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Smol TTS keeps getting better! Introducing OuteTTS v0.2 - 500M parameters, multilingual with voice cloning! 🔥 > Multilingual - English, Chinese, Korean & Japanese > Cross platform inference w/ llama.cpp > Zero-shot voice cloning > Trained on 5 Billion audio tokens > Qwen 2.5 0.5B LLM backbone > Trained... show more
44,691 просмотров • 1 год назад •via X (Twitter)
Комментарии: 11
Check out the model weights and inference code base here:
llama.cpp compatible GGUFs:
OuteTTS GitHub:
Big Congrats!!! Another SOTA TTS model trained on Emilia after F5-TTS & MaskGCT! Try out:
Just tested it out and the quality is very good. More importantly, the fact that you can generate speaker profiles is awesome! Will test it out some more and add it to my growing list of supported tts engines in my app 🤣
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This is improving so fast that I don't want to speak myself anymore. Just use this and get done 🤖
Thank you for your work and for sharing insights! 🙌 Advancements like OuteTTS v0.2 showcase the rapid evolution of AI and its potential to empower global communities. 🚀 The future of #AI is bright, and collaborative innovation is key to unlocking its full potential!
Are you saying you can voice CLONE on a R-Pi? Is that what you're saying????
The font of Japanese characters is wrong, it's for (maybe) Chinese. I hope you'll pay attention and respect to each of them when you are working for multilingual/multicultural things. (like your TTS engine itself does. Brilliant quality✨️)
I tried to run it on HF. average inference time for 200 chars is >1 hour running on CPU. Why is this model so heavy?
![Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."](https://image.24vids.com/tw-2081394445682114906/amplify_video_thumb/2081392924122812417/img/uhhbrpB5pwBZ1aFF.jpg)
