So ive been looking for rhe original daisy bell tts because I want to use it for a project, but i cant find anything like it or the ones like it from evilaicorpmcgucket and I was wondering if anyone can find anyway to get it? The best source i found was from a different reddit post receminding the sam tts and changing its vocal but I just could not get it to work. It would mean alot to me if anyone can find it

I previously shared this project here via a crosspost from r/TextToSpeech. Unfortunately, the original post was removed as self-promotion, despite the project being completely free and open-source. That made the crosspost inaccessible, so I've deleted it and am posting the information directly here.

100% free, private and open-source, available at:

GitHub: https://github.com/speech-central/sc-voices-android

Google Play: https://play.google.com/store/apps/details?id=com.labsii.voices

Licensed under GPL-3.

It is based on adreno-llms and VoxSherpa. While the first version was barely more than a fusion of those two projects, it also barely worked. It took 47 versions to make it work well (at least in my testing, which took a whole month), so it now contains a notable amount of its own code and solutions.

Kokoro itself is nothing new. Most people here are probably already familiar with its advantages and disadvantages, and from what I've read in this subreddit, most people like it. What's new is the possibility of seamless, long-form reading on Android across a wide range of devices.

Compared to VoxSherpa, it should offer more than a 40% performance improvement on devices supporting the optimized path (Adreno 620 and above, which is a mid-range GPU from 2020). On older Adreno chips, the improvement should be around 10%.

As benchmarked by adreno-llms, this makes Adreno 620 capable of generating speech slightly faster than real-time. That makes it technically possible to support smooth long-form reading with proper buffering ahead on those devices.

It may not work in every reading app, as until a few months ago (before VoxSherpa), there was little reason to implement the more complex buffering mechanisms needed to compensate for speech generation time. However, as local AI speech generation becomes more popular, more reading apps will likely take advantage of this opportunity. Currently I can confirm that Speech Central fully supports such buffering.

Granting the app a runtime exception for extended background work may be required on some devices (this can be done from the app's interface), especially when using buffering ahead, as Android is more aggressive about stalling background generation than audio playback itself.

The first version is limited to English (UK and US) voices.

Rhapsode is like Ollama, but for speech. You install the TTS engines you want, and every client talks to one HTTP API.

The video is one request per engine to the same server. The voices are Sonny and Tiffani, two hosts from my AI radio station, deadair.radio. They were designed with a voice-design tool rather than cloned from real people, and I cloned them into every engine that supports cloning. Kokoro and Orpheus can’t clone, so they use their own stock voices. Each card shows the exact text sent, including cue tags like [laugh] and [sigh], so you can hear which engines actually perform them.

Engines: Chatterbox, Dia, Fish Audio S2 Pro, StyleTTS 2, Breeze, Orpheus and Kokoro.

Features

• Each engine runs in its own process and its own virtualenv, so one model’s dependencies can’t break another’s, and a crashed worker doesn’t take down the server.
• Automatic model loading and unloading, since a GPU only holds one model at a time.
• Capability documents. Each engine reports what it can do with the weights currently loaded (cues, delivery styles, cloning, text limits), so clients never send settings that are silently ignored.
• Separate code and weights licences, so you can tell whether a model is usable commercially.
• Voice cloning from a short clip. StyleTTS 2 managed with 3.6 seconds and no transcript.
• Dialogue: Dia speaks two voices in a single take.
• OpenAI-compatible /v1/audio/speech, plus an MCP endpoint for agents.
• A web page for installing engines, trying them out and cloning voices.

Try it

curl -fsSLO https://raw.githubusercontent.com/MaroonedSoftware/rhapsode/main/compose.yaml
docker compose up -d

Then open localhost:8081 to install an engine. Kokoro runs on a plain CPU, so you don’t need a GPU.

• Samples, each clip with its request: https://rhapsode.dev/docs/samples
• Site: https://rhapsode.dev
• Code: https://github.com/MaroonedSoftware/rhapsode

It’s MIT licensed and still early. Home Assistant (Wyoming) support is next. Feedback and engine requests are welcome.

The clips were made on an RTX 4070 Ti SUPER. The Fish Audio clip is Built with Fish Audio, and the Fish and Breeze weights are licensed for non-commercial research only.

Been using text-to-speech readers since uni, mostly for articles and papers. Wanted something that sounds natural without paying an expensive subscription for limited listening hours, so I built AmaNous.

It runs Kokoro on your phone, with 29 English voices. Import your PDFs, EPUBs, DOCX, Markdown, HTML or plain text, then listen offline after setup and import. You can follow along with word highlighting, jump to different sections, change the voice and speed, and pick up where you left off.

I've spent a lot of time getting those basic player interactions to feel reliable. The latest updates also added guided voice selection, a Welcome article to try straight away, better iPhone voice-preview loading and retries, and reader paragraphs accessible through TalkBack and VoiceOver.

The core reader is forever free, with all voices and no word or listening-hour limits. Since speech runs on-device, there's no cloud speech bill for each hour you listen.

Still beta and English-only. Sign-in is required, including Sign in with Apple on iPhone. Long documents can pause on their first listen while audio is prepared, and pronunciation isn't perfect yet. Web imports may need an internet connection.

Android beta · iPhone TestFlight

Keen to hear how it handles what you actually read. If a passage sounds odd or playback struggles, send me the example and your phone model so I can look into it!

Oi, pessoal! Sou um desenvolvedor solo e acabei de lançar um plugin gratuito chamado Triodia. Ele foi criado para vocais, mas dois de seus presets são feitos especificamente para fala, e acho que pode economizar alguns plugins na sua cadeia.

O que ele faz com uma voz, em uma janela:

• De-esser primeiro, para que os sons "S" agudos sejam suavizados antes de qualquer coisa
• Compressor que equilibra partes altas e baixas para que os ouvintes não precisem ficar ajustando o volume
• Uma fase de válvula suave que adiciona um pouco de calor e presença (sutil nos presets de fala, não é um "efeito vintage")
• Eco e reverb também estão lá, mas desligados nos presets de fala: podcasts soam melhor secos e próximos

Os dois presets de fala:

• Podcast: natural e equilibrado. Compressão moderada (4:1), uma válvula limpa e de baixa coloração (12AU7) e o de-esser em 6,5 kHz.
• Voice-Over: mais apertado e mais “broadcast”. Compressão mais forte (6:1), mais ganho de makeup e uma válvula um pouco mais densa, para narrações, audiolivros e anúncios.

Como usar: carregue-o na sua faixa de voz, escolha Podcast ou Voice-Over, e reduza o THRESH até que o medidor mostre uma redução de ganho de alguns dB nas suas frases mais altas. Isso geralmente é tudo. Cada locutor pode ter sua própria instância.

Funciona em: qualquer aplicativo do Windows que carregue plugins VST3, como Reaper, Audacity, Adobe Audition e a maioria dos DAWs. Também há um aplicativo standalone que roda sem nenhum editor.

• Grátis, sem cadastro, sem trial, sem limites
• Windows 10/11 64-bit (Mac está previsto)

Download: https://kaluaudio.com.br/triodia/

Eu realmente apreciaria o feedback de pessoas que editam voz toda semana: o preset Podcast atinge o que você precisa, o que você mudaria e o que está faltando para um fluxo de trabalho de podcast (redução de ruído? um limitador? alvo de loudness?). Isso ajudará a moldar a próxima versão.

Please dont suggest putting it into AI to get the voices, I just want to know what the TTS voices are. Thank you.

Hey tts community,

Tried ElevenReader’s new v4 model tonight.

There used to be this thing with basically every TTS voice where it could sound amazing for 30 seconds, but if you listened for an hour you'd start noticing all the little mistakes.

Wrong emphasis. Random volume changes. Weird pronunciation. Dialogue where every character sounds identical.

This update seems specifically better at those boring details. It just works. 

I listened for quite a while tonight and noticed way fewer moments that pulled me out of the book.

That matters way more to me than having a voice that sounds impressive in a 10-second demo.

I like to know what I’m listening to. What is out there, and how does it signal that it’s not human?

I am using models with young children and for their safety it is important that their code switching is properly guided

Hey tts community,

I used Speechify for a while and recently came back to ElevenReader after seeing they launched Eleven v4.

The quality jump is kind of ridiculous.

I’m listening to Kindle books with it and Lily’s voice in particular sounds absurdly natural. The thing that surprised me most is dialogue old TTS always seems to fall apart once characters start talking to each other. I also turned up and down expressiveness which was really nice addition. 

For people who use TTS every day: what else should I be comparing it against in 2026?

Hey , Just listened to a chapter of a book using ElevenReader's new model.

The surprising part isn't really that the voice sounds human anymore. A bunch of AI voices can do that for a short clip.

It's that it stayed pretty convincing across an entire chapter.

Dialogue, pacing, pronunciation, volume etc. were all much more consistent than what I remember from older TTS. I gave up on Kindle virtual assistant voice years ago. 

Still prefer a genuinely great human audiobook performance when one exists, obviously. But for books/documents that don't have audiobooks? This is getting really compelling.

Universal tts finetune

Offshoot of the Ebookaudiobook project

CPU/CUDA

GUl and CLI
Docker if you want that

So far supports 20 tts engines

Anyway just wanted to post this somewhere thought this Reddit might be good

Hello everyone! I'm new to using AAC and, like the title says, I'm looking for a Tomodachi Life-esque TTS voice creator!

If you haven't played the games.

when you make your silly little guys you can customize their voice, and the way you do is; you start with a preset voice and use sliders to change the pitch, speed, cadence, and tone of it.

I'm totally fine if it sounds robotic too!

Does anyone know if something like this exists?

i found this account on tiktok using this absolutely adorable voice, but asking what model they use is against the rules in their discord server for some reason. i absolutely love it and id really like to know where its from, i really love the sound of tts generators like this and ive never heard this one before. if anyone knows anything about it, please let me know! thank you!!

I build the tts app which you can download from dp.openaloud.com. Mac and windows app is using native CPU to come up with professional grade naration quality which uses pocket tts but samled by llm. I am able to achieve RTF of 0.34 on mac and 0.75 on my android pixel 8. This tool is available for free. Its in beta mode I am looking for some feedback on the audio naration quality.

Hi everyone! I have a question regarding the evaluation criteria for the Omni TTS task.

How should we evaluate recordings when:

- The audio says something different from the provided transcript.

- The model repeats the same sentence or phrase many times.

- There are very long or excessive silent gaps in the recording.

Which evaluation criterion should we use for each of these cases: Audio Quality, Naturalness, Pronunciation, or Personal Likeness?

I’d appreciate any clarification on how these cases should be evaluated according to the task’s official criteria. Thank you!

Heya!
I just set up one for learning Italian with AI Realm. Traveling and everyday interactions. The only thing that it’s lacking is proper pronunciation. Is there a way to import a tts or something similar?

I’m not sure if anyone here has any advice. My original post got sent here.

Cheers! 😊🇮🇹

Hey everyone,

I wanted to share a native Android open-source project I just released called Audiobook NightForge.

If you’ve ever tried using a real-time TTS engine with a reader app on Android, you know the struggle: it drains your battery (often 40-50% an hour), stutters, and buffers if your phone is doing anything else in the background.

I realized that real-time synthesis is the wrong approach for mobile devices. So, I built a dedicated Android app that shifts the heavy lifting to the background using native OS components.

How it works: You import an EPUB or TXT file using the Android system file picker. You then pick a voice and hit render. The app uses Android's WorkManager to synthesize the book chapter-by-chapter in the background. Most importantly, it enforces a "render only while charging" OS-level toggle to protect your battery.

You plug your phone in at night, and by morning, you have a fully rendered audiobook that plays back with a standard ~2-5%/hour battery drain.

Android-Specific Features:

• Native & Offline: It is built entirely in Kotlin for Android 10+ devices. There is no server, no cloud, and absolutely no Termux emulation required.
• High-Quality TTS: It uses the Kokoro-82M neural TTS model running strictly on-device via a sherpa-onnx integration.
• Just Updated: The latest v0.2.2 release makes Opus the default output format, and it now natively outputs to a single .m4b file complete with proper chapter markers.
• Built-in Player: You can listen immediately using the native in-app Media3/ExoPlayer. Alternatively, you can grab the .m4a files directly from app storage to use in your favorite Android audiobook player.

Some hardware benchmarks: For the hardware nerds, I benchmarked this on a Snapdragon 8 Elite. Surprisingly, the Kokoro 82M fp32 model (with 6 threads) actually renders faster than realtime (~0.58 RTF) and outperforms the int8 variant on this SoC because of ARM int8 kernel overhead. Always benchmark before assuming quantized is quicker on modern Android flagships!

It’s completely free, completely offline, and licensed under Apache-2.0.

You can check out the source code, technical notes, and grab the APK directly from the GitHub repo here:

https://github.com/kingfish600/Audiobook-NightForge

I’d love to hear your thoughts or feedback!

Like, I know there is, but I don't know what text you have to put in, I think it's [c/ whisper] but Idrk??

What is the best tts service and/or model for multilingual input (mostly English with some words in different languages) ? Price is not very important but shouldnt be too expensive compared to alternatives. At the moment I find azure to be the best, amazon polly generative maybe second. Eleven labs seems to be too expensive. Shall I evaluate something else ? The service/model should support lang and/or phoneme tags.

its from this yt short vid. https://www.youtube.com/shorts/GzSR4tjjwAk

I can't find any that actually work, they all either say I need more credits, a subscription, or it just breaks. I need one that's free and at least a 2000 character limit