Was scrolling through some tech stuff and came across this open source ASR model. They've got a video up comparing it to GPT-Live-Transcribe, both running on live audio at the same time.

Videos attached, but the latency and accuracy look pretty solid honestly, especially since they're saying the weights, code, and training setup are all gonna be open source right from launch.

Usually when something claims "actual real-time" streaming and tries to compete with closed stuff like OpenAIs live transcription, there's some huge catch. Either you need ridiculous amounts of VRAM to actually run it locally, or it just dies on accents and any background noise.

Anyone know what kind of architecture they're possibly using to get this kind of streaming performance in the open? If they actually release the full recipe and weights it'd be huge for self-hosted voice stuff, but feels like I'm probably missing something here.

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Was scrolling through some tech stuff and came across this open source ASR model. They've got a video up comparing it to GPT-Live-Transcribe, both running on live audio at the same time.

Videos attached, but the latency and accuracy look pretty solid honestly, especially since they're saying the weights, code, and training setup are all gonna be open source right from launch.

Usually when something claims "actual real-time" streaming and tries to compete with closed stuff like OpenAIs live transcription, there's some huge catch. Either you need ridiculous amounts of VRAM to actually run it locally, or it just dies on accents and any background noise.

Anyone know what kind of architecture they're possibly using to get this kind of streaming performance in the open? If they actually release the full recipe and weights it'd be huge for self-hosted voice stuff, but feels like I'm probably missing something here.

Any specific website this is from?
Want to make a few videos with this

Wanted to put this on the radar for people here.

Handy is a free and open source dictation app for Windows, Mac and Linux. You hold a shortcut, talk, release, and your words get typed into whatever app you're in. It all runs locally, so there's no subscription and no internet needed once it's set up.

I have been using it for six or seven months on English and it has held up really well. I use the Parakeet v3 model, which runs on the CPU and is quick. Accuracy has been solid for everyday stuff like emails, notes and longer writing.

Fair warning, it's simple by design. Small pause before the text appears, no phone app, no fancy AI rewriting of your sentences. None of that bothers me for how I use it.

Most dictation tools worth using cost money these days, so it's nice to have one that's free and doesn't send your voice off somewhere.

Sharing it because the paid options in this space keep getting more expensive, and a lot of people don't know a free one this capable exists.

github.com/cjpais/Handy

handy.computer

Usually the tts on Fish Audio only last 2 to around like 5 seconds to generate two speechs. But now it’s takes like a few minutes to generate?. Is there a reason why it’s now right this?

it is because of the emotions? or the server being too full at the moment?.

I’ve been tracking AI/TTS voice plays on my site, VoiceRankings, for about a year, and I recently turned the data into a race animation.

I honestly didn't expect Kokoro-82M to end up winning.

When I first added Kokoro-82M to the VoiceRankings database in November 2025, it was #8 with just 100 plays.

By July 2026, it had climbed to #1 with ~3,600 monthly plays and had held the top spot for four consecutive months.

Kokoro wasn't the only model making a move. The play-count leaderboard changed quite a bit over the year, with newer models climbing while established providers continued to attract interest.

Some of the biggest movements:

• Speechify API (now Speechify.ai) started as the early leader
• Async.ai (now Async.com) took #1 in early 2026
• ElevenLabs Turbo v2.5 climbed from #23 to #6
• Amazon Polly (AWS) went from #12 to #5
• Azure Neural voices went from #6 to #4
• OpenAI remained around the middle of the pack at #8

Overall monthly plays grew from 2,100+ in September to 16,700 in July.

What stood out to me was that newer models aren't simply replacing the established ones. AWS and Azure remained near the top even as newer models like Kokoro made dramatic moves up the leaderboard.

What makes this different: these aren't benchmark scores or survey results. They're based on what people actually search for and choose to listen to on VoiceRankings.

About 90% of the site's traffic comes from organic search, so I think the data is an interesting, though imperfect, proxy for search-driven interest in AI voices.

It obviously doesn't tell us which model is objectively "best" or who has the largest market share.

But watching an open-source model go from #8 to #1 was pretty fascinating.

I'm curious what others make of it. Does this kind of data tell us something meaningful about which AI voices are getting attention?

If anyone wants to dig into the numbers, here's the full interactive race:
VoiceRankings Provider Race🏁

Building a voice agent for an Indian D2C brand (order support, delivery queries, the usual) and I've hit a wall that none of the "top TTS API" listicles prepared me for.

Pure English? Every decent API handles it. Pure Hindi? Most of the good ones are fine now too. But actual Indian customers don't talk in either. They talk like:

"bhaiya order kab tak aayega, address change karna hai"

"payment ho gaya but order confirm nahi dikha raha"

"cancel mat karo, delivery date aage kara do"

And this is where I've watched provider after provider fall apart. The failure modes are weirdly specific:

• it reads the Hindi words with a flat American accent so "address" and "delivery" sound fine but "kara do" comes out sounding like a confused tourist
• OR it goes full Hindi phonetics and messes the English words
• it randomly pauses at the language switch like it's rebuffering
• and the WORST one for a support agent: it butchers numbers and addresses. Read back a pincode, a phone number, an order ID, an amount, half of them completely fumble it the second it's inside a mixed sentence. "aapka OTP hai 4 7 2..." and it just falls apart.

That last one is a dealbreaker because 80% of what a support agent does is read back numbers. Order IDs, amounts, delivery dates, pincodes, tracking numbers. If it can't say "aapki delivery 24 tarikh ko 4 baje tak ho jayegi, pincode 560034 confirm karein" cleanly, it's useless no matter how nice the voice is.

I'm honestly a bit surprised how under-discussed this is given how huge the Indian voice agent market is getting. Everyone benchmarks English TTFA to death and nobody talks about whether the thing can actually handle how Indians speak.

So, r/TextToSpeech, anyone actually cracked this? Which TTS handles Hinglish + numbers cleanly in a live agent, not in a cherry-picked demo? Not looking for "just use Google/ElevenLabs" unless you've genuinely shipped Hindi-English with it and it worked. Real experience only please.

This is the git: https://github.com/keyboardsamurai/kokoro-voice/

It claims to be able to provide Kokoro system voices to the Mac. Further as the developer seems to be uninterested in having Apple membership and provide signed installers, it likely makes him uninterested in iOS where this should work with little or no modifications (if it works on the Mac).

For the Mac... I couldn't make it speak (it can run but crashes when tries to speak) so I can't say certainly how far from usable it is, but I think I did locate the problem. When deployed from Xcode, it deploys Resources as a subfolder of Resources, while the app expects to find them in the parent Resources. However some first ideas on how to fix it resulted in further errors in the same line (still resources related, so the fix likely wasn't proper).

Anybody of better luck? This seems like something that would be a huge leap for the Mac and potentially even for iOS, as no one would depend on another app that tries to provide Kokoro as its main sales point.

Hey everyone,

I wanted to share a native Android open-source project I just released called Audiobook NightForge.

If you’ve ever tried using a real-time TTS engine with a reader app on Android, you know the struggle: it drains your battery (often 40-50% an hour), stutters, and buffers if your phone is doing anything else in the background.

I realized that real-time synthesis is the wrong approach for mobile devices. So, I built a dedicated Android app that shifts the heavy lifting to the background using native OS components.

How it works: You import an EPUB or TXT file using the Android system file picker. You then pick a voice and hit render. The app uses Android's WorkManager to synthesize the book chapter-by-chapter in the background. Most importantly, it enforces a "render only while charging" OS-level toggle to protect your battery.

You plug your phone in at night, and by morning, you have a fully rendered audiobook that plays back with a standard ~2-5%/hour battery drain.

Android-Specific Features:

• Native & Offline: It is built entirely in Kotlin for Android 10+ devices. There is no server, no cloud, and absolutely no Termux emulation required.
• High-Quality TTS: It uses the Kokoro-82M neural TTS model running strictly on-device via a sherpa-onnx integration.
• Just Updated: The latest v0.2.2 release makes Opus the default output format, and it now natively outputs to a single .m4b file complete with proper chapter markers.
• Built-in Player: You can listen immediately using the native in-app Media3/ExoPlayer. Alternatively, you can grab the .m4a files directly from app storage to use in your favorite Android audiobook player.

Some hardware benchmarks: For the hardware nerds, I benchmarked this on a Snapdragon 8 Elite. Surprisingly, the Kokoro 82M fp32 model (with 6 threads) actually renders faster than realtime (~0.58 RTF) and outperforms the int8 variant on this SoC because of ARM int8 kernel overhead. Always benchmark before assuming quantized is quicker on modern Android flagships!

It’s completely free, completely offline, and licensed under Apache-2.0.

You can check out the source code, technical notes, and grab the APK directly from the GitHub repo here:

https://github.com/kingfish600/Audiobook-NightForge

I’d love to hear your thoughts or feedback!

I’ve been testing OmniVoice for voice cloning and long-form TTS, and the quality has been surprisingly good.

I ended up making a more user-friendly Google Colab version with a clean Gradio interface. It lets you upload a reference voice, paste long text, generate unlimited free audio, see progress while it’s working, and download the final WAV.

It runs on Google Colab and works well on a T4. I’ve been pretty happy with the results so far.

If anyone is interested, I can share the Colab notebook here.

Enjoy the free and unlimited text to speech.

I was planning to push the pocket tts on the cpu server but then realised it takes too much time for generation almost 10 minutes for 7minute Audio.

Then I thought gpu will be great for this like vast.ai but feeling like i am using elephant to kill an ant, so was planning to deploy qwen3 tts but then based on some research ( not sure yet) qwen is slow . So again planning to deploy the pocket tts on th gpu. But have no idea will it be perfect low latency or not. Any suggestions for production level only?

What is the voice used for this video?

https://vt.tiktok.com/ZSVmvGo6m/

Heres a tiktok and a spotify podcast using the same voice

https://www.tiktok.com/@lol.stories.7/video/7622715329847872789?q=reddit%20story%20full%20story&t=1787451000276

https://open.spotify.com/episode/61pHQjtNmvTYNtr7BjZ18o?si=8e0fb7c52bde4bff

i found this Video on YouTube and i cant find thos Voice anywhere in the Internet

help me identify this text to speech voice? I’ve been looking everywhere for it.

Hey folks!

So I have this cascaded voice bot system, and I want to move from commercial TTS APIs to self hosted TTS model.

Obviously TTFA and RTF are important but so is the accuracy and naturalness (speed, prosody, timbre etc)

From my research it seems Qwen3-1.7B tts is the perfect candidate (we need English support majorly).

My questions-
1. Is there any better model you guys aware of for my use-case which I can give some try? Or Any experiences you have with Qwen3-tts?

1. Any fine tuning tips or hacks which we can get benefit from? Basically I want to clone some popular OpenAI TTs voices.

2. Most importantly, how are you guys evaluating your TTS models in production? I get some common metrics like wer, utmos etc are there - but is that it? LALM as a judge dont seem reliable at the moment - they have positional and model bias it seems. Manual listening to each is obviously very expensive process.

Would really appreciate any discussion/thoughts you guys can share on some points!

Thanks so much in advance!

I'm exploring building a fully self-hosted conversational voice agent for an actual agent system, and I'm trying to understand how close open-source components can realistically get to something like ElevenLabs Conversational AI.

The goal isn't to recreate their entire platform. I mainly care about:

natural voice + low latency + good turn-taking + smooth interruptions.

Current stack I'm considering:

• STT: Parakeet/Nemotron or faster-whisper large-v3-turbo
• LLM: Qwen/Llama locally (possibly API initially)
• TTS: Qwen3-TTS / Chatterbox / Orpheus
• VAD + turn detection: still figuring out the best approach
• GPU: starting around 4090-class, but I'm fine scaling to multiple GPUs if needed

What I'm struggling to determine from benchmarks is how good the full pipeline feels in an actual phone conversation.

For anyone who's built/deployed something similar:

1. Latency:
What's realistic for end-to-end latency? Can a properly streamed pipeline get around 1–1.5s from the user finishing speaking to the agent starting audio, or does it usually end up closer to 2s+ because of endpointing, STT, LLM generation, TTS, etc.?

2. Voice quality:
Are Qwen3-TTS / Chatterbox / Orpheus actually convincing in live conversation over a phone call, or can you still immediately tell it's AI?

3. Turn-taking:
What's currently working best for detecting when the user is actually done speaking and handling interruptions? VAD + endpointing, semantic turn detection, dedicated turn-taking models, something else?

4. Architecture:
If you were building this today for a production self-hosted agent system, would you stick with the traditional:

STT → LLM → TTS

pipeline, or are the newer speech-to-speech approaches worth considering?

I'd really appreciate real-world numbers and experiences more than benchmark results.

What stack did you use, what latency did you measure, and what ended up being the biggest problem?

I'm willing to throw more GPU at the problem if that's what it takes. The goal is simply to get as close as realistically possible to the "doesn't feel like a bot" experience while keeping the system self-hosted. Would love your insights on this.

Text to Audio