piper VS opentts

After some brief research it seems the issue you're seeing may be a known bug in at least some versions/release of espeak-ng.

Here's some potentially related links if you'd like to dig deeper:

* "questions about mandarin data packet #1044": https://github.com/espeak-ng/espeak-ng/issues/1044

* "ESpeak NJ-1.51’s Mandarin pronunciation is corrupted #12952": https://github.com/nvaccess/nvda/issues/12952

* "The pronunciation of Mandarin Chinese using ESpeak NJ in NVDA is not normal #1028": https://github.com/espeak-ng/espeak-ng/issues/1028

* "When espeak-ng translates Chinese (cmn), IPA tone symbols are not output correctly #305": https://github.com/rhasspy/piper/issues/305

* "Please default ESpeak NG's voice role to 'Chinese (Mandarin, latin as Pinyin)' for Chinese to fix #12952 #13572": https://github.com/nvaccess/nvda/issues/13572

* "Cmn voice not correctly translated #1370": https://github.com/espeak-ng/espeak-ng/issues/1370

If you're not already aware, the primary developer of Mimic 3 (and its non-Mimic predecessor Larynx) continued TTS-related development with Larynx and the renamed project Piper: https://github.com/rhasspy/piper

Last year Piper development was supported by Nabu Casa for their "Year of Voice" project for Home Assistant and it sounds like Mike Hansen is going to continue on it with their support this year.

Coqui-ai was a commercial continuation of Mozilla TTS and STT (https://github.com/mozilla/TTS).

At the time (2018-ish), it was really impressive for on-device voice synthesis (with a quality approaching the Google and Azure cloud-based voice synthesis options) and open source, so a lot of people in the FOSS community were hoping it could be used for a privacy-respecting home assistant, Linux speech synthesis that doesn't suck, etc.

After Mozilla abandoned the project, Coqui continued development and had some really impressive one-shot voice cloning, but pivoted to marketing speech synthesis for game developers. They were probably having trouble monetizing it, and it doesn't surprise me that they shut down.

An equivalent project that's still in active development and doing really well is Piper TTS (https://github.com/rhasspy/piper).

There isn't an ElevenLabs app like that, but I think that's the most expedient method, by far.

(details and warning: in-depth, opinionated take, written almost for my own benefit, I've done a lot of work near here recently but haven't had to organize my thoughts until now)

Why? Local inference is hard. You need two things: the clips to voice model (which we have here, but bleeding edge), and text + voice -> speech model.

Text to voice to speech, locally, has excellent prior art for me, in the form of a Raspberry Pi-based ONNX inference library called [Piper](https://github.com/rhasspy/piper). I should just be able to copy that, about an afternoon of work!

Except...when these models are trained, they encode plaintext to model input using a library called eSpeak. eSpeak is basically f(plaintext) => ints representing phonemes. eSpeak is a C library and written in a style I haven't seen in a while and depends on other C libraries. So I end up needing to port like 20K lines of C to Dart...or I could use WASM, but over the last year, I lost the ability to be able to reason through how to get WASM running in Dart, both native and web.

It's a really annoying technical problem: the speech models all use this eSpeak C library to turn plaintext => model input (tokenized phonemes).

Re: ElevenLabs

I had looked into the API months ago and vaguely remembered it was _very_ complete.

I spent the last hour or two playing with it, and reconfirmed that. They have enough API surface that you could build an API that took voice recordings, created a voice, and then did POSTs / socket connection to get audio data from that voice at will.

Only issue is pricing IMHO, $0.18 for 1000 characters. :/ But this is something I feel very comfortable saying wouldn't be _that_ much work to build and open source with a "bring your own API key" type thing. I had forgotten about Eleven Labs till your post, which made me realize there was an actually meaningful and quite moving use case for it.

piper (https://github.com/rhasspy/piper)

I have been using piper-tts and it is GREAT and super lightweight / easy to use. On a 2080 I'm sure you can use the HQ models no worries!

So i found some library and one which is from github and have read.me or good documentation called piper (https://github.com/rhasspy/piper) so apparently this library is for rasbery pi and yes there is TXT function and i need to modify again to make it more simple but my simple project don't need this kind of big complex libary and all i need is what i said before just a function that can output sound from computer using c++ libary.

piper-whistle is a tool to manage voices used with the piper (https://github.com/rhasspy/piper) speech synthesizer. Main motivation was to download and reference models in a structured way. You may browse the docs online at https://think-biq.gitlab.io/piper-whistle/

You may want to try Piper for this case (RPi 4): https://github.com/rhasspy/piper

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).