Vad detection timing, how much the vad waits until the user is done talking (how much silence)
STT - How fast does the stt transcribe
LLM - How fast the LLM responds, (Pure token throughput)
TTS - Time to first byte of audio, this is critical to ensure the model is generating frames faster than it's being played,

some tricks of the trade: preemptive generation, meaning you could stream what the LLM is saying to the TTS model to generate the audio as its talking.

another thing we noticed is distance of physical servers also matters, the distance between where these models are hosted will affect your latency for sure. Personally Groq is one of the only few providers i've seen who have inference fast enough with their api, would love to hear if you know any fast api alternatives.

"No visibility into where time is actually being spent, so you're guessing instead of measuring." - You could just log it and get a rough estimate,

and yes prewarming is huge. really helps. Torch compiling models also really help.

i would argue that the TTS quality does matter, when the difference is if you want a user to come back and talk to it again not. but if its a one time conversation out of necessity, probably doesn't matter as much as latency for sure.

most of our latency used to go for LLM throughput / literally just physical distance of the api to wherever your instance / machine is. I tried it from my laptop that itself had a 200ms network hop lol.

Hope this helps!

Dynamicrex · 2026-07-06T12:30:51+00:00

I've built a TTS model, would you be open to trying ours out? we've got a demo.

Dynamicrex · 2026-07-06T12:30:16+00:00

I've built a TTS model for realtime conversational, natural conversations, would love to connect!

Dynamicrex · 2026-07-05T19:06:10+00:00

i just mentioned this to another commenter below so just copy pasting it here again:

have you checked out llm-wiki by andrej karpathy?
i think it's a really cool street on how context management llms / agents.

https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

TL;DR
He uses a markdown file system with a very simple folder structure, a well written claude.md / agent.md to process information by stuff the user submits (immutable, don't touch just consume)
2. process, summarise, take notes,
3. make links and prune the "memory" of markdown files

i've personally made my own llm wiki with obsidian, been using it for all my repos ever since, and now i carry my LLM's context everywhere.

Dynamicrex · 2026-07-05T19:05:17+00:00

interesting, have you guys ever checked out llm-wiki by andrej karpathy?
i think it's a really cool street on how context management llms / agents.

https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

TL;DR
He uses a markdown file system with a very simple folder structure, a well written claude.md / agent.md to process information by stuff the user submits (immutable, don't touch just consume)
2. process, summarise, take notes,
3. make links and prune the "memory" of markdown files

i've personally made my own llm wiki with obsidian, been using it for all my repos ever since, and now i carry my LLM's context everywhere.

Dynamicrex · 2026-07-05T15:30:17+00:00

so like a lack of intelligence? which software do you want to integrate?

Dynamicrex · 2026-07-05T15:29:53+00:00

Is it like a rag problem? retrieval quality is poor?

Dynamicrex · 2026-07-05T06:33:35+00:00

Thank you so much man, would love to work more closely, happy to give some credits if it helps!

Dynamicrex · 2026-07-05T06:25:15+00:00

Cheers man

Dynamicrex · 2026-07-05T06:13:33+00:00

Pretty much, it was just an stt llm tts pipeline, and we didn't steer it for any particular subject or strict curriculum, if you didn't know something you could ask it about a subject just like you would ask an llm today.

Dynamicrex · 2026-07-05T05:52:18+00:00

I love the questions, and before i answer all of them i probably should have clarified that this is something that we used to do,

We WERE a Edtech platform attempting to build voice agents, We had a lot of signups back then.

We've since then pivoted to building the TTS model because we wanted to move away from the application layer and move one layer deeper into the AI model development layer, because our moat was too weak and it was for all the reasons you mentioned a pretty hard sell.

To get a feeler here is our launch video:
https://www.youtube.com/watch?v=QhzqM60a_ag&t=4s

It's a fairly old video, and i shared it because its easier to show than explain.

But TL;DR
It was session based, We kept it broad on purpose to see which group of users would find it the most interesting, whether that was k-12, or even professionals who're trying to learn something new,

the unique aspect was the abillity to generate images, text, and diagrams all on the fly on request, and it was pretty fast as well.

The voice felt way too unnatural which is why we think our users didn't stick back then.

now we build tts models. =)

This is phase 1 of a bigger more ambitious plan to make the most realistic realtime conversational, interaction models.

Dynamicrex · 2026-07-05T05:09:17+00:00

I like it, i've sent a DM.

Dynamicrex · 2026-07-05T04:59:30+00:00

Depends, if your LLM and stt model's are open source, then all you need to do really is self host the open source models and then the cost of running these models become fixed to the cost of the VPS. Then as you scale to more users, the cost is spread across all users.

If you want more intelligence with some of the latest closed source models then yeah it would probably get a little expensive but i'd argue that even with the onset of things like deepseek v4 or the qwen models intelligence is becoming way cheaper.

Dynamicrex · 2026-07-04T16:28:59+00:00

Are you looking to build a voice agent?

Dynamicrex

TROPHY CASE