opentts VS coral-pi-rest-server

I think using something like openTTS might be safer. Though I'm pretty sure no one will ever find out you used their online tts.

The most exciting thing about Home Assistant's "Year of the Voice", for me, is that it is apparently enabling/supporting @synesthesiam's continued phenomenal contributions to the FLOSS off-line voice synthesis space.

The quality, variety & diversity of voices that synesthesiam's "Larynx" TTS project (https://github.com/rhasspy/larynx/) made available, completely transformed the Free/Open Source Text To Speech landscape.

In addition "OpenTTS" (https://github.com/synesthesiam/opentts) provided a common API for interacting with multiple FLOSS TTS projects which showed great promise for actually enabling "standing on the shoulders of" rather than re-inventing the same basic functionality every time.

The new "Piper" TTS project mentioned in the article is the apparent successor to Larynx and, along with the accompanying LibriTTS/LibriVox-based voice models, brings to FLOSS TTS something it's never had before:

* Too many voices! :)

Seriously, the current LibriTTS voice model version has 900+ voices (of varying quality levels), how do you even navigate that many?![0]

And that's not even considering the even higher quality single speaker models based on other audio recording sources.

Offline TTS while immensely valuable for individuals, doesn't seem to be attractive domain for most commercial entities due to lack of lock-in/telemetry opportunities so I was concerned that we might end up missing out on further valuable contributions from synesthesiam's specialised skills & experience due to financial realities & the human need for food. :)

I'm glad we instead get to see what happens next.

[0] See my follow-up comment about this.

Yes, if you scroll down on the github page you can read the extensive README.md file on its setup.

I was wondering if there was a way to use a private OpenTTS server for the Android Text-To-Speech engine.

If your use case allows for a web API, I've had good experience running OpenTTS[0].

It packages several models, including Coqui AI's TTS which I tend to use the most. There's a handy Docker image, too.

[0] https://github.com/synesthesiam/opentts

https://github.com/synesthesiam/opentts is run through Docker, which is pretty simple, and provides a GUI in the browser. There is a good selection of voice engines and voices, and the local Web server has API endpoints. I've been using this on Linux Mint lately.

If you've not already encountered them I'd definitely encourage you to check out these Free/Open Source projects too:

* Larynx: https://github.com/rhasspy/larynx/

* OpenTTS: https://github.com/synesthesiam/opentts

* Likely Mimic3 in the near future: https://mycroft.ai/blog/mimic-3-preview/

Larynx in particular has a focus on "faster than real-time" while OpenTTS is an attempt to package & provide common REST API to all Free/Open Source Text To Speech systems so the FLOSS ecosystem can build on previous work supported by short-lived business interests, rather than start from scratch every time.

AIUI the developer of the first two projects now works for Mycroft AI & is involved in the development of Mimic3 which seems very promising given how much of an impact on quality his solo work has had in just the past couple of years or so.

I haven't personally dealt with Docker much, but am trying to make use of some open source stuff that seems to require Docker to run (https://github.com/synesthesiam/opentts).

That said, you can always look at something like https://coral.ai/products/accelerator/ to help with the performance you need.

Should only be ~$60 https://coral.ai/products/accelerator/

The Coral USB accelerator might be of interest if you want to do some light ML with a low power budget.

However, a pi doesn't have the strength to run something like Llama.cpp, of course, so I've been considering using something like the Coral USB Accelerator (https://coral.ai/products/accelerator). As I've been learning more about it, it seems to be very geared towards TensorFlow Lite models. But whisper.cpp and Llama.cpp use ggml models.

Recently found this thing, though I haven't found a use case for me.