Juice-Labs
tortoise-tts
Juice-Labs | tortoise-tts | |
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20 | 145 | |
387 | 11,819 | |
2.3% | - | |
8.7 | 8.0 | |
4 months ago | 1 day ago | |
Go | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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Juice-Labs
- GPU-over-IP for LLM inference?
- GTA 5 running in Qemu without PCI Passthrough using Juicy Labs
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This looks very cool: GPU-over-IP with Juice. You can attach GPU to non GPU nodes, share GPU across multiple users and applications, bring GPU to your data (vs bringing your data to the GPU) - all with just software.
The website https://www.juicelabs.co/ they have an community version as well https://github.com/Juice-Labs/Juice-Labs
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EGPU ALTERNATIVE?
I recently discovered juicelabs.co but I have not yet tested it. Maybe worth a look.
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Why I think 3D artists should get an eGPU for rendering, even if they have a desktop [How stuff works + Idea]
Or you could even use a remote GPU like Juice GPU
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Using Cloud-GPU as an eGPU?
check out https://www.juicelabs.co/
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Looking for a Bitfusion replacement? I think I may have found something really cool... Juice - which not only supports CUDA but all the graphical APIs
So our lab had been using Bitfusion until recently for a large number of VM deployments. With Bitfusion support coming to an end, we were talking about solutions and did some Googleing around GPU-over-IP and stumbled across these guys: www.juicelabs.co
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is it possible to install Automatic1111 and manage it like locally, but using a shared gpu service such as runpod.io/endpoints?
The Juice may help passing gpu over IP, I haven't tried it yet though
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ClosedAI strikes again
Even then you can always use Juice. https://www.juicelabs.co/
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Multiple inference, single remote GPU of Stable Diffusion
The functionality to do this today is available via our community edition here: https://github.com/Juice-Labs/Juice-Labs/wiki
tortoise-tts
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ESpeak-ng: speech synthesizer with more than one hundred languages and accents
The quality also depends on the type of model. I'm not really sure what ESpeak-ng actually uses? The classical TTS approaches often use some statistical model (e.g. HMM) + some vocoder. You can get to intelligible speech pretty easily but the quality is bad (w.r.t. how natural it sounds).
There are better open source TTS models. E.g. check https://github.com/neonbjb/tortoise-tts or https://github.com/NVIDIA/tacotron2. Or here for more: https://www.reddit.com/r/MachineLearning/comments/12kjof5/d_...
- FLaNK Stack Weekly 12 February 2024
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OpenVoice: Versatile Instant Voice Cloning
I use Tortoise TTS. It's slow, a little clunky, and sometimes the output gets downright weird. But it's the best quality-oriented TTS I've found that I can run locally.
https://github.com/neonbjb/tortoise-tts
- [discussion] text to voice generation for textbooks
- DALL-E 3: Improving image generation with better captions [pdf]
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Open Source Libraries
neonbjb/tortoise-tts
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Running Tortoise-TTS - IndexError: List out of range
EDIT: It appears to be the exact same issue as this
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My Deep Learning Rig
It was primarily being used to train TTS models (see https://github.com/neonbjb/tortoise-tts), which largely fit into a single GPUs memory. So, for data parallelism, x8 PCIe isn't that much of a concern.
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PlayHT2.0: State-of-the-Art Generative Voice AI Model for Conversational Speech
Previously TortoiseTTS was associated with PlayHT in some way, although the exact connection is a bit vague [0].
From the descriptions here it sounds a lot like AudioLM / SPEAR TTS / some of Meta's recent multilingual TTS approaches, although those models are not open source, sounds like PlayHT's approach is in a similar spirit. The discussion of "mel tokens" is closer to what I would call the classic TTS pipeline in many ways... PlayHT has generally been kind of closed about what they used, would be interesting to know more.
I assume the key factor here is high quality, emotive audio with good data cleaning processes. Probably not even a lot of data, at least in the scale of "a lot" in speech, e.g. ASR (millions of hours) or TTS (hundreds to thousands). As opposed to some radically new architectural piece never before seen in the literature, there are lots of really nice tools for emotive and expressive TTS buried in recent years of publications.
Tacotron 2 is perfectly capable of this type of stuff as well, as shown by Dessa [1] a few years ago (this writeup is a nice intro to TTS concepts). With the limit largely being, at some point you haven't heard certain phonetic sounds before in a voice, and need to do something to get plausible outcomes for new voices.
[0] Discussion here https://github.com/neonbjb/tortoise-tts/issues/182#issuecomm...
[1] https://medium.com/dessa-news/realtalk-how-it-works-94c1afda...
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Comparing Tortoise and Bark for Voice Synthesis
Tortoise GitHub repo - Source code, documentation, and usage guide