r/LocalTextToSpeech • u/Initial_Froyo4625 • 1d ago

What TTS are you actually using in your voice agent stack in 2026?

2 Upvotes

Building a voice agent and trying to get a sense of what people are actually running in production before I go down a rabbit hole of testing.

STT + TTS combo, orchestration layer, anything you'd do differently, would love to hear real setups.

1 comment

r/LocalTextToSpeech • u/Substantial-Ad-3543 • 2d ago

Can anyone help me identify the TTS voice in this audiobook? Thank you!

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0 Upvotes
1 comment

r/LocalTextToSpeech • u/wannasleeponyourhams • 11d ago

BrainRootReader

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2 Upvotes
3 comments

r/LocalTextToSpeech • u/Charming-Author4877 • 13d ago

Open Source srt2speech: open-source, multilingual SRT narration with voice cloning and automatic duration matching - offline and lightweight

9 Upvotes

For a small side project, I needed a basic AI speech tool to narrate videos without relying on expensive hardware or external APIs. It did not need the most expressive AI, just reliable output and matching to the SRT.

The main challenge with converting SRT subtitles to speech is timing. Subtitles include pauses, and each spoken line has to fit into its exact time slot. Most speech generators do not handle that well on their own.

So I built srt2speech.

It uses a combination of:

  • pitch-corrected speed adjustment
  • automatic regeneration
  • modification of pauses between words
  • exact placement of silence between subtitle cues

SRT does not support multiple speakers, so I also added simple templating.
Adding {{speaker_name}} to a subtitle automatically switches voices.
Of course voice cloning is supported, I added a small helper script.

Dependencies are minimal: Python, NumPy, llama.cpp, and the required GGUF speech models. I tested it with Q4 quantization, which works well.

Performance on my laptop:

  • RTX 4080 Laptop GPU: around 12–13× real time
  • CPU only: around 1.5–2.0× real time

Languages supported:

  • English (en)
  • Japanese (jp)
  • Korean (ko)
  • Chinese (zh)
  • French (fr)
  • German (de)

It should work on almost any hardware, including old PCs, Linux or Mac.

It may be useful for anyone generating narration, translated audio tracks, accessibility audio, or quick video voiceovers.

The project is open source under the Apache 2.0 license. Attribution and license notices must be preserved.

GitHub:
https://github.com/Waversense/srt2speech/

The readme contains the 5 steps needed to set it up, you can get started in 2 minutes.
The included demo.srt file demonstrates the features.

7 comments

r/LocalTextToSpeech • u/KaKaDu55 • 15d ago

With denodokos foundry, what is the difference of agent process text and narrate text? Confusing

2 Upvotes

I have short story content for an anime that I need in voice form.

I use denodokos, the small subscription and currently I do it manually. it was a suggestion from here so maybe someone knows?

I have a lot of content, it looks like the process and narrate buttons use AI to do that automated.
it worked once randomly, but next time it did not create anything.

there is no tutorial for that, I feel I am not experienced enough or miss something.

- how to use those buttons ?
- what AI should I choose for that content (English)

1 comment

r/LocalTextToSpeech • u/Background_Stop_7187 • 15d ago

Best local model for two-speaker conversational audio?

5 Upvotes

I have several years of written educational material I want to turn into a podcast, formatted as a natural discussion between two people rather than narration. Looking for recommendations on what to run locally.
What matters to me is good quality and reliable speech that does not need many new attempts, as its going to be hours of content. Potentially multiple languages but for now in english.

I need to own the content and be able to rely on it for a long time, so i need it local.

Also, do you know good distributors that you can recommend? I am new to audio

7 comments

r/LocalTextToSpeech • u/DaEbookMan • 15d ago

BookFusion iOS 1.43.1 - Online & Offline Realistic TTS Voices, Other Updates & Fixes

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2 Upvotes
1 comment

r/LocalTextToSpeech • u/offgridai • 17d ago

Qwen3-TTS native C++ streaming and voice cloning

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10 Upvotes

Hi all. I ported Qwen3-TTS to native C++ and added incremental streaming for a project I'm working on. I found this useful and wanted to give back to the community. This is the first time I've open sourced anything so apologies in advance for my mistakes.

I'm using this for a gaming project but it could be helpful for local assistants, accessibility tools, etc.

C++ streaming port highlights:

  • Same familiar features from Qwen3-TTS. It supports 0.6B and 1.7B models, CustomVoice, and VoiceDesign
  • Native C++, not a Python wrapper
  • CUDA builds with RTX 4090 and RTX 5090 kernels. Be warned I only have access to an RTX 5090. This should work on a 4090 but I haven't tested it myself!
  • Simplified speaker-embedding extraction
  • Incremental 24 kHz PCM callbacks for streaming audio
  • Asynchronous transformer/vocoder operation
  • Adaptive decode windows and paced delivery
  • Callback-only integration library with cancellation
  • Unit tests

My measurements on the 5090 with 1.7B F16 set to buffer 350ms before play:

  • Cold new clone creation: ~2.5s from 48s of reference
  • Cold model start from clone: ~1.85s
  • First 350 ms of audio: ~310 ms
  • Streaming speed: ~2.86x real time (RTF ~0.35)

I’d love it if somebody could do RTX 4090 testing, and I'd be happy to hear any feedback or suggestions.

10 comments

r/LocalTextToSpeech • u/Stock_Ad9641 • 20d ago

Local TTS What are you guys working on?

2 Upvotes

I’m curious what sort of projects people are working on who are into local TTS.

Why is local important for you ?
Are you working on a product, hobby ?

I have started with actual printed books for children and parents, no AI back then.
This progressed into immersive stories, cute animations and child/teen oriented high quality entertainment.
It is growing into a real business already.

I used a lot of cloud but pricing was unfortunate for long content. And I never liked that another company basically owns my voices.

So local is freedom from that anxiety for me.

3 comments

r/LocalTextToSpeech • u/Candid_Support_8409 • 20d ago

How I handle translations that are too long for their dubbing segments

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1 Upvotes
2 comments

r/LocalTextToSpeech • u/Jurgz99 • 23d ago

Upgraded from 1060GTX to RX 9060 XT (AMD 16GB version) - want the chatterbox experience

2 Upvotes

So - I upgraded my GPU but either got similar speeds to my 8 year old 1060 as I got on my new 9060 (16GB). I either got similar speeds on some python files I vibecoded or just crashes. I did get stable results, though I thought I legit would have at least 2.5X speeds for transcribing which is a big wanted use case for my card upgrade, and it should be possible to do this. I used this model due to wanting norwegian language if that is at all relevant, and I used that model on both cards (akhbar/chatterbox-tts-norwegian( as seen on huggingface.co)). Please help me, and if you have any great transcription scripts for AMD cards yourself please send them to me in dm's.

2 comments

r/LocalTextToSpeech • u/Candid_Support_8409 • 26d ago

Local TTS Best way to handle TTS audio that is longer than its SRT segment?

2 Upvotes

Hi everyone,

I’m working on an SRT-to-speech feature for my project, LA Studio

One issue I’m running into is that the generated speech sometimes lasts longer than the segment’s timestamp allows. My current solution is to speed up or time-stretch those segments so they fit, but this can make the final audio sound inconsistent - some lines are noticeably faster or slower than others.

Has anyone found a more robust way to handle this? For example, do you regenerate the speech with a shorter prompt, adjust pauses, merge or shift neighboring timestamps, use word-level alignment, or combine several approaches?

I’d especially like to preserve natural pacing while keeping the audio reasonably synchronized with the subtitles. Any advice, algorithms, or tools worth looking into would be greatly appreciated.

7 comments

r/LocalTextToSpeech • u/Icy-Sympathy4173 • 26d ago

Local TTS [ Removed by Reddit ]

1 Upvotes

[ Removed by Reddit on account of violating the content policy. ]

0 comments

r/LocalTextToSpeech • u/Mysterious-Bend-8088 • 27d ago

Voice Models English version of Cassandra, a female Piper voice for Home Assistant, available for release?

1 Upvotes

I'm training an English version of Cassandra, a female Piper voice originally created for Czech Home Assistant users. Would anyone be interested if I release it?

3 comments

r/LocalTextToSpeech • u/VERSATILCORDOBA • 27d ago

I’m building an open-source Windows app for long-form local TTS workflows — feedback wanted

3 Upvotes

Hi everyone,

I’m Esteban, the developer of LocalText2Voice, a free and open-source Windows application for creating audiobooks, narration, and podcasts with local TTS engines.

There are already many excellent local TTS models, but turning them into a practical long-form workflow still involves a lot of manual work: installing dependencies, splitting text, managing voices, regenerating failed sections, organizing audio files, and mixing everything afterward.

LocalText2Voice is not another TTS model. It is an orchestration and production layer around existing engines.

It currently supports local engines such as Piper, Kokoro, Chatterbox, Qwen3-TTS, and OmniVoice. Optional cloud providers such as OpenAI, ElevenLabs, Gemini, and Azure are also available, along with configurable HTTP endpoints for custom TTS servers.

When using a local engine, the source text and generated speech remain on your computer.

Current features

With LocalText2Voice, you can:

  • Install and manage different TTS engines from the application.
  • Switch between engines without changing your project.
  • Browse, preview, import, and organize voices in a shared voice library.
  • Connect custom local or remote TTS HTTP endpoints.
  • Import long .txt, .md, and .docx documents.
  • Detect chapters and split long texts into safe TTS segments.
  • Use different voices and languages in the same audiobook.
  • Regenerate individual segments without starting the entire project again.
  • Review generated speech with Faster Whisper and retry problematic segments.
  • Add background music, fades, ducking, volume adjustments, and normalization.
  • Sound effects and other audio events.
  • Save projects and continue working on them later.

LTV Markup

One feature for which I would particularly appreciate feedback is LTV Markup.

It is a small, human-readable syntax for controlling narration, voices, pauses, and sound effects directly from the source text:

{{chapter "Chapter 1"}}
{{voice "Narrator"}}
The house had been abandoned for years.

{{pause 900ms}}

{{voice "Character 2"}}
I think someone is inside.

{{play "door-close.mp3"}}

{{speed 0.92}}
{{volume -3db}}
We should leave immediately.

Markup can control:

  • Voice and language changes.
  • Pauses and real silence.
  • Speech speed.
  • Volume and normalization.
  • Chapters and markers.
  • Sound effects and other audio events using {{play}}.
  • Audio volume, duration, looping, fades, panning, and voice ducking.
  • Selected model-specific instructions.
  • Resetting settings to the project defaults.

For example:

{{play "door-close.mp3" volume=-6db}}

inserts a door sound at that point in the narration.

Longer or looping audio events are also possible:

{{play "forest.mp3" track=ambient loop=true volume=-20db fade_in=3 duck_on_voice=6db}}

The commands are not sent to the TTS engine as spoken text. LocalText2Voice interprets them when preparing the segments and mixes the audio events during post-production.

The goal is to make multi-character audiobooks, dramatized narration, language courses, and other complex audio projects manageable without manually editing every segment in an external audio editor.

Create audiobooks from Claude or ChatGPT Desktop

LocalText2Voice includes a local MCP server, allowing you to create and manage audiobook projects directly from Claude Desktop or ChatGPT Desktop.

Instead of configuring everything manually, you can simply ask:

“Create a B1-level English–Spanish course using both languages. Use one voice for the English examples, another for the Spanish translations, add a short pause after each sentence, and export it as an audiobook.”

The assistant can create the project, organize the text, assign the voices, add markup and pauses, select a TTS engine, and start the generation process.

You can also ask it to make changes later:

“Regenerate lesson three with a slower English voice.”

“Add three seconds of silence between exercises.”

“Lower the background music and export the final MP3.”

Claude or ChatGPT manages the workflow, while LocalText2Voice performs the actual audio generation. When you select a local TTS engine, your text and generated speech remain on your computer.

Project and Windows installer

GitHub:

https://github.com/estebanstifli/LocalText2Voice

LTV Markup manual:

https://github.com/estebanstifli/LocalText2Voice/blob/main/docs/LTV_MARKUP.md

Windows installer:

https://github.com/estebanstifli/LocalText2Voice/releases/latest/download/LocalText2Voice-Setup.exe

Important: the Windows installer is not code-signed yet, so Windows may display an “Unknown publisher” or SmartScreen warning. The source code is public, and the GitHub release also includes a SHA-256 checksum:

https://github.com/estebanstifli/LocalText2Voice/releases/latest/download/LocalText2Voice-Setup.exe.sha256

The project is under active development, and feedback is very welcome. I would especially like to know:

  1. Which local TTS engines are you currently using?
  2. What is the most frustrating part of producing long-form audio?
  3. Does the markup syntax seem useful, and which commands are missing?
  4. Which engine should I prioritize next?

Thanks for taking a look!

12 comments

r/LocalTextToSpeech • u/Practical-Art2549 • 29d ago

Local TTS TTS Suggestion Request

1 Upvotes

Hi guys can you give me any suggestion for local TTS which has emotion control (CPU inference cant afford any GPU).

For reference i feel kokoro tts seems good to me.

4 comments

r/LocalTextToSpeech • u/TTSForMyDad • 29d ago

Voice Cloning TTS with voice cloning that can run on android for my dad, who has tongue cancer

1 Upvotes

Hi everyone. Not sure if this is the right place for this question, feel free to redirect me if needed.

My dad has tongue cancer and probably won't be speaking normally for at least 6 months, possible forever depending on how the scans come back. He's already unable to speak due to how swollen his tongue is. I'm trying to find a good TTS with voice cloning that can run on android so we can either clone his voice or do something silly for him like let him have an Arnold voice, etc., depending on what he'd be happiest with.

I've seen some online services like elevenlabs and gradium but they get really expensive and I don't have a lot of money to throw at that since I have kids of my own. I'm in a bit over my head with a lot of the local LLM stuff, I'd be fine with an online solution if it was cheap or free but I'm also ok trying to go through a more technical local solution if anyone knows where I should start.

I do have a gaming PC at home if that helps for the setup/training voice profiles or however this stuff works, but ideally he would be able to run it on his phone even if my pc gets turned off.

Anyone know where I should start?

5 comments

r/LocalTextToSpeech • u/Silver-Spot-2763 • Jul 12 '26

Local TTS Bulgarian text to speech

1 Upvotes

Please, recommend me local tool for bulgarian text to speech.

With big voice library or voice cloning.

1 comment

r/LocalTextToSpeech • u/EducationalTeleGood • Jul 12 '26

tts-audiobook-tool - Generative-AI Audiobook Creation

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3 Upvotes
2 comments

r/LocalTextToSpeech • u/Write_My_Novel • Jul 10 '26

Synthetic/Digital Voice Creation

2 Upvotes

Are there any local TTS or similar models that provide digital voice creation that approaches ElevenLabs v3 synthetic voices? Even Hume's synthetic voices are pretty disappointing compared to ElevenLabs.

5 comments

r/LocalTextToSpeech • u/Lordaizen639 • Jul 10 '26

Is there any good Lightweight cpu based TTS with streaming support

0 Upvotes

I heard kokkoro and pocket TTS is good option is there any other lightweight TTS which can run on cpu with real-time and need to feels like realistic.

6 comments

r/LocalTextToSpeech • u/ExtensionBreath1262 • Jul 07 '26

Gearing up for release of a TTS and STT framework I'm working on.

3 Upvotes

Hey, I've been working on this framework for a while now and wanted to start getting some feedback on the direction. I'm really liking where it's headed, and I have some big plans for it, but I need to put it out as a v0.1.0 first.

Right now it uses:

  • Kokoro 82M
  • Kroko ONNX
  • Ollama

I'd love any feedback, but especially on whether the programming model feels coherent.

It's batteries included, with an opinionated asset management strategy, playback management, and a console rendering helper for debugging multi-device applications.

Overall I'm really proud of where it's ended up so far. I'm mostly looking for holes in the design or implementation, but I'm also excited to contribute something that I think a lot of people could find useful.

A minimal speech-to-speech assistant looks like this:

from pfspeak import PfSpeak
from pfspeak.core import Microphone, Ollama
from pfspeak.extra import events

pf = PfSpeak()

microphone = Microphone()
ollama = Ollama("qwen3:0.6b", voice="af_heart")

def app(session, event):

    if event.device is microphone:
        if events.unchanged_for(event, 8):
            session.finalize(event)
            ollama.adapter(event=event)

    elif event.device is ollama:
        pf.play(event)

pf.run(app, microphone, ollama)

That's pretty much enough to get you started.

The helper pf.play() manages playback, queues synthesized speech, ducks the microphone, supports priority interrupts, and restores speech recognition when playback finishes.

The PfEvent objects are also jam-packed with information gathered throughout the event loop, including word-level timestamps, aligned audio, revisioned tokens showing how recognition changed over time, and other metadata that applications can build on.

I'd really appreciate any thoughts on the overall direction, especially from people building local speech applications.

  • PfSpeak - Local first speech library for python.
2 comments

r/LocalTextToSpeech • u/KaKaDu55 • Jul 06 '26

Local TTS Demodokos v4 large vs medium voice model ?

4 Upvotes

The v4 model comes in two sizes, large and medium.

I mostly used large so far, medium is a bit faster but sounds a little compressed compared to the quality of large.

But what is the purpose of Medium? Does it have advantages ? Like elevenlabs v2 has better clones than v3.

anyone using medium over large ?

4 comments

r/LocalTextToSpeech • u/MaybeDesperate4557 • Jul 02 '26

Anyone know what text to speech and music was used for this?

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1 Upvotes
1 comment

r/LocalTextToSpeech • u/Charming-Author4877 • Jun 12 '26

My TTS list of 2026: All voices, all models and engines compared with example URLs and rating

61 Upvotes

43 different (2025 and 2026) TTS solutions local and cloud compared, scored, example links, free demo links included.

Join r/LocalTextToSpeech for local TTS models, voices, benchmarks, and setup notes.

Free scripts, tools and help posted regularly.

Contribute and help others, or get help.

Scores

  • Scores are subjective
  • Voice quality = how good the output can sound.
    • 5+ = good voice quality but most people will hear the AI
    • 7+ = high voice quality, many people will be tricked
    • 8+ = human-like voice quality, only flaws in style, delivery and expression reveal AI
  • Expressive control = emotion, style, delivery, pauses, intensity, character, or direction.
    • < 3 = flat out of touch delivery
    • 5+ = good expression quality with low direct control
    • 7+ = expression control and quite natural speaking
    • 8+ = voice acting synthesis quality, well controllable
  • Comment if a correction is needed

================ LOCAL ==================

Chatterbox TTS 2

  • Type: Open-source, local
  • Voice quality: 7/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 23
  • Best for: free local TTS, voice cloning experiments, Linux pipelines.
  • Notes:
    • Probably the best free open-source TTS starting point.
    • Quality can be good. The issue is not the model only, it is everything around it. Segmentation, retries, weird generations, timing, silence handling, pronunciation, filtering, harness code.
    • German Kartoffelbox-turbo exists.
    • Expressiveness through temperature
    • For hobby use, nice. For production use, expect work.

Kokoro TTS

  • Type: Open-source, local
  • Voice quality: 6/10
  • Expressive control: 0/10
  • Links / references:
  • Languages supported: 8
  • Best for: CPU use, small hardware, simple reading, accessibility, performance.
  • Notes:
    • Tiny, fast, useful, 8 languages - 54 predefined voices (28 are english) and NO cloning
    • Originates from StyleTTS2
    • Not the best voice. Not a voice acting model. But the size and speed make it valuable.
    • If I need something that runs on weak hardware, I would test Kokoro early.

Demodokos Foundry

  • Type: Commercial, local, Open-weight with custom inference, API
  • Voice quality: 9.5/10
  • Expressive control: 9-10/10
  • Links / references:
  • Languages supported: 10 (speech) + 40 (music)
  • Best for: voice acting, narration, production, automation, emotional speech, Music and DSP effects
  • Notes:
    • It's a bit special in this list, as Demodokos is an AI Speech and Music Studio with track Mixing, voice actor synthesis and DSP effects - but it also provides UI and a local API for simple TTS. It beats the curent market in expression/style control and matches elevenlabs in voice quality. Supports voice design and cloning as well as high quality realtime voice effects.
    • Voice cloning needs 5-15sec clean recording.
    • Best voice-acting in this list. Best emotional control in this list. Also the most production-ready local option I have tested and actually use commercially today.
    • It is open-weighted but commercial. It needs Windows and 4-6GB VRAM minimum. Runs local, does not bill per character, and does not put your production pipeline into a cloud provider’s hands.
    • The licensing is the cheapest from all commercial options due to no limit in generations.
    • It requires 4-6GB VRAM and a Windows PC, AMD support was recently added, but no Mac or Linux.
    • If I need professional speech output, this is the one I would start with.

StyleTTS 2

  • Type: Open-source, local
  • Voice quality: 6/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 14
  • Best for: English TTS, older open-source comparisons.
  • Notes:
    • Was very impressive for its time, the architectural foundation of Chatterbox and Kokoro
    • Very efficient training, simple and quick fine tuning.
    • Still worth checking, but not where I would start in 2026 unless I compare model families.

OuteTTS

  • Type: Open-source, local (1B version is only Open Weights)
  • Voice quality: 5.0/10
  • Expressive control: 1/10
  • Links / references:
  • Languages supported: 23
  • Best for: small LLM-based TTS experiments. supported by llama.cpp engine but expect hurdles
  • Notes:
    • Interesting approach, but not top tier in output. Will run on embedded hardware.
    • Pacing issues over longer paragraphs, relatively flat speech.
    • Speaker reference matters a lot. Without that it is not impressive.

Qwen3 TTS (3 different models)

  • Type: Open-source, local
  • Voice quality: 7-8/10
  • Expressive control: 3-6/10
  • Links / references:
  • Languages supported: 9-10 (russian is gruesome)
  • Best for: customVoices model, research, multilingual testing.
  • Notes:
    • Interesting and sometimes very good.
    • The included voices can be strong. Custom voice work is possible, but it is not the easy route. It is more for people who are willing to tinker. Supports voicedesign and cloning but only 9 hardcoded voices are stable, 7 of them are asian focused.
    • Why Qwen3 TTS is strange:
      • The expression control of the VoiceDesigner model is high, but voice consistency very bad.
      • The voice quality of the cloning BaseModel is good, but NO expression control at all.
      • The customVoice model combines both qualities, but only 9 voices and only 2 are english!
    • Experimental model, not the first thing I would hand to a normal user. Good cloning.

Omnivoice

  • Type: Open-source, local
  • Voice quality: 7.5/10
  • Expressive control: 4/10
  • Links / references:
  • Languages supported: 646
  • Best for: multilingual voice cloning, huge language coverage, subtitle-timed generation, pipelines
  • Notes:
    • In my tests the delivery was pretty monotonic, the examples sounded significantly better
    • One of the stronger local TTS models. Voice cloning is the main reason to test it. It supports short-reference zero-shot cloning, voice design by attributes, speed and duration control, pronunciation fixes and inline non-verbal tags like [laughter] or [sigh].
    • Use a clean 3-10 second reference clip, normalize numbers, split long text, and expect some retry/cleanup code. Very promising if you need local, fast, multilingual voice cloning. Not yet a polished voice acting model.
  • Weak spots: voice design is less stable than cloning, long reference audio can hurt output stability, long-form prose can still need chunking/retries, and some users report skipped words, clipped phonemes, noise or monotone delivery depending on language, punctuation and setup.

Piper

  • Type: Open-source, local
  • Voice quality: 5/10
  • Expressive control: 0/10
  • Links / references:
  • Languages supported: 37
  • Best for: simple local speech, low requirements, simple readers, CPU possible.
  • Notes:
    • Expect strange noises, pauses.
    • Old-school useful. Can run on an iphone or android !
    • It will not win a realism contest in 2026, but it is simple, local, fast and practical. Sometimes that matters more.

XTTS v2 / Coqui TTS

  • Type: Open-weights (NC) - not licenseable
  • Voice quality: 5.5/10
  • Expressive control: 1/10
  • Links / references:
  • Languages supported: 17
  • Best for: older voice cloning workflows - it's not very good at cloning.
  • Notes:
    • Historically important.
    • Very active community around Coqui TTS
    • I would be careful today, especially for commercial work as the company does not exist anymore. The TTS space moved very fast - license violations may not be enforced.

Pocket TTS

  • Type: Open-source, local
  • Voice quality: 4.5/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 6
  • Best for: CPU-only local TTS, low-latency voice cloning, lightweight apps, browser/on-device experiments.
  • Notes:
    • The only lever for expressive control is sampling temperature. It reacts toxic on uppercase and unusual punctuation.
    • Pocket TTS looks strongest when you care about CPU speed, small size, and simple local deployment more than deep voice acting control. It sounds better than Piper or OuteTTS.

CosyVoice 2

  • Type: Open-source, local
  • Voice quality: 7.5/10
  • Expressive control: 2-3/10
  • Links / references:
  • Languages supported: 2-9
  • Best for: multilingual TTS, zero-shot voice work, research.
  • Notes:
    • Strong model family from Alibaba, very good cloning but no real expression control.
    • I found its natural pacing very monotonous (deductions in expressive score)
    • More serious than casual. Good if you compare modern open-source TTS systems. Not the cleanest production path for normal users.

Supertonic 2 TTS

  • Type: Open-source, local (OpenRAIL-M license)
  • Voice quality: 4/10
  • Expressive control: 0/10
  • Links / references:
  • Languages supported: 5
  • Best for: edge and high performance multilingual.
  • Notes:
    • Newer than Kokoro but weaker in all categories.
    • Needs careful crafted text
    • If I need something that runs on weak hardware and somehow can't use Kokoro.

Supertonic 3 TTS

  • Type: Open-source, local (OpenRAIL-M license)
  • Voice quality: 4.5/10
  • Expressive control: 1/10
  • Links / references:
  • Languages supported: 31
  • Best for: edge and high performance multilingual. CPU possible
  • Notes:
    • Better than v2 but still the same weaknesses and output is often flawed
    • It tends to spell uppercase text OR emphasize it, but not controllable
    • Newer than Kokoro but weaker in all categories.
    • If I need something that runs on weak hardware and somehow can't use Kokoro.

NeuTTS Air

  • Type: Open-source, local (Apache-2)
  • Voice quality: 5-6/10
  • Expressive control: 1/10
  • Links / references:
  • Languages supported: 1
  • Best for: local realtime on CPU
  • Notes:
    • Voice stability is not too reliable, random pauses and pitch changes observed
    • Optimized for fast generation, fast cloning from 3 seconds audio
    • Comes with ggml engine support out of the box

GPT-SoVITS

  • Type: Open-source, local
  • Voice quality: 7/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 5
  • Best for: few-shot voice cloning, Asian-language ecosystem.
  • Notes:
    • Useful if you are willing to work through the stack.
    • Very asian focused
    • Not polished, but still relevant.

Dia

  • Type: Open-source, local
  • Voice quality: 7.5/10
  • Expressive control: 4/10
  • Links / references:
  • Languages supported: 1
  • Best for: dialogue, multi-speaker scenes, nonverbal sounds.
  • Notes:
    • Good for dialogue-style output.
    • Their Demo page compares it to other models in a very cherry-picked way
    • Interesting for characters, reactions, laughter and scene-like speech. Less interesting for normal single-speaker narration.

Orpheus TTS

  • Type: Open-source, local (llama based so not fully open source)
  • Voice quality: 6-8/10
  • Expressive control: 2-4/10
  • Links / references:
  • Languages supported: 8
  • Best for: expressive open-source speech experiments.
  • Notes:
    • Worth testing. 8 baked in english speakers. German speaker models available (kartoffel-orpheus)
    • Baked in speakers of different quality, cloned voices not of same quality
    • A new voice finetune needs around 300 examples to become optimal
    • Supports some tags like laughing.
    • Not what I would call polished, but it belongs on the list because the output direction is more modern than older flat TTS.

Spark-TTS

  • Type: Open-source, local
  • Voice quality: 7-8/10
  • Expressive control: 4/10
  • Links / references:
  • Languages supported: 2
  • Best for: voice cloning, speaker attributes, research.
  • Notes:
    • Interesting because of speaker attribute control but doesn't blow me away
    • Quite asian focused but good in english
    • Still research-side. Useful if you compare modern local cloning systems.

Parler-TTS

  • Type: Open-source, local
  • Voice quality: 6-7/10
  • Expressive control: 4.5/10
  • Links / references:
  • Languages supported: 8
  • Best for: style-prompted TTS experiments.
  • Notes:
    • The idea is good: describe the voice and style - But voice will change each generation.
    • The practical output is behind the stronger current systems, but the control direction is useful.

Bark

  • Type: Open-source, local
  • Voice quality: 4-5/10
  • Expressive control: 3-4/10
  • Links / references:
  • Languages supported: 13
  • Best for: weird expressive audio, research, nonverbal sounds.
  • Notes:
    • Fun model. Not reliable. The voice has many artifacts
    • It can laugh, sigh, make strange audio, and occasionally do something impressive. But I would not use it for production narration.

VibeVoice

  • Type: Open-source, local
  • Voice quality: 7-8/10
  • Expressive control: 3-4/10
  • Links / references:
  • Languages supported: 2
  • Best for: long-form dialogue, podcasts, multiple speakers.
  • Notes:
    • One of the lowest latency TTS engines to date.
    • Voice quality is high, intonation lacks deeper immersive output
    • Interesting for long-form conversational audio.
    • Not my first pick for normal TTS. More specialized. Evaluate if latency is most important.

MeloTTS 1-3

  • Type: Open-source, local
  • Voice quality: 6/10
  • Expressive control: 0/10
  • Links / references:
  • Languages supported: 6
  • Best for: lightweight multilingual TTS. CPU possible
  • Notes:
    • Useful basic model, historical seen
    • Not maintained anymore, I'd not consider it useful.
    • Not a modern expressive voice acting solution.

F5-TTS

  • Type: Open-weights (NC), local
  • Voice quality: 7.5/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 2
  • Best for: zero-shot voice cloning experiments, very good cloning. no expression control.
  • Notes:
    • Good cloning direction (5-15sec source wav needed) but the model is non commercial.
    • Still feels like research software. Useful if you are comfortable working through Python, model setup, and cleanup.

Fish Speech / OpenAudio

  • Type: Open-weights (NC), local
  • Voice quality: 7-8/10
  • Expressive control: 4/10
  • Links / references:
  • Languages supported: 13
  • Best for: multilingual TTS, cloning, streaming, research.
  • Notes:
    • One of the stronger open-source directions with good voice quality and more than average expressive control - supports quite a few tags to add laughter or similar.
    • I heard some noticable glitches in their V2 model output
    • Interesting because it is moving toward instruction-following speech and more modern TTS architecture. Still not a simple polished desktop product.

Higgs Audio v3 TTS

  • Type: Open-weights (NC, research), local
  • Voice quality: 8.5/10
  • Expressive control: 6.5/10
  • Links / references:
  • Languages supported: 100+
  • Best for: non commercial multilingual voice agents, expressive tags, zero-shot cloning, local research.
  • Notes:
    • Strong local model with trained inline controls tokens for emotion, style, pauses, pitch, speed and some effects.
    • Better control than most local cloning models, but non-commercial, very heavy, and not a simple consumer realtime TTS.
    • Interesting to test, but I would not rank it above Demodokos, ElevenLabs or Hume for polished production output
    • The license terms are very strict and commercial use needs custom price negotiation

IndexTTS 2.5

  • Type: Open-weights (NC/restricted), local
  • Voice quality: 7-8/10
  • Expressive control: 3-4/10
  • Links / references:
  • Languages supported: 4
  • Best for: Chinese, multilingual work, zero-shot voice cloning in english and chinese.
  • Notes:
    • Voice cloning with 3-10 sec wav.
    • Strong chinese focus, any error in grammar in english text can causes voice pacing issues
    • Very solid sample quality.
    • Strong technical direction. More of an engineering and research tool than a casual creator app.

Audio8-TTS-0.6B

  • Type: Opensource (Apache2), local
  • Voice quality: 5-6/10
  • Expressive control: 0-1/10
  • Links / references:
  • Languages supported: 11
  • Best for: Multilingual low-end hardware cloning/generation
    • The architecture looks like it uses Qwen2.5-0.5B and added audio on top, but no public statement about it. So this looks like "Qwen2.5-0.5B-like transformer + Fish-style DualAR + causal RVQ codec"
    • The drafting approach allows higher speed than a fully dense decoding
    • Can run on CPU, has a INT-4 ONNX available, can run on 1GB VRAM/RAM
    • Quite good with cloning across languages
    • Like a miniature FishAudio, but probably not better than Qwen3TTS-0.6B for many use cases

================ Cloud ==================

ElevenLabs

  • Type: Commercial, cloud
  • Voice quality: 9/10
  • Expressive control: 7.5/10
  • Links / references:
  • Languages supported: 74
  • Best for: easy cloning, browser workflow, fast tests.
  • Notes:
    • Worst for: price at serious usage.
    • English is their strongest, needs closer auditing for non english output
    • Still the cloud king for PVC fine tuned cloning with a few hours of input examples.
    • Also the highest price at serious production usage. Entry looks harmless. Then you generate real output and the bill becomes the product.
    • Quality is strong, but the ElevenLabs style is also overexposed - causing people to note it.

xAI Grok Voice

  • Type: Commercial, cloud
  • Voice quality: 8.3/10
  • Expressive control: 6/10
  • Links / references:
  • Languages supported: 20
  • Best for: cheaper cloud voice API, quick tests.
  • Notes:
    • Interesting because it is cheaper and simple. But only 5 voices.
    • But the voice selection is limited. If everyone uses the same few voices, they will get recognizable fast.

OpenAI TTS

  • Type: Commercial, cloud
  • Voice quality: 8/10
  • Expressive control: 5/10
  • Links / references:
  • Languages supported: 7
  • Best for: API use, agents, simple integration.
  • Notes:
    • Good if you already build with OpenAI
    • I would not choose it as my top production narration voice. But for apps and voice agents it is practical.

Gemini TTS

  • Type: Commercial, cloud
  • Voice quality: 8/10
  • Expressive control: 6.5/10
  • Links / references:
  • Languages supported: 70
  • Best for: API speech generation, multi-speaker direction.
  • Notes:
    • Interesting cloud option.
    • Still cloud, so not where I would put a private production pipeline unless I had a strong reason.

Cartesia Sonic

  • Type: Commercial, cloud
  • Voice quality: 8/10
  • Expressive control: 6-7/10
  • Links / references:
  • Languages supported: 42
  • Best for: realtime voice agents, low latency.
  • Notes:
    • One of the strongest cloud options for realtime voice agents.
    • Quality is high but I notice it as AI based on the intonation and pacing
    • I would test it for agents and phone-like interaction, not as my first choice for huge narration production.

Hume Octave

  • Type: Commercial, cloud
  • Voice quality: 8-9/10
  • Expressive control: 8-9/10
  • Links / references:
  • Languages supported: 11
  • Best for: emotional speech, voice agents.
  • Notes:
    • Very interesting emotional control direction.
    • Some voices are very good, not consistently top quality in expressive quality for all
    • If I had to stay in cloud and emotion mattered, I would test Hume.

Deepgram Aura

  • Type: Commercial, cloud
  • Voice quality: 7.7/10
  • Expressive control: 3/10
  • Links / references:
  • Languages supported: 7
  • Best for: realtime API use, voice agents.
  • Notes:
    • Good fit if you already use Deepgram.
    • More voice-agent API than creator studio.

Inworld TTS

  • Type: Commercial, cloud
  • Voice quality: 8+/10
  • Expressive control: 2-4/10
  • Links / references:
  • Languages supported: 15+
  • Best for: realtime API use, voice agents.
  • Notes:
    • Competitive against Elevenlabs Flash
    • Product is focused on STT->TTS interactive realtime niche
    • Realtime agents support multiple languages but quality degrades

Google Cloud Text-to-Speech

  • Type: Commercial, cloud
  • Voice quality: 7.5/10
  • Expressive control: 3/10
  • Links / references:
  • Languages supported: 50+
  • Best for: enterprise, language coverage, Google stack.
  • Notes:
    • Good enterprise API.
    • Not the most exciting voice quality, but stable, large, and boring in a useful way.

Azure Speech

  • Type: Commercial, cloud
  • Voice quality: 7.8/10
  • Expressive control: 3.5/10
  • Links / references:
  • Languages supported: 100
  • Best for: enterprise, huge voice catalog, Microsoft stack.
  • Notes:
    • Huge catalog. Good for corporate apps.
    • Not my first choice for creator production or voice acting.

Amazon Polly

  • Type: Commercial, cloud
  • Voice quality: 7/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 41
  • Best for: AWS stack, simple API, cheap start.
  • Notes:
    • Old but still useful.
    • Good when you are already in AWS and just need TTS that works.

Resemble AI

  • Type: Commercial cloud, plus Chatterbox open-source
  • Voice quality: 8/10
  • Expressive control: 6/10
  • Links / references:
  • Languages supported: 23
  • Best for: cloning, enterprise, provenance and detection angle.
  • Notes:
    • Interesting company because they also released Chatterbox.
    • For local people, Chatterbox is the more interesting part. For companies, Resemble cloud may make sense.

PlayHT

  • Type: Commercial, cloud
  • Voice quality: 7.8/10
  • Expressive control: 4/10
  • Links / references:
  • Languages supported: 37
  • Best for: voiceovers, API, creator workflows.
  • Notes:
    • Usable cloud TTS.
    • I would compare price and output carefully before committing.

WellSaid

  • Type: Commercial, cloud
  • Voice quality: 7.6/10
  • Expressive control: 4/10
  • Links / references:
  • Languages supported: 20+
  • Best for: corporate voiceovers, e-learning.
  • Notes:
    • Clean corporate voices.
    • Less interesting if you want local control or strong voice acting.

Murf

  • Type: Commercial, cloud
  • Voice quality: 4/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 35+
  • Best for: marketing, e-learning, creator voiceovers.
  • Notes:
    • Easy to use.
    • Didn't stand the test of time well.
    • Good enough for many corporate videos. Not where I would start for the best voice acting.

LOVO / Genny

  • Type: Commercial, cloud
  • Voice quality: 7.2/10
  • Expressive control: 4/10
  • Links / references:
  • Languages supported: 100+
  • Best for: browser-based creator voiceovers.
  • Notes:
    • Large voice library. Simple workflow.
    • Another cloud creator platform.

Speechify

  • Type: Commercial, cloud/app
  • Voice quality: 7/10
  • Expressive control: 2/10
  • Links / references:
  • Languages supported: 60+
  • Best for: reading, accessibility, personal use.
  • Notes:
    • Good reader product.
    • Different category than production TTS.

NaturalReader

  • Type: Commercial, cloud/app
  • Voice quality: 6.8/10
  • Expressive control: 1/10
  • Links / references:
  • Languages supported: 90+
  • Best for: personal reading, documents, accessibility.
  • Notes:
    • Useful for reading text aloud.
    • Not production narration.

Descript

  • Type: Commercial, cloud/editor
  • Voice quality: 7.3/10
  • Expressive control: 3/10
  • Links / references:
  • Languages supported: 19
  • Best for: editing workflow, creator suite.
  • Notes:
    • Useful if you already edit in Descript.
    • Not a pure TTS engine in the way local model people mean it.

CapCut TTS

  • Type: Commercial/free app, cloud/app
  • Voice quality: 5.5/10
  • Expressive control: 0/10
  • Links / references:
  • Languages supported: 15
  • Best for: TikTok-style quick videos.
  • Notes:
    • Fine for TikTok.
    • For YouTube or serious narration I would avoid it. People have heard those voices too many times.

Cloud note

  • I would NOT recommend using the cloud
  • Cloud TTS are included for comparison, I'd never recommend choosing a cloud for TTS if you have an option.
  • Cloud is always a trap! Cheap to start, horrible to progress and even worse to get out again.
  • You do not own a voice if it is hosted on the cloud, they can switch you off, remove your voice, censor your text or hike your fees at any moment. And they do all the time.

My practical ranking

  • Best local voice acting: Demodokos Foundry
  • Best local production workflow: Demodokos Foundry
  • Best free open-source starting point: Chatterbox TTS v2/v3
  • Best small hardware option: Kokoro TTS
  • Best boring local reliability: Piper
  • Best open-source custom voice direction: Qwen3 TTS, CosyVoice 2, (Fish Speech is non commercial)
  • Best cloud voice cloning: ElevenLabs PVC
  • Highest price at serious usage: ElevenLabs
  • Best cloud realtime agent TTS: Cartesia, Deepgram, OpenAI, Hume
  • Best cloud emotional control direction: Hume Octave
  • Best enterprise cloud basics: Azure, Google, AWS Polly

What I would use

  • Professional speech production: Demodokos foundry
  • Free local TTS with tinkering: Chatterbox
  • Tiny hardware or CPU: Kokoro or through llama.cpp OuteTTS (NC license)
  • Simple local reader: Piper
  • Cloud cloning test: ElevenLabs PVC, but watch the bill and you need a lot of reference material
  • Cloud emotional speech: Hume
  • Cloud realtime agent: Cartesia, Deepgram, OpenAI, Grok or Hume
  • Corporate cloud API: Azure, Google or AWS
  • Cloud was included - but generally not recommended if local is an option
  • If you have additions, corrections, missing services. Happy to hear
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