gRPC VS tortoise-tts

That's not true at all. Case in point In general, this is not a problem that AGC can solve. The language can help (something Java is admittedly particularly bad at) but even so, there'll always be avenues for leaks. That's just the nature of shared things. Interestingly, in the linked grpc case, the leaked memory is only half the problem -- AGC doesn't help at all with the leaked HTTP2 connection.

I've trialled my new image with an existing project via JLink that's heavy on Netty and gRPC the image works great (with a small tweak to exclude grpc-netty-shaded due to grpc-java#9083).

When developing an application, the vast majority of code is written by other people. We import that code and make use of it to get whatever we need done. In this case, the developer of an various android applications are using grpc-java.

`proto_library` for building the `.bin` file from protos works great. Generating stubs/messages for "all" languages does not. Each language does not want to implement gRPC rules, the gRPC team does not want to implement rules for each language. Sort of a deadlock situation. For example:

- C++: https://github.com/grpc/grpc/blob/master/bazel/cc_grpc_libra...

- Python: https://github.com/grpc/grpc/blob/master/bazel/python_rules....

- ObjC: https://github.com/grpc/grpc/blob/master/bazel/objc_grpc_lib...

- Java: https://github.com/grpc/grpc-java/blob/master/java_grpc_libr...

- Go (different semantics than all of the other): https://github.com/bazelbuild/rules_go/blob/master/proto/def...

But there's also no real cohesion within the community. The biggest effort to date has been in https://github.com/stackb/rules_proto which integrates with gazelle.

tl;dr: Low alignment results in diverging implementations that are complicated to understand for newcomers. Buff's approach is much more appealing as it's a "this is the one way to do the right thing" and having it just work by detecting `proto_library` and doing all of the linting/registry stuff automagically in CI would be fantastic.

Small clarification (to my understanding, I'm not a Java Guru) on why Java got on top - those Java implementations use something called Direct Executor. It's super performant when there's no chance of a blocking operation. But if you are to do anything more than echo service, you might be in trouble. Other implementations probably don't suffer from the same constraint. The related discussion can be found in this PR.

clone https://github.com/grpc/grpc-java

If you do streaming then the best option would be to use a so called manual flow control. You can find an example here.

Another interesting link is their official grpc-java benchmarks project, which is also used in the benchmark I've posted you.

Both JLink (gRPC#3522) and Graal have some issues; I'm especially concerned about the Serial GC in Graal so will be putting that under some stress soon to see if that confirms my suspicions. I'll also be good when some Java 16 JRE Alpine images appear as the JDK is too bloaty.

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