LLamaSharp VS tortoise-tts

For example, I have my own task and I need another tool, so I search and find what I need. https://github.com/SciSharp/LLamaSharp and this allows me to take the next step https://github.com/Xsanf/LLaMa_Unity . I can already run LLM on Unity. And this is already an opportunity to use it in games natively.

I searched through GitHub and nothing comes up that is new. I wanted to run the model through the C# wrapper linked on LLaMASharp which requires compiling llama.cpp and extracting the libllama dll into the C# project files. When I build llama.cpp with OpenBLAS, everything shows up fine in the command line. Just as the link suggests I make sure to set DBUILD_SHARED_LIBS=ON when in CMake. However, the output in the Visual Studio Developer Command Line interface ignores the setup for libllama.dll in the CMakeFiles.txt entirely. The only dll to compile is llama.dll; I know this is a fairly technical question but does anyone know how to fix?

C#/.NET: SciSharp/LLamaSharp

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_...

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

neonbjb/tortoise-tts

EDIT: It appears to be the exact same issue as this

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.

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...

Tortoise GitHub repo - Source code, documentation, and usage guide