StarGANv2-VC VS tortoise-tts

So far I've only been able to find StarGANv2. Which one redditor used to create this. Is this the best there is or are there better alternatives?

I used this to make this

This looks like it was previously known as Vocodes, made by echelon who is here on HN:

https://news.ycombinator.com/item?id=23965787

The code repos used are listed in their credits section, and it looks like a mixture of (customised?) Tacotron2, Glow-TTS, HifGan, and others. Videos are generated using Wav2Lip.

Text-To-Speech (TTS) has improved greatly over the past several years, but there's still a lot of metallic sounds in "pure" TTS implementations. I've started exploring voice style conversion, otherwise known as "voice cloning", and there are some interesting repos out there with decent results. These work differently from TTS, in that you don't type out the text to be spoken, but rather pass in an audio file of what you want the cloned speaker to say, and the system outputs an audio file with the same sounds (words, intonation) but with a different speaker identity.

This may be easier to get the right cadence and emotion in the generated audio, as text doesn't capture proper emotion and intonation. I suspect game character audio will use more of voice-style conversion instead of pure TTS simply to get the right emotional cadence of the lines being delivered.

Some interesting voice style conversion repos (in no order, just a random selection if anyone is interested in exploring):

https://github.com/yl4579/StarGANv2-VC

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