I have used Kokoro fairly extensively for an accessibility product. I have loved working with it (especially because I don't have an NVidia GPU like many TTS of similar quality require).
I particularly appreciate the fact that it lets you manually add IPA pronunciation guides. There have been some cases where an important word is a homograph and Kokoro assumed the wrong pronunciation.
The place where it falls a little short is in saying just a single word or two. Try having it say simply "six" and it almost always says something like "ah-six-ah". I found a way around that though. If you give it a longer sentence to say (eg "The word is: six") it will say it fine. The trick is that the Kokoro API gives you the timestamp of each word in the sentence. So you can have a Python script crop out just the word you care about. The intonation is a little flat this way, but is very reliable.
I asked about this on the discord, and was told that it is a limitation of the small parameter size. But in fairness to Kokoro, even eleven-labs' voices suffer from this occasionally.
Unfortunately it makes it unsuited for my use case, which is almost entirely single words, as I don't particularly want to deal with stitching/segmenting input/output.
Love this model. I’m GPU poor and have had FOMO that I haven’t played with local models at all. About a month ago I setup Kokoro on my GTX1650 to do TTS for an article reader. A simple WebUI lets me paste a URL or a chunk of copy pasted text. Python cleans it up and sends to Kokoro for TTS and it’s then served via RSS for Apple Podcasts. Then for my morning drive I’ll catch up on articles or blog posts I’ve gathered.
At some point I’d like to play with separate voices and see if I could build something like NotebookLM for kind of like a radio morning show of news items I’ve gathered.
I used to keep a version of whisperx around, because I think it's important to have not just transcription, but also timing and speaker identification (e.g. for subtitles)... It depends on pyannote, though, which has some wierd licensing (and is tougher to script the installs because of it), so I wanted to look at something that both had better transcription, and supported diarization (the speaker and timing). I decided on parakeet for the transcription with softformer (the diarization), but most of the available engines for it don't include softformer.
I coded up an OpenAI compatible server for parakeet-rs ( https://github.com/altunenes/parakeet-rs ) (which does support softformer) and I've been using it with OpenWhispr (a desktop app for transcription that handles all sorts of neat thing).
I'm doing CPU-only transcription (because I use my GPUs for other stuff and haven't gotten around to adding in the GPU-path), but it's incredibly empowering to be able to have local transcriptions at will.
A couple months back I wrote a chrome extension that does this on any webpage, with simultaneous highlighting of the sentence being read. Skips both the container launching step and the copy pasting website contents step. Might be useful to anyone trying to use kokoro ergonomically.
It's self-improving over time, runs on your local machine, and is generally decent software. 60% of my interaction with my PC nowadays is pure voice input.
Technically Voiceio also does TTS, but it's really crappy and just meant to read stuff loud / select a lot of text and listen to it podcast style whilst I'm e.g. washing the dishes.
However you're totally right that it's focused on STT. I probably use it 95% for STT and only occasionally for TTS (which also reflects itself in the amount of polish I put into each)
I'm using Kokoro for a fun little side-project browser-based game I'm working on. It's legitimately super good for being only 85mb (for the wasm version) or 300mb (for the webgpu version).
I built a pipeline through hermes using edge-tts to automate and listen to links that I provide to it just this morning, google notebooklm style. I replaced the TTS model with Kokoro after seeing this post, thank you. Here's the pipeline if anyone is interested. https://www.klaweht.com/2026-07-07-link-to-podcast-rss-pipel... By the way, it took hermes just around 10-15 minutes to build first iteration. I am impressed.
Kokoro is great. I built an mcp for it a while back that has gotten decent traction - https://github.com/mberg/kokoro-tts-mcp if anyone wants to go that route
this is very cool! i also made a kokoro based tts tool which runs on a jetson orin kit. it serves tts generations over durable streams, try it out here: https://streamtts.dev/ , i also wrote about it: https://s2.dev/blog/local-ai
I've found that for CPU inference the PyTorch-based (non-quantized) version of Pocket TTS actually performs (both speed and quality-wise) better than the ONNX version, even after fiddling with all of the knobs that ONNX provides.
Love Kokoro tts. I wrote https://github.com/Jud/kokoro-coreml to try pushing the limits a bit on speed & size. Such great quality at a given size. As others have mentioned short utterances are problematic, but solvable.
Saw it first on reddit, and later I created a small project to generate audio books from epub. So far I've listened to couple of books generated this way and am quite satisfied with the quality. There is just one particular word I remember that it pronounced wrongly - "Malay".
Both Text-to-Speech and Speech-to-Text now have local models that are good enough to get the job done. Kokoro for TTS, Parakeet for STT and Fluid-1 for text formatting (I use it with FluidVoice). I hope this is a trend that continues for other applications.
Naive question, but I once downloaded a particular voice file that I wanted to use with some other RVC TTS project, but ended up not being able to run it CPU only, so I only kept the voice I wanted.
Thing is, the voice is in .pth format, and on Kokoro's huggingface page, their voices are all .pt.
Would I be able to use this voice I already have with Kokoro? If not, is there any way to convert it?
I could always go looking if someone made this specific voice but in .pt format, but I barely mess with AI and don't know how I could search for this.
Who is going to hack together a mac widget that allows us to select text anywhere, press a shortcut key and finally get a non robotic voice outputted in a reasonable amount of time?
I am aware of the Option + Esc shortcut on osx for the onboard TTS but wow is it hard to listen to in 2026.
In System Settings, if you go to Accessibility and click "Read & Speak" in the "Vision" section, you can select a different voice using the "System voice" section. Click the "(i)" to preview your various options and even download more. Some, like "Allison (Enhanced)," sound leagues better than the default voice.
Great point! these are better than Samantha and they're free. But still, if I could wait a few seconds to get a much richer TTS experience I'd pay for that.
Easy to send one’s clipboard to Microsoft Azure and have their DragonHD voices read the text, say with Keyboard Maestro (or presumably Alfred, Raycast, etc.). Should work with selected text too.
You’d definitely get to pay for it, not what I consider cheap. (“$15 per 1M characters”) But IMO just about best-in-class (maybe ElevenLabs has a voice I’d like even better).
It's interesting that the male voices are all so much worse than the female voices (several are quite good). There is bias in machine learning, but I wonder whether there is also systematically more training data of female speech?
kokoro is surprisingly great at nuance but it's tough to improve that last ~2% or so. kokoro + rvc is really great too; i use that for ELEMENT47, the LLM-centric comedy podcast i do that i wish more people would listen to. (e47.net , feel free to subscribe!)
Another endorsement - I used Kokoro pretty extensively with an app I was developing over the last year and it's been excellent, both on- and off- GPU. Even with Elevenlabs (long time subscriber) the comparative quality of Kokoro keeps up really well until you get to their larger models with their professional voices.
I do wish there were better support for SSML, as well as deeper documentation of how to influence inflection in-line, but the default does well with standard emphasis (e.g. putting asterisks around text elements). Both asks are getting outside the zone of reasonable asks for this sort of distribution, though, and I remain incredibly grateful for the quality of what hexgrad and nazdridoy have put out in the world.
I just hooked it up to my personal AI Japanese Teacher app, pretty good quality / natural sounding speech in mixed English / Japanese while running fast on CPU so I don't waste VRAM.
I particularly appreciate the fact that it lets you manually add IPA pronunciation guides. There have been some cases where an important word is a homograph and Kokoro assumed the wrong pronunciation.
The place where it falls a little short is in saying just a single word or two. Try having it say simply "six" and it almost always says something like "ah-six-ah". I found a way around that though. If you give it a longer sentence to say (eg "The word is: six") it will say it fine. The trick is that the Kokoro API gives you the timestamp of each word in the sentence. So you can have a Python script crop out just the word you care about. The intonation is a little flat this way, but is very reliable.
I asked about this on the discord, and was told that it is a limitation of the small parameter size. But in fairness to Kokoro, even eleven-labs' voices suffer from this occasionally.
Unfortunately it makes it unsuited for my use case, which is almost entirely single words, as I don't particularly want to deal with stitching/segmenting input/output.
At some point I’d like to play with separate voices and see if I could build something like NotebookLM for kind of like a radio morning show of news items I’ve gathered.
https://github.com/lfnovo/open-notebook
I used to keep a version of whisperx around, because I think it's important to have not just transcription, but also timing and speaker identification (e.g. for subtitles)... It depends on pyannote, though, which has some wierd licensing (and is tougher to script the installs because of it), so I wanted to look at something that both had better transcription, and supported diarization (the speaker and timing). I decided on parakeet for the transcription with softformer (the diarization), but most of the available engines for it don't include softformer.
I coded up an OpenAI compatible server for parakeet-rs ( https://github.com/altunenes/parakeet-rs ) (which does support softformer) and I've been using it with OpenWhispr (a desktop app for transcription that handles all sorts of neat thing).
I'm doing CPU-only transcription (because I use my GPUs for other stuff and haven't gotten around to adding in the GPU-path), but it's incredibly empowering to be able to have local transcriptions at will.
For what you are doing, Senko works really well for diarization along with parakeet.
Faster and more accurate than Pyannote and whisper on my MacBook anyway.
https://chromewebstore.google.com/detail/local-reader-ai-on-...
Kokoro is a really good model, considered it’s released 1.5 years ago. It’s punching above its weight https://5uck1ess.github.io/tts-bench/scores.html
I've been using my own solution since January. I'm on Linux, and can't use Aqua, Whipsrflow etc... So i made my own.
Recently cleaned it up and made it install friendly.
If anyone is interested, you can check it out here: https://github.com/Hugo0/voiceio
It's self-improving over time, runs on your local machine, and is generally decent software. 60% of my interaction with my PC nowadays is pure voice input.
However you're totally right that it's focused on STT. I probably use it 95% for STT and only occasionally for TTS (which also reflects itself in the amount of polish I put into each)
and thanks!
Quality is very close.
Will vary in your setup, but here is my script: https://github.com/DavidVentura/translator-rs/blob/master/sc...
similar to openclaw
Article refers to: https://huggingface.co/hexgrad/Kokoro-82M
Caught my eye for the related name to my book of Kakuro puzzles for sale at https://www.kakurokokoro.com
Kokoro comes from the Japanese word meaning something like heart or spirit, and not the literal ones.
the onnx version of pocket-tts does perform better. https://huggingface.co/KevinAHM/pocket-tts-onnx
Would I be able to use this voice I already have with Kokoro? If not, is there any way to convert it? I could always go looking if someone made this specific voice but in .pt format, but I barely mess with AI and don't know how I could search for this.
https://github.com/Ashish-Patnaik/kokoclone
Generate audio with the voice and your first tool and use this to clone it into kokoro.
The pth/pt extension bit isn’t the problem. Those are PyTorch extensions and they’re synonymous like jpeg/jpg.
With something like this I can even try to make a more accurate voice than the one I already have, and tailor it to my liking!
I speak over sonos speakers when certain events happen. And use it as my voice assistant.
https://www.home-assistant.io/integrations/wyoming/
Now this on a CPU is next level. When algorithms perform well on commodity hardware, the scale tips.
This gives hope that CPULLM's are not far off that'll be just fine for majority of use cases.
When given a large text, it nicely chunked them up (debug statements showed), generated the audio and played back nicely.
Well done!
I am aware of the Option + Esc shortcut on osx for the onboard TTS but wow is it hard to listen to in 2026.
You’d definitely get to pay for it, not what I consider cheap. (“$15 per 1M characters”) But IMO just about best-in-class (maybe ElevenLabs has a voice I’d like even better).
I do wish there were better support for SSML, as well as deeper documentation of how to influence inflection in-line, but the default does well with standard emphasis (e.g. putting asterisks around text elements). Both asks are getting outside the zone of reasonable asks for this sort of distribution, though, and I remain incredibly grateful for the quality of what hexgrad and nazdridoy have put out in the world.
If you're interested in an ONNX version and a permissively licensed TTS Tokenizer, I built a pipeline for that a while back: https://huggingface.co/NeuML/kokoro-base-onnx
> AMD Ryzen 7 8745HS: 1.5 seconds
These two can probably do it much faster on their iGPUs.
Hard pass.
Why do these half baked projects get all the attention and thousands of clicks when it just takes a simple thing to bring the whole castle down?