> Voice replication: Recreate consistent vocal profiles from just a 30-second audio sample of your voice or a voice you have the rights to use, backed by built-in consent verification, SynthID watermarking, and C2PA credentials to protect both developers and their vocal talent.
I guess voice cloning is widely enough available now from other providers that Google are no longer hesitant to ship it.
Yesterday night I was doing a project with QwenTTS 1.7B.
After some debugging, making a clean dataset with clean recordings, and experimenting with a good fine tune recipe (much props to the new GPT models yesterday being cheaper).
I was able to make a robo-me that sounds absurdly good, family was shocked, all in a matter of a few hours.
This has been possible for quite a long time (probably 1-2 years). There are multiple open models that can do this quite well. Recent example from my YT feed:
They probably do something similar to GPT-Live where they expect a given voice profile to send them a sample saying 'This is the owner of this voice and I consent for synthetic samples to be made of it'
and/or local voice cloning is good enough as is so Google doesn't grant a uniquely liable ability?
>and/or local voice cloning is good enough as is so Google doesn't grant a uniquely liable ability?
Probably the latter. Cat's already out of the bag to the extent that you can synthesize with a specific voice in one go and it sounds decent. Even if you need commercial models for better intonation or whatever, you can probably get the commercial models to first generate with a generic voice, then use a local model to transfer that to voice you're cloning. That'll probably get rid of any C2PA watermarks too.
usually people bring up anyone who is losing physical control of their voice faculties, so they can have a synthetic voice that matches their natural voice
The iPhone has a built in voice cloner hidden in the accessibility settings for exactly this use-case: creating a backup of your voice in case you need it in the future.
I mean yeah? If google offers a cloud nmap tool, should everyone get in a tizzy about how google is "evil", even though it saves baddies maybe 5 minutes of work?
Pet peeve on Google's AI rollouts: there's no alignment across the three platforms they have, consumer, prosumer, cloud. Scroll to the end of every release, including this one, and you'll see different availabilities. The fun part is the models don't even have the same capabilities across platforms! Omni Flash, last I tried and read the docs, is video and text out on consumer and prosumer but video out only on GCP. So if your org disables consumer and prosumer, like mine, it's a coin flip whether you can use the fancy new models or what they can do.
No cloud, no tokens to pay. Reads a book using a full cast of characters. Quotation attribution detection (for my novel) is at 97.2% accuracy (485/499 quotes identified and assigned correctly). The autofill of character voice descriptions uses the prose to determine how the character sounds.
Employs Gemma 4[1] for the prose analysis (voice fills, quotation detection) and Qwen3 TTS Voice Design[2] for creating voice samples. Runs on an 8GB NVIDIA T1000 GPU card, 96 GB RAM, and a AMD Ryzen 5 7600.
It's cool technology and I read a lot of audiobooks, even hundreds of hours of TTS. I feel like my brain can fill in the character voices from the text - on the page it's not like they're different fonts.
I understand audiobook narrators often do it, and that's fun. But it's not so critical in my opinion
I direct my own extended daydream Star Trek fanfic (okay, I'm on season 2 episode 17) and recently I looked to see if I could have each scene file be read aloud a la an audiobook or radio drama.
Getting GPT-Live to have unique enough voices and to be expressive with how I imagine the voices going in my head is hard to direct, there's not enough control there.
So this Gemini 3.8 specific large voice library and ability to tightly control (if you are willing to write a script) is nice to find, and while I'm not sure which of the 5,286 Gemini products this is, nor how to onboard and get started feeding this my own text files, nor what training will happen to my data if I did somehow use it, I love that the state of the industry is such that Google can do this and release it publicly, because that means eventually an equivalent product can come from someone else and be used locally / confidently that the generated audio or inputs won't be retained and misused.
I'm personally using this locally: https://github.com/mateogon/pdf-narrator (it's a Python frontend for Kokoro) on my M1 Macbook Air (from 2020, with 8GB RAM) and it's incredible. I make my own audiobooks now - for free!
I vibed a wikipodcast app that has a wikipedia dump, gemma llm for search and summarization and a tts model for audio gen so I could just ask about topics while on the go, and the app would just go off and tell me stuff out of the Wikipedia database.
It was especially nice during a bike trip along the Rhine, I listened to a lot of the history of the industrial area and its cities.
I vibe coded a playground UI for trying this out. The conversation mode is neat, and it's very expensive - most of my experiments have cost less than a cent.
Nice but in general I wouldn't put my API key into some third party website, no matter if it claims to not store it. It's not something personal, you are a bit of a celeb here so I wouldn't think you'd save the keys, it's just good general data hygiene. Especially not if a leaked API key can rack up thousands of dollars in fees quickly.
It depends on what you are after (quality, legibility, performance, etc.).
If you're after quality then Qwen3 TTS is a very good model esp. if you take some effort to craft a voice file. It is slow, so isn't practical for real-time voices (like assistants). It can also occasionally switch to a different voice to the one provided, so you may want to break up the text being processed.
I've not yet tried other recent/recentish models.
If you are after performance then two options from older models are:
1. flite with a HTS (Hidden Markov Model) voice like cmu_us_rms (male) or cmu_us_slt (female);
2. espeak/espeak-ng with an MBROLA (an Overlapped Add model) voice (mb-us1, mb-de5-en, etc.).
Alternatively, you could try using Qwen3 TTS or over voice changing model with the CMU Arctic (http://www.festvox.org/cmu_arctic/) voice data which includes audio for the rms and slt voices among others.
If you're feeling adventurous you could also try fine tuning one of the TTS models on that data to create a custom voice, though the data is likely to be in the training data for the voices, so using an audio sample may be sufficient depending on the TTS model.
I've got the best results with BreezeTTS, Higgs Audio v3, and Fish Audio S2 Pro. audio.cpp (https://github.com/0xShug0/audio.cpp) is an easy way to run a lot of different models.
Different models have different strengths. If you throw an entire ebook at a model you're going to get a different result than if you craft a perfect 10 second sentence with a model that supports voice direction and emotion tags, so you should try a bunch depending on your use case.
"Super tinny monotone robotic voice" does not sound neither tinny nor monotone. Compared to what TTS from 90s sounded like. Or even how actors impersonated robots in movies. Has the model been eating too much hype DJs?
None of these examples are really what the prompt asked for. It's just like image models, once you get over how unbelievable it is that a computer produced this you realize the result isn't actually what you want.
It's giving me an error when I try to generate a voice with Voice Design in AI Studio. It also says voice replication isn't available in my region.
Also weird that there are no "neutral gender" voices in the English language. There's also limited "use cases," like the "Gaming" use case is empty?
And there's no pricing listed anywhere.
I don't know, I guess their roll out is a bit sloppy. It's a bit of a shame, though, since the voices which are available all sound like generic Gemini voices to me. Nothing stands out is being particularly interesting or impressive about this.
Seems like voice actors are safe for now. This is technologically incredible, but the results are really not very good, and usually not particularly close to the prompt. In basically all of these examples some core part of the prompt is completely ignored.
Technologically incredible as in "I cannot believe it's possible for a computer to do this" and not very good as in "these examples are not what was prompted and I can't think of a use case where these would be acceptable".
Imagine someone showing you that they've trained their dog to hold a paintbrush and paint. There would be no contradiction between "this is incredible" and "these paintings suck".
Sorry if this is in that article, but I am on my phone and can't see it. How much would this cost to batch generate an audiobook? Right now I just listen to things in the 11 labs app which is free, but I would rather just generate audio files.
That is the biggest difference here for me. I have not looked at every solution, but many. They are either much more expensive or garbage quality. For example my next target audiobook is a monster 250k words, 1.5m characters, so about $15 here or $75 using elevenlabs (0.05 per 1k characters). For me that is the difference between I will or I won't use it.
The first <sigh> does sound a lot like a moan. OP linked to the timestamp so I missed it when it first played. I was also confused but on second playback I heard the first <sigh> and also thought wtf.
I guess voice cloning is widely enough available now from other providers that Google are no longer hesitant to ship it.
After some debugging, making a clean dataset with clean recordings, and experimenting with a good fine tune recipe (much props to the new GPT models yesterday being cheaper).
I was able to make a robo-me that sounds absurdly good, family was shocked, all in a matter of a few hours.
So yeah the cat is out of the bag for sure.
https://m.youtube.com/watch?v=WENMgQE9tws
and/or local voice cloning is good enough as is so Google doesn't grant a uniquely liable ability?
Probably the latter. Cat's already out of the bag to the extent that you can synthesize with a specific voice in one go and it sounds decent. Even if you need commercial models for better intonation or whatever, you can probably get the commercial models to first generate with a generic voice, then use a local model to transfer that to voice you're cloning. That'll probably get rid of any C2PA watermarks too.
https://en.wikipedia.org/wiki/Sarah_Langs
https://www.youtube.com/watch?v=WAeHgE94rVo
No cloud, no tokens to pay. Reads a book using a full cast of characters. Quotation attribution detection (for my novel) is at 97.2% accuracy (485/499 quotes identified and assigned correctly). The autofill of character voice descriptions uses the prose to determine how the character sounds.
Employs Gemma 4[1] for the prose analysis (voice fills, quotation detection) and Qwen3 TTS Voice Design[2] for creating voice samples. Runs on an 8GB NVIDIA T1000 GPU card, 96 GB RAM, and a AMD Ryzen 5 7600.
[1]: https://deepmind.google/models/gemma/gemma-4/
[2]: https://huggingface.co/spaces/Qwen/Qwen3-TTS-Voice-Design
I understand audiobook narrators often do it, and that's fun. But it's not so critical in my opinion
The title of the video is 'KeenLore - Emotive Audiobook Creator Demo' and it appears to be a web UI and some local stack that reads text files.
Getting GPT-Live to have unique enough voices and to be expressive with how I imagine the voices going in my head is hard to direct, there's not enough control there.
So this Gemini 3.8 specific large voice library and ability to tightly control (if you are willing to write a script) is nice to find, and while I'm not sure which of the 5,286 Gemini products this is, nor how to onboard and get started feeding this my own text files, nor what training will happen to my data if I did somehow use it, I love that the state of the industry is such that Google can do this and release it publicly, because that means eventually an equivalent product can come from someone else and be used locally / confidently that the generated audio or inputs won't be retained and misused.
Also the Qwen3-TTS demo is cool, you can describe the voice you want: https://huggingface.co/spaces/Qwen/Qwen3-TTS
I came across both on this subreddit, it's very active: https://www.reddit.com/r/TextToSpeech/
I'm personally using this locally: https://github.com/mateogon/pdf-narrator (it's a Python frontend for Kokoro) on my M1 Macbook Air (from 2020, with 8GB RAM) and it's incredible. I make my own audiobooks now - for free!
My favorite voice is am_michael and here's a sample: https://voicerankings.com/voice/kokoro-82M/male/am_michael/s...
https://m.youtube.com/watch?v=jDudeaWppSE
Is there a good browser extension that does this with a flexible TTS backend? I know Qwen, Kokoro, and VibeVoice all have decent quality..
https://support.google.com/chrome/answer/14768725?hl=en
It was especially nice during a bike trip along the Rhine, I listened to a lot of the history of the industrial area and its cities.
https://tools.simonwillison.net/gemini-tts-playground#compos...
How long is this stored? What could go wrong? :P
If you're after quality then Qwen3 TTS is a very good model esp. if you take some effort to craft a voice file. It is slow, so isn't practical for real-time voices (like assistants). It can also occasionally switch to a different voice to the one provided, so you may want to break up the text being processed.
I've not yet tried other recent/recentish models.
If you are after performance then two options from older models are:
1. flite with a HTS (Hidden Markov Model) voice like cmu_us_rms (male) or cmu_us_slt (female);
2. espeak/espeak-ng with an MBROLA (an Overlapped Add model) voice (mb-us1, mb-de5-en, etc.).
Alternatively, you could try using Qwen3 TTS or over voice changing model with the CMU Arctic (http://www.festvox.org/cmu_arctic/) voice data which includes audio for the rms and slt voices among others.
If you're feeling adventurous you could also try fine tuning one of the TTS models on that data to create a custom voice, though the data is likely to be in the training data for the voices, so using an audio sample may be sufficient depending on the TTS model.
Different models have different strengths. If you throw an entire ebook at a model you're going to get a different result than if you craft a perfect 10 second sentence with a model that supports voice direction and emotion tags, so you should try a bunch depending on your use case.
Also weird that there are no "neutral gender" voices in the English language. There's also limited "use cases," like the "Gaming" use case is empty?
And there's no pricing listed anywhere.
I don't know, I guess their roll out is a bit sloppy. It's a bit of a shame, though, since the voices which are available all sound like generic Gemini voices to me. Nothing stands out is being particularly interesting or impressive about this.
Having a voice under 1Mo is crazy, even if it sounds robotic.
https://tts.ampixa.com/sanoTTS/
Um, what?
Imagine someone showing you that they've trained their dog to hold a paintbrush and paint. There would be no contradiction between "this is incredible" and "these paintings suck".
Price per hour:
- 3.8 Flash TTS, standard: $0.81
- 3.8 Flash TTS, batch: $0.41
- 3.8 Flash‑Lite TTS, standard: $0.54
- 3.8 Flash‑Lite TTS, batch: $0.27
Official pricing can be seen here: https://ai.google.dev/gemini-api/docs/pricing#gemini-3.8-fla...
$0.50 per hour pricing could last a long time with back and forth conversation use.
They all sound like Americans putting in their best fake British accent.
https://storage.googleapis.com/gweb-uniblog-publish-prod/ori...
Onomatopoeia? Sure it is there, and some fillers (or whatever you call those little sounds). But moans?
I would not want that in my product.