If you want to try out the voice cloning yourself you can do that an this Hugging Face demo: https://huggingface.co/spaces/Qwen/Qwen3-TTS - switch to the "Voice Clone" tab, paste in some example text and use the microphone option to record yourself reading that text - then paste in other text and have it generate a version of that read using your voice.
This is terrifying. With this and z-image-turbo, we've crossed a chasm. And a very deep one. We are currently protected by screens, we can, and should assume everything behind a screen is fake unless rigorously (and systematically, i.e. cryptographically) proven otherwise. We're sleepwalking into this, not enough people know about it.
That was my thought too. You’d have “loved ones” calling with their faces and voices asking for money in some emergency. But you’d also have plausible deniability as anything digital can be brushed off as “that’s not evidence, it could be AI generated”.
Only if you focus on the form instead of the content. For a long time my family has had secret words and phrases we use to identify ourselves to each other over secure, but unauthenticated, channels (i.e. the channel is encrypted, but the source is unknown). The military has had to deal with this for some time, and developed various form of IFF that allies could use to identify themselves. E.g. for returning aircraft, a sequence of wing movements that identified you as friend. I think for a small group (in this case, loved ones), this could be one mitigation of that risk. My parents did this with me as a kid, ostensibly as a defense against some other adult saying "My mom sent me to pick you up...". I never did hear of that happening, though.
Yep, this has been the reality now for years. Scammers have already had access to it. I remember an article years ago about a grandma who wired her life savings to a scammer who claimed to have her granddaughter held hostage in a foreign country. Turns out they just cloned her voice from Facebook data and knew her schedule so timed it while she would be unreachable by phone.
For now you could ask them to turn away from the camera while keeping their eyes open. If they are a Z-Image they will instantly snap their head to face you.
> as anything digital can be brushed off as “that’s not evidence, it could be AI generated”.
This won't change anything about Western style courts which have always required an unbroken chain of custody of evidence for evidence to be admissable in court
Far more terrifying is Big Tech having access to a closed version of the same models, in the hands of powerful people with a history of unethical behavior (i.e. Zuckerberg's "Dumb Fucks" comments). In fact it's a miracle and a bit ironic that the Chinese would be the ones to release a plethora of capable open source models, instead of the scraps like we've seen from Google, Meta, OpenAI, etc.
> Far more terrifying is Big Tech having access to a closed version of the same model
Agreed. The only thing worse than everyone having access to this tech is only governments, mega corps and highly-motivated bad actors having access. They've had it a while and there's no putting the genii back in the bottle. The best thing the rest of us can do is use it widely so everyone can adapt to this being the new normal.
I'd be a bit more worried with Z-Image Edit/Base is release.
Flux.2 Klein is our and its on par with Zit, and with some fine tuning can just about hit Flux.2.
Adding on top of that is Qwen Image Edit 2511 for additional refinement. Anything is possible. Those folks at r/StableDiffusion and falling over the possible release of Z-Image-Omni-Base, a hold me over until actual base is out. I've heard its equal to Flux.2.
Crazy time.
Admittedly I have not dove into it much but, I wonder if we might finally have a usecase for NFTs and web3? We need some sort of way to denote items are persion generated not AI. Would certainly be easier than trying to determine if something is AI generated
That's the idea behind C2PA[1], your camera and the tools put a signature on the media to prove its provenance. That doesn't make manipulation impossible (e.g. you could photograph an AI image of a screen), but it does give you a trail of where a photo came from and thus an easier way to filter it or lookup the original.
How would NFTs/web3 help differentiate between something created by a human and something that a human created with AI and then tagged with their signature using those tools?
There are far more good and interesting use cases for this technology. Games will let users clone their voices and create virtual avatars and heroes. People will have access to creative tools that let them make movies and shows with their likeness. People that couldn't sing will make music.
Nothing was more scary than the invention of the nuclear weapon. And we're all still here.
Life will go on. And there will be incredible benefits that come out of this.
I'm not denigrating the tech, all I'm saying is that we've crossed to new territory and there will be consequences that we don't understand from this. The same way that social media has been particularly detrimental to young people (especially women) in a way we were not ready for. This __smells__ like it could be worse, alongside with (or regardless of) the benefits of both.
I simply think people don't really know that the new world requires a new set of rules of engagement for anything that exists behind a screen (for now).
We'll be okay eventually, when society adapts to this and becomes fully aware of the capabilities and the use cases for abuse. But, that may take some time. The parent is right to be concerned about the interim, at the very least.
That said, I am likewise looking forward to the cool things to come out of this.
> Nothing was more scary than the invention of the nuclear weapon. And we're all still here.
Except that building a nuclear weapon was not available to everyone, certainly not to dumb people whose brain have been feeded with social media content.
There are plenty of electronic artists who can't sing. Right now they have to hire someone else to do the singing for them, but I'd wager a lot of them would like to own their music end-to-end. I would.
I'm a filmmaker. I've done it photons-on-glass production for fifteen years. Meisner trained, have performed every role from cast to crew. I'm elated that these tools are going to enable me to do more with a smaller budget. To have more autonomy and creative control.
We've had Yamaha Vocaloid for over two decades now, and Synthesizer V is probably coming up on a decade too now. They're like any other synth: MIDI (plus phonemes) in, sound out. It's a tool of musical expression, like any other instrument.
Hatsune Miku (Fujita Saki) is arguably the most prolific singer in the world, if you consider every Vocaloid user and the millions of songs that have come out of it.
So I don't think there's any uncharted territory...we still have singers, and sampled VST instruments didn't stop instrumentalists from existing; if anything, most of these newcomer generative AI tools are far less flexible or creatively useful than the vast array of synthesis tools musicians already use.
Miku is neat but not a replacement for a human by any stretch of the imagination. In practice most amateur usage of that lands somewhere in a cringey uncanny valley.
No one was going to replace voice actors for TV and movie dubs with Miku whereas the cutting edge TTS tools seem to be nearing that point. Presumably human vocal performances will follow that in short order.
Yes, the flipside of this is that we're eroding the last bit of ability for people to make a living through their art. We are capturing the market for people to live off of making illustrations, to making background music, jingles, promotional videos, photographs, graphic design, and funnelling those earnings to NVIDIA. The question I keep asking is whether we care to value as a society for people to make a living through their art. I think there is a reason to care.
It's not so much of an issue with art for art's sake aided by AI. It's an issue with artistic work becoming unviable work.
This feels like one of those tropes that keeps showing up whenever new tech comes out. At the advent of recorded music, im sure buskers and performers were complaing that live music is dead forever. Stage actors were probably complaining that film killed plays. Heck, I bet someome even complained that video itself killed the radio star. Yet here we are, hundreds of years later, live music is still desirable, plays still happen, and faceless voices are still around, theyre just called v-tubers and podcasters.
> This feels like one of those tropes that keeps showing up whenever new tech comes out.
And this itself is another tired trope. Just because you can pattern match and observe that things repeatedly went a certain way in the past, doesn't mean that all future applications of said pattern will play out the same way. On occasion entire industries have been obliterated without a trace by technological advancement.
We can also see that there must be some upper ceiling on what humans in general are capable of - hit that and no new jobs will be created because humans simply won't be capable of the new tasks. (Unless we fuse with the machines or genetically engineer our brains or etc but I'm choosing to treat those eventualities as out of scope.)
Tin Pan Alley is the historical industry from before recording: composers sold sheet music and piano rolls to publishers, who sold them to working musicians. The ASCAP/BMI mafia would shake down venues and make sure they were paying licensing fees.
Recorded music and radio obviously reduced the demand for performers, which reduced demand for sheets.
umm, I don't know if you've seen the current state of trying to make a living with music but It's widely accepted as dire. Touring is a loss leader, putting out music for free doesn't pay, stream counts payouts are abysmally low. No one buys songs.
All that is before the fact that streaming services are stuffing playlists with AI generated music to further reduce the payouts to artists.
> Yet here we are, hundreds of years later, live music is still desirable, plays still happen, and faceless voices are still around...
Yes all those things still happen, but it's increasingly untenable to make a living through it.
Artists were saying this even before streaming, though, much less AI.
I listen pretty exclusively to metal, and a huge chunk of that is bands that are very small. I go to shows where they headliners stick around at the bar and chat with people. Not saying this to be a hipster - I listen to plenty of "mainstream" stuff too - but to show that it's hard to get smaller than this when it comes to people wanting to make a living making music.
None of them made any money off of Spotify or whatever before AI. They probably don't notice a difference, because they never paid attention to the "revenue" there either.
But they do pay attention to Bandcamp. Because Bandcamp has given them more ability to make money off the actual sale of music than they've had in their history - they don't need to rely on a record deal with a big label. They don't need to hope that the small label can somehow get their name out there.
For some genres, some bands, it's more viable than ever before to make a living. For others, yeah, it's getting harder and harder.
On the other hand, maybe we'll get models capable of removing the lyrics from things without damaging the rest of the audio. Or better yet, replacing the lyrics with a new instrument. So it might yet work out in our favor.
The HF demo space was overloaded, but I got the demo working locally easily enough. The voice cloning of the 1.7B model captures the tone of the speaker very well, but I found it failed at reproducing the variation in intonation, so it sounds like a monotonous reading of a boring text.
I presume this is due to using the base model, and not the one tuned for more expressiveness.
edit: Or more likely, the demo not exposing the expressiveness controls.
The 1.7B model was much better at ignoring slight background noise in the reference audio compared to the 0.6B model though. The 0.6B would inject some of that into the generated audio, whereas the 1.7B model would not.
Also, without FlashAttention it was dog slow on my 5090, running at 0.3X realtime with just 30% GPU usage. Though I guess that's to be expected. No significant difference in generation speed between the two models.
Overall though, I'm quite impressed. I haven't checked out all the recent TTS models, but a fair number, and this one is certainly one of the better ones in terms of voice cloning quality I've heard.
I just followed the Quickstart[1] in the GitHub repo, refreshingly straight forward. Using the pip package worked fine, as did installing the editable version using the git repository. Just install the CUDA version of PyTorch[2] first.
The HF demo is very similar to the GitHub demo, so easy to try out.
That's for CUDA 12.8, change PyTorch install accordingly.
Skipped FlashAttention since I'm on Windows and I haven't gotten FlashAttention 2 to work there yet (I found some precompiled FA3 files[3] but Qwen3-TTS isn't FA3 compatible yet).
I got some errors trying to run this on my MBP. Claude was able to one-shot a fix.
```
Loaded speech tokenizer from ~/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-1.7B-VoiceDesign/snapshots/0e711a1c0aa5aad30654426
e0d11f67716c1211e/speech_tokenizer
Fetching 11 files: 0%| | 0/11 [00:00<?, ?it/s]Fetching 11 files: 100%|| 11/11 [00:00<00:00, 125033.45it/s]
The tokenizer you are loading from
'!/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-1.7B-VoiceDesign/snapshots/0e711a1c0aa5aad30654426e0d11f67716c1211e' with an
incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instr.... This will
lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.
```
Remarkable tech that is now accessible to almost anyone. My cloned voice sounded exactly like me. The uses for this will be from good to bad and everywhere in-between. A deceased grandmother reading "Good Night Moon" to grandkids, scamming people, the ability to create podcasts with your own voices from just prompts.
I cloned my voice and had it generate audio for a paragraph from something I wrote. It definitely kind of sounds like me, but I like it much better than listening to my real voice. Some kind of uncanny peak.
i was one-shotting voices years ago that were timbre/tonally identical to the reference voice; however the issue i had was inflection and subtlety. I find that female voices are much easier to clone, or at least it fools my brain into thinking so.
this model, if the results weren't too cherry picked, will be huge improvement!
If i am ever in the same city as you, i'll buy you dinner. I poked around during my free time today trying to figure out how to run these models, and here is the estimable Simon Willison just presenting it on a platter.
hopefully i can make this work on windows (or linux, i guess).
the old voice cloning and/or TTS models were CPU only, and they weren't realtime, but no worse than 2:1, 30 seconds of audio would take 60 seconds to generate. roughly. in 2021 one-shot TTS/cloning using GPUs was getting there, and that was close enough to realtime; one could, if one was willing to deal with it, wire microphone audio to the model, and speak words, and the model would, in real time, modify the voice. Phil Hendrie is jealous.
anyhow, with faster CPUs and optimizations, you won't be waiting too long. Also 20GB is overkill for an audio model. Only text - LLM - are huge and take infinite memory. SD/FLUX models are under 16GB of ram usage (uh, mine are, at least!), for instance.
Interesting model, I've managed to get the 0.6B param model running on my old 1080 and I can generated 200 character chunks safely without going OOM, so I thought that making an audiobook of the Tao Te Ching would be a good test. Unfortunately each snippet varies drastically in quality: sometimes the speaker is clear and coherent, but other times it bursts out laughing or moaning. In a way it feels a bit like magical roulette, never being quite certain of what you're going to get. It does have a bit of charm, when you chain the various snippets together you really don't know what direction it's gonna go.
Using speaker Ryan seems to be the most consistent, I tried speaker Eric and it sounded like someone putting on a fake exaggerated Chinese accent to mock speakers.
If it wasn't for the unpredictable level of emotions from each chunk, I'd say this is easily the highest quality TTS model I've tried.
Have you tried specifying the emotion? There's an option to do so and if it's left empty it wouldn't surprise me if it defaulted to rng instead of bland.
Character Name: Marcus Cole
Voice Profile: A bright, agile male voice with a natural upward lift, delivering lines at a brisk, energetic pace. Pitch leans high with spark, volume projects clearly—near-shouting at peaks—to convey urgency and excitement. Speech flows seamlessly, fluently, each word sharply defined, riding a current of dynamic rhythm.
Background: Longtime broadcast booth announcer for national television, specializing in live interstitials and public engagement spots. His voice bridges segments, rallies action, and keeps momentum alive—from voter drives to entertainment news.
Presence: Late 50s, neatly groomed, dressed in a crisp shirt under studio lights. Moves with practiced ease, eyes locked on the script, energy coiled and ready.
Personality: Energetic, precise, inherently engaging. He doesn’t just read—he propels. Behind the speed is intent: to inform fast, to move people to act. Whether it’s “text VOTE to 5703” or a star-studded tease, he makes it feel immediate, vital.
Yeah, it's not great. I wrote a harness that calculates it as: 3.61s Load Time, 38.78s Gen Time, 18.38s Audio Len, RTF 2.111.
The Tao Te Ching audiobook came in at 62 mins in length and it ran for 102 mins, which gives an RTF of 1.645.
I do get a warning about flash-attn not being installed, which says that it'll slow down inference. I'm not sure if that feature can be supported on the 1080 and I wasn't up for tinkering to try.
you can install flash attention, et al, but if you're on windows, afaik, you can't use/run/install "triton kernels", which apparently make audio models scream. Whisper complains every time i start it, and it is pretty slow; so i just batch hundreds of audio files on a machine in the corner with a 3060 instead. technically i could batch them on a CPU, too, since i don't particularly care when they finish.
An RTF above 1 for just 0.6B parameters suggests the bottleneck isn't the GPU, even on a 1080. The raw compute should be much faster. I'd bet it's mostly CPU overhead or an issue with the serving implementation.
I can't quite figure this out: Can you save a generated voice for reuse later? The mlx-audio I looked at seems to take the text itself in every interface and doesn't expose it as a separate object. (I can dive deeper, but wanted to check if anyone's done it already)
it isn't often that tehcnology gives me chills, but this did it. I've used "AI" TTS tools since 2018 or so, and i thought the stuff from two years ago was about the best we were going to get. I don't know the size of these, i scrolled to the samples. I am going to get the models set up somewhere and test them out.
Now, maybe the results were cherrypicked. i know everyone else who has released one of these cherrypicks which to publish. However, this is the first time i've considered it plausible to use AI TTS to remaster old radioplays and the like, where a section of audio is unintelligible but can be deduced from context, like a tape glitch where someone says "HEY [...]LAR!" and it's an episode of Yours Truly, Johnny Dollar...
I have dozens of hours of audio of like Bob Bailey and people of that era.
scroll down to the second to last group, the second one down is obama speaking english, the third one down is trump speaking japanese (a translation of the english phrase)
besides, they know what side their bread is buttered on. I feel like this is almost not the real announcement; or, the engineers that wrote this up and did the demos just ran it that way. The normal speech voices are fine (lower than the anime ones on the page.) i agree that the first few are very infantile. I'll change that word if i can think of a better one.
Indeed, I have a future project/goal of "restoring" Have Gun - Will Travel radio episodes to listenable quality using tech like this. There are so many lines where sound effects and tape rot and other "bad recording" things make it very difficult to understand what was sad. Will be amazing, but as with all tech the potential for abuse is very real
hey if you want to collab or trade notes, my email is in my profile.
there was java software that did FANTASTIC work cleaning up crappy transfers of audio, like, specifically, it was perfect for "AM Quality Monaural Audio".
Observe, original: https://www.youtube.com/watch?v=YiRcOVDAryM
my edit (took about an hour, if memory serves, to set up. forgot render time...): https://www.youtube.com/watch?v=xazubVJ0jz4
i say "was [...] software" because the last 2 times i've tried to use it, it did imperceptible cleanup, making it worthless. Anyhow, all my radio plays are from OTRR, i think.
Audio.Restoration.DeNoise.DeNoiseLF.2.8.3_WiN.OSX is a more recent version i think
Have you tested alternatives? I grabbed Open Code and a Minimax m2.1 subscription, even just the 10usd/mo one to test with.
Result? We designed a spec for a slight variation of a tool for which I wrote a spec with Claude - same problem (process supervisor tool), from scratch.
Honestly, it worked great, I have played a little further with generating code (this time golang), again, I am happy.
I've been using GLM 4.7 with Claude Code. best of both worlds. Canceled my Anthropic subscription due to the US politics as well. Already started my "withdrawal" in Jan 2025, Anthropic was one of the few that was left
Are you using an API proxy to route GLM into the Claude Code CLI? Or do you mean side-by-side usage? Not sure if custom endpoints are supported natively yet.
I think the least politically divisive issue within the US is concern about China’s growth as it directly threatens the US’s ability to set the world’s agenda. It may be politically divisive if you are aligned with Chinese interests but I don’t see anything politically divisive for a US audience. I expect Chinese CEOs speak in similar terms to a Chinese audience in terms of making sure they’re decoupled from the now unstable US political machine.
From the perspective of competing against China in terms of AI the argument against open models makes sense to me. It’s a terrible problem to have really. Ideally we should all be able to work together in the sandbox towards a better tomorrow but thats not reality.
I prefer to have more open models. On the other hand China closes up their open models once they start to show a competitive edge.
With a good harness I am getting similar results with GLM 4.7. I am paying for TWO! max accounts and my agents are running 24/7.
I still have a small Claude account to do some code reviews. Opus 4.5 does good reviews but at this point GLM 4.7 usually can do the same code reviews.
If cost is an issue (for me it is, I pay out of pocket) go with GLM 4.7
Your GitHub profile is... disturbing. 1,354 commits and 464 pull requests in January so far.
Regardless of how productive those numbers may seem, that amount of code being published so quickly is concerning, to say the least. It couldn't have possibly been reviewed by a human or properly tested.
If this is the future of software development, society is cooked.
Yes, the idea is to really, fully automate software engineering. I don't know if I am going to be successful but I'm on vacation and having fun!
if Opus 4.5/GLM 4.7 can do so much already, I can only imagine what can be done in two years. Might as well adopt to this reality and learn how leverage this advancement
I've been using GLM 4.7 alongside Opus 4.5 and I can't believe how bad it is. Seriously.
I spent 20 minutes yesterday trying to get GLM 4.7 to understand that a simple modal on a web page (vanilla JS and HTML!) wasn't displaying when a certain button was clicked. I hooked it up to Chrome MCP in Open Code as well.
It constantly told me that it fixed the problem. In frustration, I opened Claude Code and just typed "Why won't the button with ID 'edit' work???!"
It fixed the problem in one shot. This isn't even a hard problem (and I could have just fixed it myself but I guess sunk cost fallacy).
I've used a bunch of the SOTA models (via my work's Windsurf subscription) for HTML/CSS/JS stuff over the past few months. Mind you, I am not a web developer, these are just internal and personal projects.
My experience is that all of the models seem to do a decent job of writing a whole application from scratch, up to a certain point of complexity. But as soon as you ask them for non-trivial modifications and bugfixes, they _usually_ go deep into rationalized rabbit holes into nowhere.
I burned through a lot of credits to try them all and Gemini tended to work the best for the things I was doing. But as always, YMMV.
Amazingly, just yesterday, I had Opus 4.5 crap itself extensively on a fairly simple problem -- it was trying to override a column with an aggregation function while also using it in a group-by without referring to the original column by its full qualified name prefixed with the table -- and in typical Claude fashion it assembled an entire abstraction layer to try and hide the problem under, before finally giving up, deleting the column, and smugly informing me I didn't need it anyway.
That evening, for kicks, I brought the problem to GLM 4.7 Flash (Flash!) and it one-shot the right solution.
It's not apples to apples, because when it comes down to it LLMs are statistical token extruders, and it's a lot easier to extrude the likely tokens from an isolated query than from a whole workspace that's already been messed up somewhat by said LLM. That, and data is not the plural of anecdote. But still, I'm easily amused, and this amused me. (I haven't otherwise pushed GLM 4.7 much and I don't have a strong opinion about about it.)
But seriously, given the consistent pattern of knitting ever larger carpets to sweep errors under that Claude seems to exhibit over and over instead of identifying and addressing root causes, I'm curious what the codebases of people who use it a lot look like.
This has been my consistent experience with every model prior to Opus 4.5, and every single open model I've given a go.
Hopefully we will get there in another 6 months when Opus is distilled into new open models, but I've always been shocked at some of the claims around open models, when I've been entirely unable to replicate them.
Hell, even Opus 4.5 shits the bed with semi-regularity on anything that's not completely greenfield for my usage, once I'm giving it tasks beyond some unseen complexity boundary.
I use Opus 4.5 for planning, when I reach my usage limits fallback to GLM 4.7 only for implementing the plan, it still struggles, even though I configure GLM 4.7 as both smaller model and heavier model in claude code
The Chinese labs distill the SOTA models to boost the performance of theirs. They are a trailer hooked up (with a 3-6 month long chain) to the trucks pushing the technology forwards. I've yet to see a trailer overtake it's truck.
China would need an architectural breakthrough to leap American labs given the huge compute disparity.
Agreed. I do think the metaphor still holds though.
A financial jackknifing of the AI industry seems to be one very plausible outcome as these promises/expectations of the AI companies starts meeting reality.
Care to explain how the volume of AI research papers authored by Chinese researchers[1] has exceeded US-published ones? Time-traveling plagiarism perhaps, since you believe the US is destined to lead always.
1. Chinese researcher in China, to be more specific.
Not a great metric, research in academia doesn't necessarily translate to value. In the US they've poached so many academics because of how much value they directly translate to.
I don't doubt China wouldn't be capable of making SOTA models, however they are very heavily compute constrained. So they are forced to shortcut compute by riding the coattails of compute heavy models.
They need a training-multiplier breakthrough that would allow them to train SOTA models on on a fraction of the compute that the US does. And this would also have to be kept a secret and be well hidden (often multiple researchers from around the world put the pieces together on a problem at around the same time, so the breakthrough would have to be something pretty difficult to discover for the greatest minds in the field) to prevent the US from using it to multiply their model strength with their greater compute.
Perhaps you should pay attention to where the puck is going to be, rather than where it is currently. Lots of original ideas are coming out of Chinese AI research[1], denying this betrays some level of cope.
1. e.g. select any DeepSeek release, and read the accompanying paper
I'll pay attention to where the puck is because that is something I can observe, where it is going to be is anybody's guess. Lots of original ideas are coming out of Chinese AI research but there is also lots of junk. I think in the longer term they will have the advantage but right now that simply isn't the case.
Your 'cope' accusation has no place here, I have no dog in the race and do not need to cope with anything.
I will rephrase my statement and continue to stand by it: "Denying the volume of original AI research being done by China - a falsifiable metric - betrays some level of cope."
You seem to agree on the fact that China has surpassed the US. As for quality, I'll say expertise is a result of execution. At some point in time during off-shoring, the US had qualitatively better machinists that China, despite manufacturing volumes. That is no longer the case today - as they say, cream floats to the top, and that holds true for a pot or an industrial-sized vat.
I could say the same about grok (although given there are better models for my use cases I don't use it). What part of divisive politics are you talking about here?
Amusingly one of their examples (the final Age Control example) is prompted to have American English as an accent, but sounds like an Australian trying to sounds American to my ear haha
Has anyone successfully run this on a Mac? The installation instructions appear to assume an NVIDIA GPU (CUDA, FlashAttention), and I’m not sure whether it works with PyTorch’s Metal/MPS backend.
FWIW you can run the demo without FlashAttention using --no-flash-attn command-line parameter, I do that since I'm on Windows and haven't gotten FlashAttention2 to work.
Is there any way to take a cloned voice model and plug into Android TTS and/or Windows?
I have a friend with a paralysed larynx who is often using his phone or a small laptop to type in order to communicate. I know he would love it if it was possible to take old recordings of him speaking and use that to give him back "his" voice, at least in some small measure.
Haha something that I want to try out. I have started using voice input more and more instead of typing and now I am on my second app and second TTS model, namely Handy and Parakeet V3.
Parakeet is pretty good, but there are times it struggles. Would be interesting to see how Qwen compares once Handy has it in.
I see a lot of references to `device_map="cuda:0"` but no cuda in the github repo, is the complete stack flash attention plus this python plus the weights file, or does one need vLLM running as well?
Can anyone please provide directions/links to tools that can be run locally, and that take an audio recording of a voice as an input, and produce an output with the same voice saying the same thing with the same intonations, but with a fixed/changed accent?
This is needed for processing an indie game's voice recordings, where the voice actors weren't native speakers and had some accent.
I suspect they might be using voice lines from Chinese gacha games in addition to what clearly sound like VTubers, YouTubers, and Chinese TV documentary narrations. Those games all come with clean monaural CN/JP/EN files consistent in contents across language for all regions, for, an obvious[1] reason.
Well, if you look at the prompts, they are basically told to sound like that.
And if you ask me, I think these models were trained on tween fiction podcasts. (My kids listen to a lot of these and dramatic over-acting seems to be the industry standard.)
Also, their middle-aged adult with an "American English" accent sounds like any American I've ever met. More like a bad Sean Connery impersonator.
The real value I see is being able to clone a voice and change timbre and characteristics of the voice to be able to quickly generate voice overs, narrations, voice acting, etc. It's superb!
I shared a recording of audio I generated with that here: https://simonwillison.net/2026/Jan/22/qwen3-tts/
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