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rhasspy
piper
C++2023–2025
rhasspy / piper · Archived

piper

A fast, local neural text to speech system

30Distinct people
47Mentions
1Explicit recommenders
11.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A fast, local neural text to speech system

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

Coqui is great, but another fantastic tool for TTS I recommend checking out is Piper. The voice quality is great, it's extremely lightweight, and it's fast enough to generate…

phyce used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:49:33 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month47 dated mentions

Observed mentions: 2023-02-01 14:32:11 UTC to 2025-06-25 22:16:19 UTC.

0510152023-02-01 00:00:00 UTC to 2023-03-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsFeb 20232023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments2023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsApr 20242024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 14 mentions; 0 submissions, 14 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJun 2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Feb 20232023-02-01 00:00:00 UTC2023-03-01 00:00:00 UTC202
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC000
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC202
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC413
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC101
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC000
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC000
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC220
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC101
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC211
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC202
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC303
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC000
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC14014
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC211
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC101
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC101
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC000
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC303
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC101
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC202
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC202
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC101

What Hacker News says

14 sources
This is so great--both in terms of the project & the write-up. Thanks for sharing your work! :) A "quick" dump of some thoughts it provoked: (1) Really like the "meaning-nonsense continuum" concept--and that neither extreme is explicitly labelled in the image immediately following where the term is introduced. :) (And, yes, "Gravity learns about regret." is indeed kind of beautiful.) (Aside: Adding alt text to the generated images would be beneficial both for copy/pasting & accessibility reasons. :) ) (2) This statement made me smile: "If you don’t enjoy reading and contemplating these sentences like this, we are simply very different people." Because while I very much fall into the category of "enjoy reading and contemplating these sentences" I can also imagine people who definitely don't. :D (3) Part of the reason for (2) was because I'
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d already encountered a couple of the "don't work" examples where my immediate thought was "wait, how about in this situation though?". e.g. "Aligning with compass." Which then got me thinking about stylistic, metaphor or time-period related aspects of grammar, e.g. pirate treasure map instructions written in cryptic sentence fragments. (4) Which leads into the whole "procedurally generated diagrams/documents" aspect of the project that I also think is particularly cool. It immediately got me thinking of being in a game and walking into an inventor character's lab or a magician's library and finding all manner of technical or arcane drawings generated for either environment or game mechanic purposes (e.g. in a detective/mystery game like "Shadows of Doubt"). Then again, since I was a kid I've always had an interest in browsing technical component and, err, office stationery catalogues, so maybe it's just me. :D It also made me think back to "Interminal" an entry to the 2020 ProcJam procedural generation game jam, which generated an airport terminal including duty-free stores with procedurally generated perfumes (including "name, visual identity and smell"): https://nothke.itch.io/interminal (5) The "physicalization" aspect of the project to render it in a tangible form is also cool--particularly because (at least to me but maybe also to all those vinyl-owning kids with no turntable :D ) a physical form seems to the amplify the "meaningfulness" of a message... The underlying "digitalized cursive handwriting" project was also of interest--though I only skimmed its write-up in an attempt to avoid yet another rabbit hole. :) The handwriting system seemed like it might also be amenable to animated use--which then made me think, "Wait! I've seen something about that recently...", had no idea what, but then some background retrieval process just produced the answer as I was writing, it was Noclip's recent "The Making of Pentiment" documentary: https://www.youtube.com/embed/ffIdgOBYwbc (6) With regard to the technical execution/implementation side of things: I've observed that a project's need for text generation often raises a question of what implementation approach to use, in terms of a DIY vs pre-existing solution. One issue which affects text generation specifically is "general solutions" often seem to tend toward specialization over time (e.g. "text generation for interactive fiction", "text generation for branching narrative-driven games", tools such as Yarn Spinner[0] & Ink[1]), which ends up making the solutions less suitable for simpler/different use cases & increases difficulty of the learning curve. This was something I ran into during a small sub-project[2] last year where the text generation was somewhat "incidental" to overall project goal. I started out with a "quick DIY" solution but still ended up spending enough time on that aspect that I started to wonder if I'd be better not entirely re-inventing the wheel. Around this time I ran into the "Blur Markup Language"[3][4][5] project which has the tag line "write text that changes" and--while I haven't yet used it--seems like it might be a promising "mid-level abstraction" solution for text generation, so thought I'd mention it as a potential option for others with text generation needs. (7) In terms of other helpful text generation related resources, I've found various "word lists" to be of use, so thought I'd mention this "weasel words" list as a starting point: https://github.com/words/weasels/blob/main/data.txt#L14 (The repo README also links to other word lists under the same org including word categories such as "buzzwords", "filler", "hedges" & words listed in order of associated positive/negative sentiment.) Thanks again for sharing your work & look forward to seeing where your projects go in future, should you share more in future. :) ---- footnotes ---- [0] https://github.com/YarnSpinnerTool/YarnSpinner [1] https://github.com/inkle/ink [2] I wanted to generate "scripted dialogue" samples[2a] to demonstrate the 900+ individual Text-To-Speech speaker voices in the Piper TTS[2b] LibriTTS voice model[2c], in a form that is: useful for evaluating the voices; not incredibly tedious to listen to; and, makes it possible to identify which speaker you are currently hearing. [2a] Subset of resulting generated[2d] speech output can be heard in the second example here: https://rancidbacon.gitlab.io/piper-tts-demos/ [2b] https://github.com/rhasspy/piper [2c] https://huggingface.co/rhasspy/piper-voices/blob/main/en/en_... [2d] Text generation script: https://gitlab.com/RancidBacon/larynx-dialogue/-/blob/featur... [3] https://bml-lang.org [4] BML intro/overview: https://bml-lang.org/docs/guide/language-basics/ [5] Online BML editor with syntax cheat sheet: https://bml-lang.org/sandbox/
In Meaningful Nonsense: How I generate sentences
My interest in TTS is around "indie" game creation, animation and "radio plays". A couple of years ago I started development of a tool to help with the generation of game audio such as NPC dialogue, "barks" or narration for those without access to/budget for human voice actors: https://rancidbacon.itch.io/dialogue-tool-for-larynx-text-to... One thing I found interesting is that writing a small "scene" and then hearing dialogue being spoken by a variety of voices often prompted the writing of further lines of dialogue in response to perceived emotion contained in voices in the generated output. Plus it was just fun. :) The version of the tool on that page is based on Larynx TTS which has continued development more recently as Piper TTS: https://github.com/rhasspy/piper I'm yet to publish my port which uses Piper TTS though: https://gitlab.
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com/RancidBacon/larynx-dialogue/-/tree/featur... Though I did upload some sample output (including some "radio announcer" samples in response to a HN comment :) ): https://rancidbacon.gitlab.io/piper-tts-demos/ Obviously there's variations in voice quality, and ability to control expression is currently limited but beats hearing my own voice. :D
In ChatTTS-Best open source TTS Model
The repo does claim that something is "open source" but the only included license text is "CC-BY-NC-ND" and the README seems to restrict permissions even further. In addition, the Hugging Face repo[-1] states the license as "Creative Commons Attribution Non Commercial 4.0" (lacking the "ND"). Unfortunately, this combination of license imprecision and restrictiveness seems par for the course with a lot of academic TTS projects. (And, even for commercial "Open Source" TTS projects it's often the case that while code might be OSS licensed, none of the the voice data/models are.) The current version[0] of the README repo states: * "The open-source version on HuggingFace is a 40,000 hours pre trained model without SFT." (Presumably refers to model.) * "At the same time, we have internally trained a detection model and plan to open-source it in
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the future." (Not directly relevant.) The included "Roadmap" indicates related completed & uncompleted tasks: * "[x] Open-source the 40k hour base model and spk_stats file" * "[ ] Open-source VQ encoder and Lora training code" * "[ ] Open-source the 40k hour version with multi-emotion control" However, as noted, the current LICENSE[1] file states: * "Attribution-NonCommercial-NoDerivatives 4.0 International" And the README also contradicts the license: * "This repo is for academic purposes only. It is intended for educational and research use, and should not be used for any commercial or legal purposes." * "The information and data used in this repo, are for academic and research purposes only." And this part of the "disclaimer" would make me concerned about potential licensing issues in regard to code and or data from other sources: * "The data obtained from publicly available sources, and the authors do not claim any ownership or copyright over the data." The code in the repo itself appears to have no license information contained within it. My go-to actually Open Source licensed Text-To-Speech project (with a range of voice[2] model licenses[3]--including Public Domain & CC-BY[4]) is Piper TTS: https://github.com/rhasspy/piper ---- footnotes ---- [-1] https://huggingface.co/2Noise/ChatTTS [0] https://github.com/2noise/ChatTTS/blob/f4c8329f0d231b272b676... [1] https://github.com/2noise/ChatTTS/blob/f4c8329f0d231b272b676... [2] Voice samples: https://rhasspy.github.io/piper-samples/ [3] Though I would also caution that (at least by my interpretation) some of the voices listed as CC0/PD or CC-BY also note that they've been "fine-tuned" on models which have more restrictive licenses and thus probably can't inherit the voice data's more permissive license. [4] However, these Public Domain and/or CC-BY voices do appear specifically created to be data license-compliant: https://brycebeattie.com/files/tts/
In ChatTTS-Best open source TTS Model
Would you settle for as "horrible" as new open source TTS systems? :D My current go to is Piper TTS: https://github.com/rhasspy/piper It's MIT-licensed, supports ~30 languages and multiple voices[0]/quality/licenses. Voice output samples: https://rhasspy.github.io/piper-samples/ Discovered just now that recently there's also been some efforts to train Public Domain/CC-BY licensed voices specifically for Piper TTS too: https://brycebeattie.com/files/tts/ ---- footnotes ---- [0] e.g. One of the English voice sets has ~900 voices(!).
In ChatTTS-Best open source TTS Model
> Last time I looked into TTS systems for German, Google was the only game in town. What I wouldn't give for a viable alternative! It doesn't even need to be open source, I'd be quite ready to pay top dollar. Will you still pay top dollar if it is open source though? :D Piper TTS[0] (MIT Licensed; developed by main dev of Larynx TTS, Mimic3 TTS & Rhasspy voice assistant) has support for ~30 languages, at least some of which have multiple voices available--in a range of quality & data licenses. And, particularly fortuitous for your needs, potentially, there's at at least one German voice that was recorded[1] specifically for Piper[2] (with emotion variants and CC0-licensed, no less :) )... Check out `thorsten` & `thorsten_emotional` on the samples page: https://rhasspy.github.io/piper-samples/ I can't speak to the quality of the German voi
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ce specifically but for English at least I've found Piper's quality & range of voices of use[3]. ---- footnotes ---- [0] https://github.com/rhasspy/piper [1] https://www.youtube.com/playlist?list=PL19C7uchWZeojjI5FUk3q... [2] In addition to other German voices based on other sources: https://huggingface.co/rhasspy/piper-voices/tree/main/de/de_... [3] Somewhat of an understatement.
In ChatTTS-Best open source TTS Model
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 vo
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ice not correctly translated #1370": https://github.com/espeak-ng/espeak-ng/issues/1370
In ESpeak-ng: speech synthesizer with more than one hundred languages and accents
FYI re: Mimic 3: the main developer Michael Hansen (a.k.a synesthesiam) (who also previously developed Larynx TTS) now develops Piper TTS (https://github.com/rhasspy/piper) which is essentially a "successor" to the earlier projects. IIUC ongoing development of Piper TTS is now financially supported by the recently announced Open Home Foundation (which is great news as IMO synesthesiam has almost single-handed revolutionized the quality level--in terms of naturalness/realism--of FLOSS TTS over the past few years and it would be a real loss if financial considerations stalled continued development): https://www.openhomefoundation.org/projects/ (Ok, on re-reading OHF is more generally funding development of Rhasspy of which Piper TTS is one component.)
In ESpeak-ng: speech synthesizer with more than one hundred languages and accents
Based on my own recent experience[0] with espeak-ng, IMO the project is currently in a really tough situation[3]: * the project seems to provide real value to a huge number of people who rely on it for reasons of accessibility (even more so for non-English languages); and, * the project is a valuable trove of knowledge about multiple languages--collected & refined over multiple decades by both linguistic specialists and everyday speakers/readers; but... * the project's code base is very much of "a different era" reflecting its mid-90s origins (on RISC OS, no less :) ) and a somewhat piecemeal development process over the following decades--due in part to a complex Venn diagram of skills, knowledge & familiarity required to make modifications to it. Perhaps the prime example of the last point is that `espeak-ng` has a hand-rolled XML parse
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r--which attempts to handle both valid & invalid SSML markup--and markup parsing is interleaved with internal language-related parsing in the code. And this is implemented in C. [Aside: Due to this I would strongly caution against feeding "untrusted" input to espeak-ng in its current state but unfortunately that's what most people who rely on espeak-ng for accessibility purposes inevitably do while browsing the web.] [TL;DR: More detail/repros/observations on espeak-ng issues here: * https://gitlab.com/RancidBacon/floss-various-contribs/-/blob... * https://gitlab.com/RancidBacon/floss-various-contribs/-/blob... * https://gitlab.com/RancidBacon/notes_public/-/blob/main/note... ] Contributors to the project are not unaware of the issues with the code base (which are exacerbated by the difficulty of even tracing the execution flow in order to understand how the library operates) nor that it would benefit from a significant refactoring effort. However as is typical with such projects which greatly benefit individual humans but don't offer an opportunity to generate significant corporate financial return, a lack of developers with sufficient skill/knowledge/time to devote to a significant refactoring means a "quick workaround" for an specific individual issue is often all that can be managed. This is often exacerbated by outdated/unclear/missing documentation. IMO there are two contribution approaches that could help the project moving forward while requiring the least amount of specialist knowledge/experience: * Improve visibility into the code by adding logging/tracing to make it easier to see why a particular code path gets taken. * Integrate an existing XML parser as a "pre-processor" to ensure that only valid/"sanitized"/cleaned-up XML is passed through to the SSML parsing code--this would increase robustness/safety and facilitate future removal of XML parsing-specific workarounds from the code base (leading to less tangled control flow) and potentially future removal/replacement of the entire bespoke XML parser. Of course, the project is not short on ideas/suggestions for how to improve the situation but, rather, direct developer contributions so... shrug In light of this, last year when I was developing the personal project[0] which made use of a dependency that in turn used espeak-ng I wanted to try to contribute something more tangible than just "ideas" so began to write-up & create reproductions for some of the issues I encountered while using espeak-ng and at least document the current behaviour/issues I encountered. Unfortunately while doing so I kept encountering new issues which would lead to the start of yet another round of debugging to try to understand what was happening in the new case. Perhaps inevitably this effort eventually stalled--due to a combination of available time, a need to attempt to prioritize income generation opportunities and the downsides of living with ADHD--before I was able to share the fruits of my research. (Unfortunately I seem to be way better at discovering & root-causing bugs than I am at writing up the results...) However I just now used the espeak-ng project being mentioned on HN as a catalyst to at least upload some of my notes/repros to a public repo (see links in TLDR section above) in that hopes that maybe they will be useful to someone who might have the time/inclination to make a more direct code contribution to the project. (Or, you know, prompt someone to offer to fund my further efforts in this area... :) ) [0] A personal project to "port" my "Dialogue Tool for Larynx Text To Speech" project[1] to use the more recent Piper TTS[2] system which makes use of espeak-ng for transforming text to phonemes. [1] https://rancidbacon.itch.io/dialogue-tool-for-larynx-text-to... & https://gitlab.com/RancidBacon/larynx-dialogue/-/tree/featur... [2] https://github.com/rhasspy/piper [3] Very much no shade toward the project intended.
In ESpeak-ng: speech synthesizer with more than one hundred languages and accents
I'm quite surprised to find this on HN, synthesizers like espeak and eloquence (ibm TTS) have fallen out of favor these days. I'm a blind person who uses espeak on all my devices except my macbook, where unfortunately I can't install the speech synthesizer because it apparently only supports MacOS 13 (installing the library itself works fine though). Most times I try to use modern "natural-sounding" voices they take a while to initialize, and when you speed them at a certain point the words mix together into meaningless noise, while at the same rate eloquence and espeak would handle just great, well, for me at least. I was thinking about this a few days back while I was trying out piper-tts [0] how supposedly "more advanced" synthesizers powered by AI use up more ram and cpu and disk space to deliver a voice which doesn't sound much bette
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r than something like RH voice and gets things like inflection wrong. And that's the english voice, the voice for my language (serbian) makes espeak sound human and according to piper-tts it's "medium". Funny story about synthesizers taking a while to initialize, there's a local IT company here that specializes in speech synthesis and their voices take so long to load they had to say "<company> Mary is initializing..." whenever you start your screen reader or such. Was annoying but in a fun way. Their newer Serbian voices also have this "feature" where they try to pronounce some english words it comes upon properly. It also has another "feature" where it tries to pronounce words right that were spelled without accent marks or such, and like with most of these kinds of "features" they combine badly and hilariously. For example if you asked them to pronounce "topic" it would pronounce it as "topich, which was fun while browsing forums or such. [0] https://github.com/rhasspy/piper
In ESpeak-ng: speech synthesizer with more than one hundred languages and accents

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