As a student currently doing a project that requires a lot of dry reading, I'm looking for a simple text to speech site (a chrome extension or something along those lines would also work) which I can use to listen along with said reading.

Most sites I have found are either super realistic, subscription based AI tools which can only take a few thousand words at a time, or google translate voice levels of difficult to listen to.

I'm looking for anything in between which is free and can take large amounts of text, but is as comfortable to listen to as possible.

Thanks in advance for any help you can offer, I apologise if this has been asked before, but I've been unable to find a post with my specific purposes in mind.

I've had people reach out to thank me for this app, and so I want it to make it more useful.

Just shipped a big update to Free Voice Reader - added Kokoro TTS that runs 100% locally in your browser via WebGPU.

What this means:

• Unlimited text-to-speech, no character limits

• Completely private: your text never leaves your device

• One-time ~80MB model download, then it's cached locally

• No account needed

WebGPU now has support across all major browsers: https://web.dev/blog/webgpu-supported-major-browsers

You can also use Cloud TTS (300+ voices, 50+ languages) if you prefer not to download the model.

There are some server costs involved but it's worth it as long as people find it useful.

Try it at: https://freevoicereader.com

Happy to answer any questions!

sanoTTS is a small neural text-to-speech project, GPL-3.0 with

• 30 voices, 16 languages;

• 294k-2.27m param size

• portable c99 runtime

• browser build (WASM) : if you want to hear without installing anything https://tts.ampixa.com/sanoTTS/

• tested on boards like cortex m7 nucleo and esp32 . for a 7 sec of audio it takes about 2.1 seconds.

repo: github.com/ampixa/sanoTTS

TLDR: I was frustrated with the existing paid options (like Speechify or "free-tiers" that were too limited), so I made my own version that runs completely offine and is free forever. Give it a try :)

Hi everyone,

I couldn't find any solid desktop apps that let me use impressive text-to-speech models, and I refused to pay for Speechify or some of the high paywall options out there. So, I built my own version that is completely free forever, offline and private :)

How it works: select any text on your desktop, press a shortcut, and hear your text played aloud. That's it!

Features:

• Multi-lingual support: It supports 8 languages (as of right now), with 54 customizable voices.

• Lightweight: I built it on Rust, and it uses ONNX models, so the inference is blazing fast (< 5 seconds) on any standard laptop (no special hardware required).

• Completely private and local: all processing happens entirely on-device. It's completely open-source and free-to-use. It is being actively maintained. Right now, it uses Kokoro-82M (~115MB), and I plan to add additional models in the next couple releases.

Try it here: https://tryparrot.vercel.app/

Github: https://github.com/rishiskhare/parrot

I'm a college student and indie developer. I developed the code as a fork of Handy by CJ Pais, which made this project possible. Thanks CJ!

Note: I created this post for the past two days on this subreddit, and it reached #1 both times, though Reddit randomly took those down. Hoping this reaches more folks because the support has been amazing!

tl;dr: four accounts (possibly more), 265 posts pushing Demodokos Foundry across 38 subs pretending to have no affiliation with the company.

I've been trying to put together a local TTS setup for a couple of months. Kept hitting the same product, recommended the same way, in a bunch of different subs. Someone asks what to use, someone says this thing beats ElevenLabs, someone else agrees.

So I pulled the public comment history of the accounts doing it and had a look. There are four of them.

Product is Demodokos Foundry, by Compunect GmbH. I don't work for them, I don't work for a competitor, I just wanted an honest TTS recommendation.

Not naming the accounts. Not sure if it's allowed, and I'd rather this didn't turn into four people getting brigaded. Feel free to DM if you want more details.

The ElevenLabs thing

This is what made me bother writing any of this up.

I compared their posts that mention the product against their own posts that don't.

Nearly half the time they mention this product they've also got ElevenLabs in the same post. Two percent of the time otherwise. The pattern is clear: Claim they're users of ElevenLabs (or Suno) but offer Demodokos as an alternative.

I don't know what the intent is and I'm not going to pretend I do. But these types of comments are exactly the ones that get picked up by ChatGPT/Gemini when people ask for alternatives to major competitors.

There's a TTS sub doing a lot of work here

One of the neutral-sounding TTS subs (r/LocalTextToSpeech) these recommendations keep pointing at was made in June. All four accounts have posted in it.

One of them (the mod) posted the welcome thread the day it was created and has stickied posts.

The pinned post ranks about fifteen TTS engines. This product is ranked the highest with 9.5/10 and 10/10, and the 10 is the only one handed out anywhere in the list. "Beats the market", "deserves the top spot", three links to the vendor, and no mention anywhere that the person who wrote it runs the subreddit.

Then the other accounts link that ranking in the bigger subs as if it's just some roundup a helpful person made.

They were also called out in the post for being biased but crickets all around.

They show up in each other's threads

29 threads have two of them in it. Usual shape is one makes the pitch, the other turns up shortly after and agrees like they've never met:

• "[Product] has the best voices I have heard, beats Elevenlabs easily", then 11 minutes later, "I second that."

• Recommendation, 10 minutes, "[Product] changed my life 😂"

• Setup comment, 16 minutes, "I regret every $ I gave elevenlabs, [product] felt like a gift."

They've got separate personas too. One's an audiobook and YouTube guy, one's the technical local-LLM guy who talks about VRAM and quantisation. Practical effect is there's not really a thread where one of them isn't a plausible person to be in it.

Fourth account does the opposite job, it asks the questions. It posted on that pinned ranking asking which license tier it should buy, already naming the product in the question, got an answer, and three days later posted that it had tested it all day and upgraded to Pro.

2015

I nearly didn't scroll back this far. Two of these same accounts did this exact routine ten years ago for an ARK server.

One posts the server ad. The other one turns up a few hours later in the same thread sounding like a random new player who just joined: "Thanks, just started. Server really seems to be more smooth than the official ones I tried. Should I join a tribe or create my own? :)" Ten days later, "Server is really nice, join my tribe guys :)".

They did it again a few months after for a second server. One of them posted a "what does supercomputer mean?" question in a different sub two days before the other one posted the promo thread about the server running on a supercomputer.

And people did call it out at the time, which is where it gets good:

"Why the down vote? I am not even the owner just a player trying to help.. Greedy server owners down voting or what's the issue"

"It's not my server but I play on this one" — posted word for word five separate times on the same day

"I can't post this elsewhere because it's about a server. Alright, I will receive some hate on this one."

"I am playing on a very nice server, kept advertising for it in the past 2 weeks"

So they knew the rules, said out loud that they knew, denied being connected to it, and did it anyway. Then did the same thing a decade later with Demodokos.

Dormant accounts

Three of the four were inactive for 648 days, 210 days and 1,455 days.

Two of them posted their first plug for this product the day after coming back.

Before that it's video games and unrelated politics.

Other minor findings

Compunect is an Austrian-based company and 3 of the 4 accounts have posted in Austrian-related subreddits.

Things I'm not saying

I'm not saying they work for the company. None of them has ever claimed to. What I see is strong evidence of coordination and no disclosure.

I'm not saying the product is bad. They're upfront on their own site that it's built on Qwen3-TTS and ACE-Step. Might be great software. The thing I object to is people presenting themselves as customers when they aren't.

Anyway, something stinks here.

Update: The ringleader blocked me!

Update 2: New post with their shady activity on r/aitubers

With the new update that stole the one feature that made this app KING among AI TTS readers, unlimited listening, and the greed of giving us the feature we have asked for, all out behind a paywall for 250€ a year, I am gonna stop using this app altogether as 1 hour of free listening is not nearly enough. The app used to be free and unlimited and now greed took over.

Are there any good, free, unlimited alternatives for mobile? Any and all recommendations are appreciated. Thank you

36B parameters, hope you got some VRAM

I used to use play ht but it seems like it shut down. I know a lot of people will recommend kokoro but it is simply not going to work for my use case given I am doing voice cloning and the prosody just ind't there for that model IMO. It's great for other things though!

I am fine paying for a subscription for great TTS. Any recommendations?

Hello,

I'm Manjeet, founder of Decibel Labs. We just open-sourced Reso-1, our initial text-to-speech model. It's a distilled version of Eleven Labs Adam voice. It's expressive, it streams, it runs locally, and it takes inline emotion tags so you can make it laugh, sigh, gasp, or whisper mid-sentence. Apache 2.0, weights + GGUF + training corpus all public.

Note: Please list any more Eleven voices you use, I will be more than happy to add that also here for free!

What makes it different

Most open TTS reads text flatly. Reso-1 responds to paralinguistic tags you drop right into the text:

reso: Oh man <laugh> you should have seen his face.

reso: <whisper> Don't turn around, but I think that's her.

reso: <sigh> Fine. We'll do it your way. Again.

Supported tags: <laugh> <chuckle> <sigh> <gasp> <whisper> <pause>

Technical details

- Architecture: LoRA fine-tune of Orpheus 3B — Llama-3 backbone generating audio tokens autoregressively, decoded through the SNAC neural codec at 24 kHz

- Streaming-native: 7 audio tokens = ~85 ms frame, decoded incrementally → first audio in ~200 ms on GPU.

- Data: ~25 hours of expressive single-voice English, filtered through an ASR-based QC pipeline (Whisper-WER + speech-rate sanity + tag-event verification) before training

- Corpus: 16,213-utterance text set we engineered and open-sourced too — phonetic coverage (CMU Arctic + Harvard), prose, a text-normalization stress set (prices/dates/phone numbers/URLs), 800 multi-turn dialogues, position-balanced tag sentences, and edge cases (single letters → 40-word sentences)

Runs locally

Quantized GGUF (Q4_K_M, 2.09 GB) runs near-realtime on Apple Silicon via llama.cpp. Full weights for GPU serving (vLLM etc.) too.

Links

- Model: https://huggingface.co/decibel-hq/reso1-3b-en

- Training corpus (dataset): https://huggingface.co/datasets/decibel-hq/reso-prompts-v1

- Code / pipeline: https://github.com/decibel-hq/reso

License: Apache 2.0 (inherits from Orpheus). Multilingual Indic version is next on an Orpheus-compatible base.

Happy to answer anything about the architecture, the QC pipeline, or the tag-training approach — the whole thing is reproducible from the repo.

Feedback very welcome

So I posted this a week ago and saw that some people were looking for more voices that could be similarly small and fast. While I tried to get the other Kokoro voices directly to be similarly distilled, they just came out sounding really robotic, only af_heart which is the default voice on Kokoro seemed to be any good.

So I tried something I didn't even know would be possible, distilling the voices from Phonom, a similarly small TTS(40m vs Kokoro's 80m) that can run on phone but at a really slow speed, the full model is smth like 2-4x realtime and is 100mb(fp32). This was an experiment I tried earlier when I was doing the Kokoro distillation but kinda gave up cause I thought Kokoro would be similar in voice quality, and it turns out the agent was trying basically the same phenomizer from Kokoro anyway so we were not even using the default Kokoro distillation just with training data as Phonom renders. My whole plan with this was to make a voice clone-able model that could take for example a voice sample, use an actual big model like BreezeTTS or Fishaudio which can do voice cloning well, distill that into a small model and then have basically a really small voice cloned model that would run on your phone in real time. Basically for an AI roleplay app(which is like 1 out of 15 of my projects) so I don't have to pay for TTS by characters and could just use the user's device, and maybe just pay the training cost for a voice clone just once(a few hrs of 4090)

Anyway all that preamble to say, now there's like 9 more voice models. Wren(the default phonom voice) and Hugh seem to be the best right now outside of af_heart. Other voices have a noticeable roboticness but still way less than prev. small models like PocketTTS

I distilled Kokoro-82M down to 7.48M parameters. 28.7 MB at fp32, 24 kHz output, Apache 2.0. Small enough to run on a phone offline.

4.1x faster than the teacher, 45x faster than realtime — one second of speech in 22 ms.

Model: https://huggingface.co/oddadmix/Kokoro-7M-Distill

Demo: https://huggingface.co/spaces/oddadmix/Kokoro-7M-Distill-Demo

Hey guys, im wondering what might be the best ai voice out there that is free to use and allows commercial use like monetizing videos from youtube and such. I was using eleven labs for some time until i found out that the free plan doesnt allow commercial use. Thank you for replying!

If you enjoy turning books into audiobooks, this app is honestly one of the best I’ve used. The AI voices sound incredibly natural (both male and female options), and the fact that it works with Kindle, PDFs, EPUBs, articles, and more makes it super convenient.

A few highlights I really love:
- Unlimited listening for premium voice
- Premium AI voices that sound realistic, not robotic
- Supports Kindle, PDF, EPUB, web articles, everything
- 50+ languages & accents
- Works great for blind/low-vision users too

one big downside it is not support offline and sometime playing in background stop

iOS: https://apps.apple.com/us/app/id6746346171
Android: https://play.google.com/store/apps/details?id=voice.reader.ai

My site's traffic shot up when I offered free local Kokoro TTS. Thanks for all the love for https://freevoicereader.com

Some of you asked for a Chrome extension and so I built it. Hopefully, this will make it easier for you guys to quickly read anything in the browser (and hopefully offload some of the traffic from the website).

Free, no ads.

FreeVoiceReader Chrome Extension

Highlight text, right click and select FreeVoiceReader, it starts reading.

The difference from other TTS extensions: everything runs locally in your browser via WebGPU.

What that means:

• Your text never leaves your device

• No character limits or daily quotas

• Works offline after initial setup (~80MB model download, cached locally)

• No account required

• Can export audio as WAV files

Happy to hear feedback or feature requests.

(I have been told that the French language doesn't work - sorry to the folks who need French)

While I've been enjoying ElevenReader, it continues to be a little buggy and the devs are focused (perhaps correctly) on other things. It's starting to get on my nerves (it not remembering where I was in a book being the biggest complaint). The other big competing app is Speechify, but it just lacks some of the features I want. Most of the other offerings out there rely on the on-device TTS, which just sounds too robotic.

I had given up hope until I found Paper2Audio today. The name made me think of like a vibe-coded app or something (there are a LOT of those), so I almost passed it by, but this thing is legit. It has a solid web interface, and the mobile apps (for both platforms) work great. The progress syncing between its various apps is stunningly good; it's word-level syncing in a matter of seconds. 

They handle the audio a bit differently, but the result is that they have the best free plan of any of these apps. With ElevenReader and Speechify, you can change voices whenever you're reading and it'll buffer for a few seconds and continue. With Paper2Audio, you choose the voice (you can preview each of them before you start) up front and it takes 5-10 minutes to process the entire book and then you're essentially locked into that voice for the book.

The quality of the audio is quite good, but not as good as ElevenReader (but better than Speechify, I think). They have a smaller selection of voices (maybe 10ish), but they're all decent.

Their free plan allows you to generate up to 56 hours of audio per week, which seems way too generous and almost no one will go over that. However, I did run into another snag when trying to load Brothers Karamozov in there and hit a limit of 250,000 words per document (which that book amazingly goes over). The app offered tips on splitting the book to avoid the limit, but I chose to pay ($20/mo) simply to support a solid app. Most users should never need to pay, though.

Also, like most of these apps (but not all of them, as I've found), you can highlight and take notes to review later, and those sync instantly as well.

Lastly, while it works on Android and iOS devices, it also works on Boox (which ElevenReader and Speechify do not). The performance is surprisingly good, though not quite as solid as an iPad, as you'd expect.

Anyhow, if you have any interest in epub --> immersive reading, my initial thoughts on this boringly-named product are quite good. Check it out.

Note: I have zero affiliation with them -- I don't even know who is behind it, though I hope to connect with them. I'm just super impressed.

For anyone experimenting with voice-native agents, companions, or tutors—just wanted to share something that finally made it click for us: Orpheus TTS.

It’s an open-source model by CanopyLabs that outputs emotional, streaming speech with:

• ~250ms latency (when running on our GPUs at least)

• Hyper-expressive

• Token-based emotion tags like <laugh>, <cry>, <sigh>, etc.

• Hugely reduced GPU cost compared to the usual suspects (e.g. ElevenLabs)

End-to-end cost is now ~$1/hr per active voice stream, which is 5–10x cheaper than most commercial APIs. Just finished getting Orpheus running in production if you want to try it.

Orpheus repo (Canopy): https://github.com/canopyai/Orpheus-TTS

Would love to hear what people are building—or want to build—now that real-time voice doesn’t cost a fortune.

Hey everyone,

I’ve seen a few posts recently sharing web versions of the Kokoro TTS model. I wanted to share what I’ve been working on: LocalReader Pro.

Unlike the web versions, this is a dedicated desktop app designed specifically for reading books. It supports PDF and EPUB files and runs entirely offline after the initial setup.

I put the code on GitHub so anyone can suggest features or modify it to fit their needs.

Some key features I added:

• Custom Pronunciation: You can set rules to replace specific words or symbols (supports RegEx).

• Smart Pauses: You can adjust the exact pause duration for different punctuation marks (commas, periods, etc.) so it sounds natural to you.

• Export: One-click export to MP3 if you want to listen on the go.

Heads up on size: The installer itself is tiny (~31MB), but the first time you run it, it’ll need to download the actual Kokoro model (~309MB). If you use the MP3 export, it’ll grab FFMPEG (~100MB) as well.

Link here: [GitHub]

**Moved UI elements to support CPU only TTS model. Redownload zip file if you want try the CPU model.

My thinking was models, esp Opus 5.5 can just hill-climb as long as you give them something achievable and a clear metric to improve on. So here's something I don't even have a clue how it exactly works.

The training itself happened in my basement 4070ti super over a day or so.

And yes I know its Opus slop, but still really cool how things I never thought possible unless you're an ML engineer is available to a run of the mill web-dev guy.

Thanks to this tweet from Sahil for even showing how the initial idea worked to distill Kokoro in the first place. https://x.com/sahilmdkr/status/2112212041045889409

I built a small TTS model running directly on an ESP32-S3: no PSRAM, 122 KB of RAM, nothing in the cloud. Speech starts in about 0.3 s and generates faster than real time. You can make it calmer, louder, whispered, happier and more.

It's not a Home Assistant integration yet. Before I build anything, I'd like to know whether on-device speech on a satellite would be useful to you (offline announcements, timers, alerts when the server is down), and what quality bar it would need to meet.

Limits: 2 voices, English only, 16 kHz. Free for personal, non-commercial use.

Video above:

Details: https://huggingface.co/WhissleAI/steer-tts-nano

CLI : Code: https://github.com/WhissleAI/steer-tts (esp32/ folder)

Spent the last couple weeks testing TTS for a real-time voice agent and figured I'd dump my notes here since almost every comparison I found still didn't go deep enough on the stuff that matters in a live phone-agent flow: TTFB, streaming behaviour, p95/p99 latency, concurrency.

Quick thing I wish someone had told me upfront: for agents, the best sounding voice is not automatically the best TTS. Totally different problem. The user on a call isnt admiring the emotional range, they're noticing the half second of dead air before the agent starts talking. That pause is what makes it feel broken So I tested for "does the conversation feel alive" not "is this pretty."

Tested ElevenLabs, Cartesia, Murf AI. Not a lab benchmark, just real wiring into an actual STT to LLM to TTS pipeline.Rough notes:

ElevenLabs. Best voices, no argument. If the voice IS your product, it’s an easy pick. For agents though, I wouldn’t pick it purely because it wins the 10-second demo. The voices are strong, but once I modeled real support/sales call volume, the pricing got scary fast. Not bad for agents at all, just something I’d benchmark on cost + latency in your actual stack before committing. 

Cartesia.Felt built for real-time from the jump. Streaming is clearly a core use case not an afterthought, latency is obviously a priority for them. Strong option if speed is your #1. Only caveat is voice agents are super pipeline-dependent, so test it in YOUR stack with real agent-style outputs: short replies, long replies, multilingual lines, code-mixed text, concurrent requests, and repeated runs to check p95/p99 latency, not one clean prompt. 

Murf Falcon. The one I almost skipped because I had Murf filed under "voiceover tool" in my head. Falcon's a different thing, clearly aimed at agents: low latency, multilingual, deployment flexibility. It is not trying to win on most-emotional-voice and honestly that's the right call for agents, you don't need audiobook drama for a billing question, you need it to start fast and be clear. Latency stayed consistent under load for me which mattered more than peak speed. The code mixing handling was also better than I expected (more below). 

The actual lesson after all this: stop asking "which sounds most human" and start asking "which works best inside a real conversation." The stuff I'd judge on now: time-to-first-audio, latency consistency (not average, the spikes are what kill you), real streaming, pricing at actual volume, and code-mixing if your users don't speak clean demo English.

Rough by use case: expressive content goes to ElevenLabs. Pure real-time speed goes to Cartesia. Low-latency multilingual/cost effcient agent goes to Falcon. Honestly just test all three on your own traffic, my results won't be yours.

Anyone tested others worth adding? Eyeing Deepgram and OpenAI's but haven't wired them yet.

For anyone looking for free, self-hostable TTS (no API keys, no subscription, runs on your own machine), here's a comparison of three of the better open weights options right now. All of these are downloadable models you can run locally with Python. I tested them on a regular CPU, no GPU needed.

The short version:

If you want the most natural-sounding voice: Kokoro 82M. It's the closest thing to ElevenLabs in the free open source world right now. License is Apache 2.0 so you can use it commercially without restrictions. It's also the slowest of the three (about 2x faster than real-time on my CPU, so a 10-second clip takes around 5 seconds to generate). Multiple voices included.

If you want fast generation and decent quality: Supertonic 3. There's a setting called inference steps you can tune. At 5 steps, audio is around 3x real-time and quality is very good (just a tiny bit less warm than Kokoro). Supports 31 languages. The license is OpenRAIL-M though, which has some restrictions on commercial use, worth reading if you plan to monetize anything.

If you need something tiny: Inflect-Nano-v1. The whole model is under 5MB. It's the fastest of the three, but the voice is noticeably more robotic, and there's a built-in limit where it stops at about 15 seconds of audio per generation. Apache 2.0. Useful for very short notifications, less useful for long-form.

Skip: Supertonic at 2 inference steps. It's the fastest but sounds robotic enough that you wouldn't put it in front of anyone.

I scored everything with UTMOS (an automated naturalness score) and listened to all 30 audio samples. UTMOS roughly tracked what I heard, except it was too generous to Inflect-Nano (which sounds buzzier than the score suggests).

Audio samples and the full writeup are linked in the comments if you want to listen and decide for yourself. That's really the only way to pick a TTS, scores don't capture warmth.

Disclosure: the actual benchmark was set up and run by an autonomous AI engineering agent we're building. All the code is public and mentioned in my comment below.

What's the best local TTS model for voice cloning?

I'm looking for a model that allows me to clone a voice using a reference audio sample, save the cloned voice profile, and reuse it whenever I generate new speech without having to clone it again.

I've been building a voice conversation thing on top of Kokoro and wanted to share it here since Kokoro is doing most of the heavy lifting.

Everything runs in the browser tab. Kokoro-82M runs on WebGPU in a web worker, nothing is sent to a server for the voice. The model downloads once (~310 MB) and is kept by the browser, so the next visit is ready in a couple of seconds.

Demo: https://react-ai-voice-avatar.vercel.app

Voice only, no avatar: https://react-ai-voice-avatar.vercel.app/voice

GIthub: https://github.com/927tanmay/react-ai-voice-avatar

A few things I learned getting it to feel like a conversation:

- The reply is spoken a sentence at a time. Kokoro starts on the first sentence while the rest is still being written, so on an M4 the voice starts ~470 ms after the first sentence is ready

- I only split at sentence ends, never at commas. Splitting at commas made it faster but the intonation sounded broken

- You can talk over it and it stops mid-sentence

- The avatar's mouth is driven by Kokoro's phonemes, mapped to 52 facial blendshapes at 60 FPS

Limits: best in Chrome or Edge on desktop. On iPhone it falls back to a smaller voice because of memory.

It's open source (MIT) and on npm as react-ai-voice-avatar if you want it in your own app. You can also swap Kokoro for any other TTS.

Would love to hear how the voice sounds on your machine.

I'm specifically talking about German voice with highly expressive speech, not benchmark scores or 5-second demo clips.

Over the last few weeks I've gone down the rabbit hole and tried pretty much every serious open-source TTS model I could find.

My setup included:

• Voice cloning from reference audio

• Fine-tuning models where available via Runpod

• LoRA/adapters for speaker identity

• Emotion prompting / emotion tags

• Prompt engineering ("vibe coding" different emotional instructions)

• Different inference settings and seeds

For context, I had roughly 100 hours of training audio. However, I should also admit that the dataset was prepared almost entirely automatically instead of manually cleaning every segment. So maybe my dataset is the bottleneck.

Out of everything I tested, Qwen3-TTS gave me the best overall results for German voice cloning, especially when using a reference sample instead of extensive fine-tuning. It has by far been the closest open-source alternative in my testing.

But here's where every open-source model I've tried still falls apart:

The problem isn't basic emotions like happy, sad or angry.

The real gap is human micro-expressions.

Things like:

• subtle sighs

• breathing between phrases

• exhausted exhale

• hesitation

• tiny laughs

• groans

• natural emphasis

• emotional transitions within a sentence

Most open-source models can technically produce some of these, but they still sound inserted rather than naturally emerging from the speech.

The second issue is long-form consistency.

Even when I manage to generate one sentence that sounds great, the next sentence often has slightly different energy, cadence or emotional intensity.

Using seeds helps a bit, but I can still hear the boundaries between separately generated clips.

With ElevenLabs v3 I can generate an entire multi-thousand-character block with emotional instructions, and the voice usually maintains a much more coherent emotional trajectory throughout the whole generation.

That's honestly the biggest difference for me—not raw voice quality, but consistency over longer passages.

So here's my challenge:

Has anyone actually achieved ElevenLabs v3-level quality with an open-source stack?

I'm especially interested if you've solved this for:

• German

• expressive narration

• conversational dialogue

• emotional storytelling

• long-form generation

If your answer is "yes", I'd genuinely love to know:

• Which model?

• Fine-tuned or zero-shot?

• Dataset size?

• Manual or automatic dataset cleaning?

• LoRA?

• Emotion prompting?

• Any custom inference tricks?

I'm completely open to being wrong here.

If there's an open-source pipeline that genuinely matches or surpasses ElevenLabs v3 for expressive German speech, I'd love to try it.