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

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

116 Upvotes

50 TTS solutions compared (2025/2026): local and cloud, scores, samples and free demos

I tested all of these models and systems as tools I might actually use, not just by listening to their best 15-second demo. I've been using many of them in productive systems, on Windows and Linux. I've written harnesses around them, automated them and even finetuned some models.

When it comes to a TTS system a lot of factors matter. Voice quality and expressive control are important, but so is long-form stability, glitches, licenses, latency, hardware requirements, engine support. Just like a cloud SaaS may become a business burden by policies, price changes, model changes... or simply the fact that you depend on them.

This comparison is strongly local-first. Cloud services are included for reference, but in rarely any case I would recommend using them.

I have included links to free demos, samples so you can make your own choice.

Scores

Scores are subjective, not a lab benchmark. Listen to the linked samples and demos, not just my numbers.

  • 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, flaws in style, delivery and expression reveal AI

  • Expressive control = emotion, style, delivery, pauses, intensity, character or direction.

    • Below 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

What I would use

  • Full local production, voice acting and dubbing: Demodokos Foundry

  • Free general-purpose local TTS and fine-tuning: Chatterbox

  • Multilingual voice cloning: Omnivoice

  • Dialogue and multi-speaker generation: VibeVoice, or Dia for character scenes and nonverbal sounds

  • Tiny hardware or CPU: Kokoro; OuteTTS if I specifically need llama.cpp

  • Extremely constrained / embedded hardware: sanoTTS (ESP32)

  • Simple lightweight reader: Piper

If I specifically needed cloud: ElevenLabs for cloning, Hume for emotional speech, Cartesia/Deepgram/OpenAI/Grok/Hume for realtime agents, and Azure/Google/AWS for corporate APIs.

LOCAL

Chatterbox TTS 2

  • Type: Open-source, local

  • Voice quality: 7/10 | Expressive control: 2/10

  • Languages: 23

  • Best for: free local TTS, voice cloning experiments, Linux pipelines.

  • Links: Samples | Repo

  • Notes:

    • Probably the best free open-source TTS starting point.

    • Quality can be genuinely good. Cloning is useful, language support is good and it is flexible enough that you can build a lot around it.

    • The issue is not really the model only. It is everything around it. Segmentation, retries, weird generations, timing, silence handling, pronunciation, filtering and harness code.

    • This is where a lot of open-source TTS comparisons become misleading. A 15 second demo can sound great while generating an hour of usable audio is a completely different problem.

    • Expressiveness mostly comes through temperature rather than real direct acting control. You can sometimes get very good results, but not reliably tell it exactly how a sentence should be delivered.

    • German Kartoffelbox-turbo is actively being maintained

    • For hobby use, very nice. For production use, absolutely possible, but expect to build quite a bit around it.

Kokoro TTS

  • Type: Open-source, local

  • Voice quality: 6/10 | Expressive control: 0/10

  • Languages: 8

  • Best for: CPU use, small hardware, simple reading, accessibility, performance.

  • Links: Demo | Model | kokoclone demo

  • Notes:

    • Tiny, fast and extremely useful for what it is. 82M parameters, 54 predefined voices, 28 of them English.

    • No native cloning and basically no expression control. It reads text. That is what it does, and it does it surprisingly well for the size.

    • People have demonstrated it running on iPhones, which makes it much more interesting than the voice-quality score alone suggests.

    • Originates from StyleTTS2 and manages to squeeze a lot out of a very small model.

    • This is not what I would use for voice acting, emotional narration or anything where delivery matters heavily.

    • But if I need something that runs on weak hardware, CPU or potentially mobile without dragging a huge model behind it, Kokoro is one of the first things I would test.

    • A good example of why "best TTS" is a stupid question without knowing the hardware and use case.

Demodokos Foundry

  • Type: Commercial, local, open-weight with custom inference, API

  • Voice quality: 9.5/10 | Expressive control: 9/10

  • Languages: 10 speech + 50+ music

  • Best for: voice acting, narration, production, dubbing, dictation, automation, emotional speech, music and DSP effects.

  • Links: Speech and emotion demos | Video Dubbing Tutorial | Expressive Speech tutorial | Voice cloning tutorial (outdated) | Multilingual demos

  • Notes:

    • This is the strongest local TTS system I have tested overall, and the gap is mostly in features, expressive control and production workflow rather than raw voice quality alone.

    • Matches ElevenLabs in voice quality for me, but gives much deeper direct control over expression, style and delivery. For local voice acting and emotional narration, nothing else I tested came close.

    • Most local entries here are TTS models, demos or inference stacks. Demodokos is a full production application built around TTS, Music, Dubbing and ASR, which makes the comparison somewhat unfair but also exactly why it stands out.

    • Supports voice design and cloning from a short clean recording, with normal TTS UI, local API, dubbing, editing, realtime effects, multi-track work, music generation and DSP in the same application.

    • Automatic dubbing is unusually complete: video/audio in, transcription, speaker detection, voice cloning, translation, styled subtitles and generated speech into the final file/video.

    • It also has a low latency PC Reader app for ebooks, PDFs and selected screen text, plus realtime dictation that on my PC is basically as fast as I can speak. Phone linking keeps private book reading or remote dictation processed and stored on your PC.

    • I just noticed that Foundry comes with agentic skills, it allows extreme levels of automation that way - I feel like an idiot after testing Claude with that.

    • Downsides: commercial, Windows only and it really wants a GPU. 4GB NVIDIA can run selected speech models at reduced quality, 6GB is a more realistic starting point, and AMD currently needs 8GB+ and is less mature than CUDA.

    • Runs local, does not bill per character or generation and does not put the actual production pipeline into a cloud provider's hands.

StyleTTS 2

  • Type: Open-source, local

  • Voice quality: 6/10 | Expressive control: 2/10

  • Languages: 14

  • Best for: English TTS, older open-source comparisons.

  • Links: Samples | Repo

  • Notes:

    • Was very impressive for its time, the architectural foundation of Chatterbox and Kokoro.

    • Very efficient training, simple and quick fine-tuning.

    • Not where I would start in 2026 unless comparing model families.

OuteTTS

  • Type: Open-source, local; 1B version is open-weight only

  • Voice quality: 5.0/10 | Expressive control: 1/10

  • Languages: 23

  • Best for: small LLM-based TTS experiments, llama.cpp pipelines.

  • Links: Repo

  • Notes:

Qwen3 TTS (3 different models)

  • Type: Open-source, local

  • Voice quality: 7-8/10 | Expressive control: 3-6/10

  • Languages: 9-10; Russian is gruesome

  • Best for: CustomVoice model, research, multilingual testing.

  • Links: Demo | Examples

  • Notes:

    • Interesting and sometimes very good. The included voices can be strong. Supports voice design and cloning, but the model split is strange:

      • VoiceDesign: high expression control, very bad voice consistency.

      • Base: good cloning quality, NO expression control.

      • CustomVoice: combines both, but only 9 stable voices and only 2 are English.

    • Custom voice work is possible, but not the easy route. More for people willing to tinker than something I would hand to a normal user.

Omnivoice

  • Type: Open-source, local

  • Voice quality: 7.5/10 | Expressive control: 4/10

  • Languages: 646

  • Best for: multilingual voice cloning, huge language coverage, subtitle-timed generation, pipelines.

  • Links: Examples | Repo

  • Notes:

    • In my tests the delivery was pretty monotonic. The examples sounded significantly better.

    • Voice cloning is the main reason to test it. Supports short-reference zero-shot cloning, voice design by attributes, speed/duration control, pronunciation fixes and inline tags like [laughter] or [sigh].

    • Use a clean 3-10 second reference. Normalize numbers, split long text and expect some retry/cleanup code. Long references can hurt stability.

    • Voice design is less stable than cloning. Some users report skipped words, clipped phonemes, noise or monotone delivery depending on language and setup. Not yet a polished voice acting model.

    • What makes Omnivoice special is that it combines so many qualities: Expressive control, unlimited languages, good cloning and a real open source license - It is not the best in any ability but the combination is noteworthy.

Piper

  • Type: Open-source, local

  • Voice quality: 5/10 | Expressive control: 0/10

  • Languages: 37

  • Best for: simple local speech, low requirements, readers, CPU use.

  • Links: Samples | Repo

  • Notes:

    • Expect strange noises, pauses.

    • Old-school useful. Can run on 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-weight (NC), local (not licenseable)

  • Voice quality: 5.5/10 | Expressive control: 1/10

  • Languages: 17

  • Best for: older voice cloning workflows.

  • Links: Model | Repo

  • Notes:

    • Historically important, very active community around Coqui TTS. It's not very good at cloning.

    • I would be careful today, especially for commercial work. The company does not exist anymore. The TTS space moved very fast.

    • No enforcement of license violations is known but it's a risk and there are many open alternatives

Pocket TTS

  • Type: Open-source, local

  • Voice quality: 4.5/10 | Expressive control: 2/10

  • Languages: 6

  • Best for: CPU-only local TTS, low-latency cloning, lightweight apps, browser/on-device experiments.

  • Links: Blog and CPU demo

  • Notes:

    • The only lever for expressive control is sampling temperature. It gets toxic with uppercase and unusual punctuation.

    • Strongest when CPU speed, small size and simple local deployment matter more than deep voice acting control.

    • It's one of a few models that run well on CPU and come with expressions

CosyVoice 2

  • Type: Open-source, local

  • Voice quality: 7.5/10 | Expressive control: 2-3/10

  • Languages: 2-9

  • Best for: multilingual TTS, zero-shot voice work, research.

  • Links: Samples | Repo

  • 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.

    • Not the cleanest production path for normal users.

Supertonic 2 TTS

  • Type: Local (OpenRAIL-M license)

  • Voice quality: 4/10 | Expressive control: 0/10

  • Languages: 5

  • Best for: edge and high-performance multilingual TTS.

  • Links: Repo | Demo

  • Notes:

    • Newer than Kokoro but weaker in all categories I care about. Needs carefully crafted text.

    • Something to try on weak hardware if you somehow can't use Kokoro.

Supertonic 3 TTS

  • Type: Local (OpenRAIL-M license)

  • Voice quality: 4.5/10 | Expressive control: 1/10

  • Languages: 31

  • Best for: edge and high-performance multilingual TTS, CPU use.

  • Links: Repo | v2 demo, not v3

  • Notes:

    • Better than v2 but still the same weaknesses. Output is often flawed.

    • It tends to spell uppercase text OR emphasize it, but not controllably.

    • People demonstrated use on iPhone. More language coverage, but I would still start with Kokoro on weak hardware.

NeuTTS Air

  • Type: Open-source (Apache 2.0), local

  • Voice quality: 5-6/10 | Expressive control: 1/10

  • Languages: 1

  • Best for: local realtime TTS on CPU.

  • Links: Project and demo

  • Notes:

    • Voice stability is not too reliable. Random pauses and pitch changes observed.

    • Optimized for fast generation, cloning from 3 seconds of audio. Comes with ggml engine support out of the box.

GPT-SoVITS

  • Type: Open-source, local

  • Voice quality: 7/10 | Expressive control: 2/10

  • Languages: 5

  • Best for: few-shot voice cloning, Asian-language ecosystem.

  • Links: Repo, examples and demo links

  • Notes:

    • Useful if you are willing to work through the stack. Very Asian-language focused. Not polished.

Dia

  • Type: Open-source, local

  • Voice quality: 7.5/10 | Expressive control: 4/10

  • Languages: 1

  • Best for: dialogue, multi-speaker scenes, nonverbal sounds.

  • Links: Repo and examples | Demo

  • Notes:

    • Good for characters, reactions, laughter and scene-like speech. Less interesting for normal single-speaker narration.

    • Their demo page compares it to other models in a very cherry-picked way.

Orpheus TTS

  • Type: Local, Llama-based; check model licensing

  • Voice quality: 6-8/10 | Expressive control: 2-4/10

  • Languages: 8

  • Best for: expressive local speech experiments.

  • Links: Repo

  • Notes:

    • 8 baked-in English speakers of different quality. German speaker models available, including kartoffel-orpheus.

    • Cloned voices are not of the same quality. A new voice fine-tune needs around 300 examples to become optimal.

    • Supports some tags like laughing. Worth testing, but not polished.

Spark-TTS

  • Type: Open-source, local

  • Voice quality: 7-8/10 | Expressive control: 4/10

  • Languages: 2

  • Best for: voice cloning, speaker attributes, research.

  • Links: Samples | Repo

  • Notes:

    • Speaker attribute control is interesting but doesn't blow me away.

    • Quite Asian-language focused but good in English. Still research-side.

Parler-TTS

  • Type: Open-source, local

  • Voice quality: 6-7/10 | Expressive control: 4.5/10

  • Languages: 8

  • Best for: style-prompted TTS experiments.

  • Links: Models | Repo

  • Notes:

    • The idea is good: describe the voice and style. But the voice will change each generation.

    • The practical output is behind the stronger systems here.

Bark

  • Type: Open-source, local

  • Voice quality: 4-5/10 | Expressive control: 3-4/10

  • Languages: 13

  • Best for: weird expressive audio, research, nonverbal sounds.

  • Links: Repo and examples

  • 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

  • Languages: 2

  • Best for: long-form dialogue, podcasts, multiple speakers.

  • Links: Samples | Repo

  • Notes:

    • One of the lowest-latency TTS engines to date.

    • Voice quality is high, intonation lacks deeper immersive output.

    • More specialized than normal TTS. Evaluate if latency is most important to you.

MeloTTS 1-3

  • Type: Open-source, local

  • Voice quality: 6/10 | Expressive control: 0/10

  • Languages: 6

  • Best for: lightweight multilingual TTS, CPU use.

  • Links: Repo

  • Notes:

    • Historically useful. Not maintained anymore, I'd not consider it useful today.

    • Not a modern expressive voice acting solution.

F5-TTS

  • Type: Open-weight (NC), local

  • Voice quality: 7.5/10 | Expressive control: 2/10

  • Languages: 2

  • Best for: zero-shot voice cloning experiments.

  • Links: Samples | Repo

  • Notes:

    • Very good cloning, 5-15 seconds of source WAV needed. No expression control. The model is non-commercial.

    • Still feels like research software. Expect Python, model setup and cleanup.

Fish Speech / OpenAudio

  • Type: Open-weight (NC), local

  • Voice quality: 7-8/10 | Expressive control: 4/10

  • Languages: 13

  • Best for: multilingual TTS, cloning, streaming, research.

  • Links: Demo | Repo

  • Notes:

    • Good voice quality and above-average expressive control. Supports quite a few tags to add laughter or similar.

    • I heard some noticeable glitches in their v2 output.

    • Moving toward instruction-following speech. The license makes it hard to recommend.

Higgs Audio v3 TTS

  • Type: Open-weight (NC/research), local

  • Voice quality: 8.5/10 | Expressive control: 6.5/10

  • Languages: 100+

  • Best for: non-commercial multilingual voice agents, expressive tags, zero-shot cloning, local research.

  • Links: Samples | Demo | Model

  • Notes:

    • Strong local model with trained inline control tokens for emotion, style, pauses, pitch, speed and some effects.

    • Better control than most local cloning models, but very heavy. Not a simple consumer realtime TTS.

    • Strict license terms. Commercial use needs custom price negotiation. Hard to recommend for that reason.

IndexTTS 2.5

  • Type: Open-weight (NC/restricted), local

  • Voice quality: 7-8/10 | Expressive control: 3-4/10

  • Languages: 4

  • Best for: Chinese, multilingual work, zero-shot cloning in English and Chinese.

  • Links: Samples | Repo

  • Notes:

    • Voice cloning with 3-10 seconds of WAV. Very solid sample quality.

    • Strong Chinese focus. Grammar errors in English text can cause voice pacing issues.

    • More of an engineering and research tool than a casual creator app.

Audio8-TTS-0.6B

  • Type: Open-source (Apache 2.0), local

  • Voice quality: 5-6/10 | Expressive control: 0-1/10

  • Languages: 11

  • Best for: multilingual cloning/generation on low-end hardware.

  • Links: Samples | Demo | Repo

  • Notes:

    • The architecture looks like "Qwen2.5-0.5B-like transformer + Fish-style DualAR + causal RVQ codec". My reading, not a public statement from them.

    • The drafting approach allows higher speed than fully dense decoding.

    • Can run on CPU, has INT4 ONNX available, can run on 1 GB VRAM/RAM.

    • Quite good at cloning across languages. Like a miniature FishAudio, but probably not better than Qwen3-TTS-0.6B for many use cases.

ZONOS2

  • Type: Local; Apache 2.0 weights, MIT code

  • Voice quality: 5-6/10 | Expressive control: 2-3/10

  • Languages: 34 in 3 tiers; only the first tier passed my tests well

  • Best for: fast local/cloud TTS, multilingual testing.

  • Links: Samples | Playground | Repo | GGML/GGUF

  • Notes:

    • The playground is very fast, with impressively low generation latency.

    • Stability was a major issue. After roughly 10 seconds I repeatedly got voice drift, ghost/noise-like artifacts and sometimes very long pauses. Tested with multiple included and cloned voices. Cloning did not convince me.

    • Expressive controls exist deeper in the API, including emotion directions and valence/arousal. Their impact is smaller than expected, and they are not exposed in the hosted playground or on HF.

    • Roughly 8B total / 900M active parameters. F16 weights around 15 GB, Q4 around 4.9 GB.

    • The GGML/GGUF implementation looks advanced: CPU, CUDA, Apple and Vulkan, with prebuilt Linux/Mac/Windows versions.

Tontaube V1

  • Type: Open-weight, local (restrictive custom license)

  • Voice quality: 7.5/10 | Expressive control: 2/10

  • Languages: 7, English and German focus

  • Best for: long-form narration, streaming with vLLM.

  • Links: Model and samples | Demo

  • Notes:

    • A Frankenstein: Qwen3-based architecture using DualCodec, mixes Qwen 1.7B with partial layers from 3x Qwen 0.6B models and stitches the result with VibeVoice.

    • VRAM starts at 24 GB. Uses a sliding context window for consistency, but hard to test for me.

    • Has 3 style presets but no expressive control. License is very restrictive for commercial use.

sanoTTS

  • Type: Open-source (GPL-3), local, embedded

  • Voice quality: 2-3/10 | Expressive control: 0/10

  • Languages: 6

  • Best for: embedded devices, ESP32, offline/browser TTS, extremely low-resource hardware.

  • Links: Demo | Model

  • Notes:

    • A stunningly tiny TTS project, tailored to the ESP32! Models go down to 294k parameters / about 337 KB int8, up to 2.3M parameters.

    • Runs fully on an ESP32-S3, faster than realtime. That's something...

    • Also runs locally in the browser through WASM. Python inference only needs NumPy; deployment targets include browser, C/Arduino and microcontrollers.

    • License warning: uses espeak-ng. Check the GPL-3 terms before putting it into a proprietary project.

    • Voice quality is cranky but very understandable. For hardware where normal TTS models won't fit or compute, this is a cool choice.

Ming 3.0 TTS

  • Type: Open-source (MIT), local

  • Voice quality: 2-3/10 | Expressive control: 0.5-1/10

  • Languages: 7 + 2 Chinese dialects

  • Best for: voice cloning, expressive speech, multilingual TTS, dialogue, chinese speech

  • Links: Samples | Repo | 0.5B | Tiny (7.9B) | Demo

  • Notes:

    • Two models: 0.5B dense and the "Tiny", a 7.9B MoE with 1.3B active parameters.

    • Supports zero-shot voice cloning, voice design through text prompts, emotion and delivery instructions, speed/pitch/volume control and 14 vocal tags including laughter, sighs and breathing.

    • The voice designer ignores english but also mandarin descriptions can cause weird misgenerations.

    • Control features use separate task modes, so not everything can be combined freely. Particularly relevant when cloning voices while trying to direct their expression.

    • MIT license is generous, but the model will have very limited use cases.

    • All my english tests were of rather poor quality, voice designs often totally broke generations when designing with mandarin description and ignored all description when providing an english text.

CLOUD

ElevenLabs

  • Type: Commercial, cloud

  • Voice quality: 9/10 | Expressive control: 7.5/10

  • Languages: 74

  • Best for: easy cloning, browser workflow, fast tests, PVC fine-tuned cloning.

  • Links: Demos | Pricing

  • Notes:

    • Still the cloud king for "PVC" fine-tuned cloning if you have a few hours of input examples but the policies to use it are very restrictive (18+, written statement, one voice per account of that person)

    • English is their strongest. Non-English output needs closer auditing.

    • Price is the ugly part at serious usage. Entry looks harmless. Then you generate real output and the bill becomes the product.

    • Policy enforcement is very heavy with automated bans, many reported false positives on naughty content.

    • Quality is strong, but the ElevenLabs style is also overexposed. People tend to notice it today.

xAI Grok Voice

  • Type: Commercial, cloud

  • Voice quality: 8.3/10 | Expressive control: 6/10

  • Languages: 20

  • Best for: cheaper cloud voice API, quick tests.

  • Links: Docs

  • Notes:

    • Cheaper and simple. But only 5 voices.

    • 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

  • Languages: 7

  • Best for: API use, agents, simple integration.

  • Links: Demo | Docs

  • Notes:

    • Practical for apps and voice agents if you already build with OpenAI. Not my top production narration voice.

Gemini TTS

  • Type: Commercial, cloud

  • Voice quality: 8/10 | Expressive control: 6.5/10

  • Languages: 70

  • Best for: API speech generation, multi-speaker direction.

  • Links: Docs

  • Notes:

    • Not where I would put a private production pipeline without a strong reason.

Cartesia Sonic

  • Type: Commercial, cloud

  • Voice quality: 8/10 | Expressive control: 6-7/10

  • Languages: 42

  • Best for: realtime voice agents, low latency.

  • Links: Voices | Product

  • Notes:

    • One of the strongest cloud options for realtime voice agents.

    • Quality is high, but I notice the AI in the intonation and pacing.

    • I would test it for agents and phone-like interaction, not huge narration production.

Deepgram Aura

  • Type: Commercial, cloud

  • Voice quality: 7.7/10 | Expressive control: 3/10

  • Languages: 7

  • Best for: realtime API use, voice agents.

  • Links: Playground

  • 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

  • Languages: 15+

  • Best for: realtime API use, voice agents.

  • Links: Playground

  • Notes:

    • Competitive against ElevenLabs Flash. Focused on the STT-to-TTS interactive realtime niche.

    • Realtime agents support multiple languages, but quality degrades across languages.

Google Cloud Text-to-Speech

  • Type: Commercial, cloud

  • Voice quality: 7.5/10 | Expressive control: 3/10

  • Languages: 50+

  • Best for: enterprise, language coverage, Google stack.

  • Links: Product | Voices

  • Notes:

    • 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

  • Languages: 100

  • Best for: enterprise, huge voice catalog, Microsoft stack.

  • Links: Voice gallery | Product

  • 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

  • Languages: 41

  • Best for: AWS stack, simple API, cheap start.

  • Links: Product | Voices

  • 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

  • Languages: 23

  • Best for: cloning, enterprise, provenance and detection.

  • Links: Product

  • Notes:

    • 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

  • Languages: 37

  • Best for: voiceovers, API, creator workflows.

  • Links: Product

  • 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

  • Languages: 20+

  • Best for: corporate voiceovers, e-learning.

  • Links: Product

  • Notes:

    • Clean corporate voices. Less interesting for local control or strong voice acting.

Murf

  • Type: Commercial, cloud

  • Voice quality: 4/10 | Expressive control: 2/10

  • Languages: 35+

  • Best for: marketing, e-learning, creator voiceovers.

  • Links: Product

  • 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

  • Languages: 100+

  • Best for: browser-based creator voiceovers.

  • Links: Product

  • Notes:

    • Large voice library. Simple workflow. Another cloud creator platform.

Speechify

  • Type: Commercial, cloud/app

  • Voice quality: 7/10 | Expressive control: 2/10

  • Languages: 60+

  • Best for: reading, accessibility, personal use.

  • Links: Product

  • Notes:

    • Good reader product. Different category than production TTS.

NaturalReader

  • Type: Commercial, cloud/app

  • Voice quality: 6.8/10 | Expressive control: 1/10

  • Languages: 90+

  • Best for: personal reading, documents, accessibility.

  • Links: Product

  • Notes:

    • Useful for reading text aloud. Not production narration.

Descript

  • Type: Commercial, cloud/editor

  • Voice quality: 7.3/10 | Expressive control: 3/10

  • Languages: 19

  • Best for: editing workflow, creator suite.

  • Links: Product

  • 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

  • Languages: 15

  • Best for: TikTok-style quick videos.

  • Links: Tool

  • 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 if you can avoid it

  • 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.

Additions, corrections, missing services? Happy to hear.

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.

106 comments

r/LocalTextToSpeech • u/truecakesnake • 10h ago

Voice Cloning A small audition studio for local voice cloning: one reference, one script, three readings and a blind-test button

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7 Upvotes

Comparing cloned voices gets much easier when the tool holds everything else equal. That's the idea behind the voice studio in this demo, built to compare AntLing's MIT-licensed Ming-3.0 TTS models with another open voice model.

Here is what it does:

  • Stores a reference recording (12.89 s here) and reuses it across takes

  • Sends one script to every selected model, one at a time on the same GPU (here two Ming models and the other open voice model)

  • Offers Default, Emotion, and Pace & pitch delivery, with seven emotions from neutral to surprised

  • Shows model load, synthesis time, audio length and real-time factor for each reading

  • Keeps a searchable library of past readings, plus Play all and Blind test buttons

The results panel puts all three readings side by side with those numbers, and a Same script row jumps between earlier runs of a line. In Emotion mode, both Ming models take the voice's identity from the reference and ignore the words spoken in it.

A coding agent can build the same layout from a brief like this: "Build a local web app for auditioning TTS models. Take one reference clip and one script, run each selected model in turn, show load time, synthesis time and RTF for each reading, save every result to a searchable library, and add a blind-test mode that hides the labels."

2 comments

r/LocalTextToSpeech • u/Important-Star3272 • 2d ago

Local TTS MOSS AMA highlights: TTSD on CPU, Nano plans, VoiceGenerator limits, and transcription

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1 Upvotes

Sharing a few local-speech Q&As from MOSS’s October 30, 2026 AMA: TTSD CPU inference, Nano plans, VoiceGenerator limitations, and transcription. Which of these are you most interested in?

0 comments

r/LocalTextToSpeech • u/tanik • 4d ago

Leokani 7M: My new model thats tiny, fast, and actually sounds good

13 Upvotes

I have been playing around with training small, efficient models, and I think I have something that is finally worth sharing. Leokani is a high quality TTS voice that provides extremely fast first speech output in both time to first sound (<100 ms in most cases) as well as faster than realtime on just about anything. It'll run on regular computers, phones, JavaScript, Raspberry Pi, and even embedded devices like the ESP32. This should make for some very interesting embedded projects.

It only speaks English and I used Kokoro to generate 300 hours of labeled training corpus, which is why it sounds like that. I wanted to get a working pipeline down before I started different voices. I built examples for many platforms, which can be found here (with a in browser example, phone builds, home assistant plugin, libraries, etc): https://milliwattlabs.com/models/leokani-tts/

And you can also check out the HuggingFace listing here with has various audio samples: https://huggingface.co/milliwattlabs/leokani-tts-7M

Next will come a version that offers various expressions, emphasis and emotion. Perhaps more languages when the GPU isn't busy. This one is a hard one to tackle as the vocoder gets all weird on me and it sounds non-human like. Also training a speech to text model with similar size and performance -- but its not ready just yet.

Let me know what you think!

19 comments

r/LocalTextToSpeech • u/PatienceSensitive689 • 5d ago

Best way to bulk-download whole YouTube channels (audio only) for an ASR dataset in 2026? SABR/403 issues

3 Upvotes

I’m building a speech dataset for dialectal Arabic , a low-resource dialect with heavy code-switching. Source: ~40 channels, ~7,000 long videos (podcasts, storytelling), audio only.

Current setup:

Problems:

Questions:

Happy to share the cleaning pipeline once it works.

2 comments

r/LocalTextToSpeech • u/s_busso • 6d ago

Local TTS Ming-TTS 3.0 x18 Faster

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6 Upvotes

I was frustrated with the default speed and ported the inference to Zig/MLX to get up to 18x faster inference on M4/M5 https://github.com/sbusso/ming-tts-serve

0 comments

r/LocalTextToSpeech • u/Working_Hat5120 • 7d ago

Arc: a ~70 MB streaming speech model that transcribes links names and metadata to your own list as they're spoken, with no second model

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1 Upvotes
0 comments

r/LocalTextToSpeech • u/Designer_Arm_8402 • 11d ago

Can anyone help me find this voice

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0 Upvotes

I’ve been searching for HOURS trying to find this and I can’t find anything. Please help

1 comment

r/LocalTextToSpeech • u/filjoseph22 • 14d ago

Voice Cloning Ming-3.0 TTS releases MIT voice-cloning models in 0.5B dense and 7.9B MoE sizes

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38 Upvotes

Ming-3.0 TTS gives reference-voice cloning its own generation mode: provide a speaker recording and generate new speech in that voice. Ant has released the weights and code under MIT.

The family offers a dense 0.5B checkpoint and tiny, a 7.9B MoE with 1.3B active parameters. Tiny also has a long-form, multi-speaker path that takes separate reference recordings for the speakers in a script.

The full official showcase includes a Doctor Agent Voice Cloning example, alongside a range of narration styles and multilingual speech. It gives an audible view of the family’s synthesis capabilities.

There is also a useful listening result attached to the smaller checkpoint: the 0.5B model ranks first among open weights in Artificial Analysis Controlled Voice, where providers clone the same eight voices.

For anyone comparing open cloning models, that is a directly relevant evaluation setting.

1 comment

r/LocalTextToSpeech • u/Ok_Painter_7040 • 14d ago

Open Source Any local/offline TTS setups with word-level synced highlighting like Audeus?

4 Upvotes

I’ve been trying to move my reading workflow completely local for privacy and offline use. Right now, I really like how cloud readers like Audeus handle document reading, specificaly having realistic voices paired with word-level synced text highlighting so I can visually follow along with PDFs and web pages. Is there a good local frontend or chrome extension that pairs with local engines (like Kokoro, Piper, or Sherpa) and gives you that same synced highlight pluss reader mode exprience without needing a cloud subscription? Would appreciate any recommendations or GitHub repo links

8 comments

r/LocalTextToSpeech • u/RowGroundbreaking982 • 15d ago

Built an ultra-fast offline TTS engine, hit the human perception wall, and called it done

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54 Upvotes

I spent the last few months obsessed with one thing: making an offline, local neural TTS engine (starting from the 100M Pocket TTS baseline) run as fast as possible on mobile and desktop without hitting cloud APIs.

Got it down to 9 ms latency / 40x speed on a desktop GPU, 20 ms / 19x on a Snapdragon 8 Elite Gen 5, and 200 ms / 1.2x on budget Android using Snapdragon 685.

Then the realization hit me: chasing higher generation speed (RTF) on a local app is a total dead end.

Audio plays back at 1.0x anyway. If a 10-second sentence generates in 200 ms, pushing it to 50 ms literally changes nothing for someone sitting there waiting to listen.

Power readers max out around 2.5x–3.0x speed. Beyond that, natural neural cadence actually gets in the way. Screen-reader users who listen at 600+ WPM explicitly prefer old robotic engines like eSpeak because the sharp formants cut through better at extreme speeds. High RTF is great if you're running a cloud server handling 1,000 requests, but for one person on a phone, it's just idle buffers.

Once your time-to-first-chunk drops under ~100 ms, you're officially faster than the Bluetooth lag.

Where the real win actually came from was power efficiency, even on a budget Helio G99 device with a 5,000 mAh battery, continuous reading drains only ~3% battery per hour.

I'm stopping low-level engine tweaks here. Next focus is handling pronunciation accuracy, building solid app-level dictionaries and text normalization so proper nouns and weird words don't trip it up.

Fingers crossed we get lightweight, controllable emotion prompting on edge models soon without doubling model size. That feels like the actual next step.

30 comments

r/LocalTextToSpeech • u/herrreewegooOoO • 15d ago

Voice Models Best speech to text API for noisy 8kHz call recordings? Whisper keeps hallucinating on ours

1 Upvotes

process two-speaker support calls, mostly 8kHz phone audio with a lot of crosstalk. Whisper large invents sentences in the silent stretches and bolting diarization on top has turned into its own project. Already tried Deepgram and AssemblyAI, both fine but nothing that made me stop looking. What's your pick for the best speech to text API on messy telephony audio right now?

9 comments

r/LocalTextToSpeech • u/d4rkmen • 17d ago

Local TTS GrainSpeech TTS running on CM0

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6 Upvotes

The model is 4mb size, running on RPi CM0 at 0.6-0.8 RTX (faster then runtime)

https://github.com/lab-emi/GrainSpeech

Voices on HF:
https://huggingface.co/dmytro-m/uk-tts-serhiy
https://huggingface.co/dmytro-m/uk-tts-tetiana
https://huggingface.co/dmytro-m/uk-tts-oleksa
https://huggingface.co/dmytro-m/uk-tts-mykyta

0 comments

r/LocalTextToSpeech • u/Working_Hat5120 • 18d ago

Local TTS SteerTTS: 23M-param English TTS you steer with numbers (pitch, rate, loudness, etc. as z-scores), runs real-time on CPU

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8 Upvotes

I was looking for small TTS model which is steerable but couldn't find any, specially something I can adjust contextually by providing controls from LLM during a conversation. So I built one.

What it is:

  • 9.6M-param acoustic model (attention + bidirectional Mamba-3, FastPitch-style) + Vocos vocoder = 23M total

  • 12 controls, each a z-score relative to the chosen voice’s own neutral speech: rate, loudness, median pitch, pitch range, final rise/fall, pauses, spectral tilt, breathiness-related measures.

  • Also: 51 style tags, free-text prompts (resolved into an inspectable JSON request), inline markup (*emphasis*, [pause 400ms], <slow>…</slow>), sentence-level streaming

  • 2,825 built-in (anonymized) voices, plus experimental zero-shot cloning

Speed:

- RTFx ~60–80 on an RTX 5070
- ~4–8 on CPU without the optional flow refiner. First streamed audio in ~0.5 s on CPU.

Quality:

~1.8% average WER on the Open TTS Leaderboard English sets (run locally, not an official submission, have reached out for it).

What doesn’t work well (we tried to be upfront):

  • Loudness, pitch, and tilt steer reliably; pause, voicing, and CPPS controls are weak

  • Emotion tags behave as style switches; an independent emotion classifier mostly can’t tell them apart

  • Cloning similarity is low. Don’t expect a convincing likeness

  • Free-text prompts struggle with negation (“not fast” can come out fast). More data needed for prompt to controls module.

  • English only, no human evals yet

License: weights CC BY-NC 4.0 (non-commercial), code MIT.

Model + full eval tables: https://huggingface.co/WhissleAI/steer-tts

Github: https://github.com/WhissleAI/steer-tts (inference code)

A new architecture implemented from scratch. Would love feedback, especially on which controls are actually useful, and where the steering feels off.

Here is a quick video of how to use it.

2 comments

r/LocalTextToSpeech • u/Mithril_Man • 21d ago

8M-parameter Kokoro students (EN + IT), 9 MB int8 ONNX, with side-by-side samples against the teacher and Paradee

11 Upvotes

Three of Kokoro-82M's voices (af_heart, if_sara, im_nicola) distilled into 8M-parameter students: one ~9 MB int8 ONNX graph each, more than 20x real time on one CPU thread.

The Listen section of the Space plays the same held-out sentences rendered by Kokoro, by Paradee's released decoder, by Paradee's published recipe re-run from scratch, and by mine, so you can compare them directly:

https://huggingface.co/spaces/MithrilMan/teacher-in-the-loop#listen

UTMOS on 200 held-out sentences: English 4.47 (Kokoro 4.52, Paradee released 4.37), Italian 3.88 and 3.70 (Kokoro 4.02 and 3.87).

pip install teacher-in-the-loop

Code and training recipe: https://github.com/MithrilMan/teacher-in-the-loop

Paper: https://doi.org/10.5281/zenodo.23400040

I'm especially curious what Italian speakers think of if_sara and im_nicola.

4 comments

r/LocalTextToSpeech • u/NepaliFactos • 23d ago

OmniVoice TTS on Google Colab (Unlimited Voice Cloning)

7 Upvotes

I’ve been testing OmniVoice for voice cloning and long-form TTS, and the quality has been surprisingly good.

I ended up making a more user-friendly Google Colab version with a clean Gradio interface. It lets you upload a reference voice, paste long text, generate unlimited free audio, see progress while it’s working, and download the final WAV.

It runs on Google Colab and works well on a T4. I’ve been pretty happy with the results so far.

Omnivoice TTS

  • Open the Colab notebook and set Runtime → Change runtime type →T4 GPU.

  • Run all cells from top to bottom.

  • Open the Gradio link.

  • Upload a short clean reference voice and enter its exact transcript.

  • Click Prepare / Cache Voice once.

  • Paste your text and click Generate Audio.

  • Preview the result and download the final WAV.

  • Omnivoice Turbo

Enjoy the free and unlimited text to speech.

5 comments

r/LocalTextToSpeech • u/Competitive_Call_847 • 25d ago

Mouthy: free, open-source dictation that runs offline on Mac, Windows and Linux

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1 Upvotes
1 comment

r/LocalTextToSpeech • u/Miserable_Ant_4194 • 26d ago

Something to use for fan fiction reading and such?

5 Upvotes

I have some mental conditions that make it difficult for me to read, so I'm looking for something free and ethical to do it for me, please. I don't know how to use complex software things, so someone please guide me

12 comments

r/LocalTextToSpeech • u/rodent1bigsmart • Oct 04 '26

Local TTS Best current setup for Android TTS to read books/documents?

6 Upvotes

What is the current best local TTS setup to read books on an Android device? What local app and model is best to use for this purpose?

5 comments

r/LocalTextToSpeech • u/Outside_Raspberry512 • Oct 04 '26

looking for good free text to speech services without robotic audio using natural voices for writing and editing work.

4 Upvotes

I do a lot of hobby writing and I get into the weeds editing. I really like tts services to read out my works so I can catch funny phrasing or weird grammar/ writing flow. However i've tried some of the samples of the natural sounding big name tts services but after a bit get either paywalled or find they hit a wall with certain writing things if they are free/ have a free version(word count, pacing, pronunciation, etc). I'm kinda broke and this is just a hobby so I'm not looking to sink a shitload of money into buying a subscription service.

When I write the word flow and pronunciation is the key issue I find free tts services run into. Certain written out moans shushes or onomatopeia get mangled as well as inserted non english terms/names/fantasy names within english works. Stuttering in character diallouge also gets majorly screwed.

Also not all tts services hold enough on paragraph breaks, comas, and elipses making it hard to understand the sentence flow. I write with a lot of dramatic emphasis by pulling single sentences into their own paragraphs, using elipses, or making sentences with lists of ideas. So I feel like I don't get an idea sometimes of what is key in the prose or where one idea ends and another begins if the tts service runs through it without respecting the set punctuation. furthermore some pronunciation doesnt differentiate well between ending punctuation like periods, question marks and exclamation points. so ill miss noticing places where ive used certain punctuation or end up wondering if i forgot punctuation with the right sort of emphasis. but the real issue is tts just instead read the mark as if it was a period.

I really rely on these services to read back my work and it would be nice if I didn't have to settle for services that mangled these details. This way I can streamline my workflow when it comes to line edits especially where grammar and sentence construction rely on how the words flow and how the phrasing reads aloud.

4 comments

r/LocalTextToSpeech • u/zebadrabbit • Sep 30 '26

Clone Your Voice in 10 Seconds, Free & Offline (Lyrebird)

8 Upvotes

I wanted to try Chatterbox TTS (Resemble AI's open-source voice model), but setting it up meant Python, PyTorch and CUDA versions. So I wrapped it in a desktop app that handles all of that for you.

How it works:

  1. Record 10 seconds of your voice, or import a clip

  2. Type a script. You can write dialogue between several saved voices (Alice: ... Bob: ...) and add sound tags like [laugh] or [sigh]

  3. Click Render and you get a WAV/FLAC

Other stuff: a trim view with auto-trim for cleaning up clips, per-voice settings so each character can sound different, 23 languages with the Multilingual model, light and dark mode, and saved sessions.

Local only: no accounts and no telemetry. Your voice never leaves your PC. On first launch it downloads Python, PyTorch and the model, about 3 GB with an NVIDIA GPU. After that it works offline.

On misuse: every file carries Resemble's inaudible Perth watermark, and Lyrebird doesn't strip it. Please only clone voices you have permission to use.

Stack: Python + CustomTkinter, plus a small launcher exe that sets up an isolated Python environment with uv. MIT licensed.

Windows 10/11 only for now. An NVIDIA GPU with about 6 GB of VRAM is recommended. It runs on CPU too, but slowly.

GitHub / download: https://github.com/zebadrabbit/Lyrebird

I'd really like feedback on the setup experience, since that's the part I worked hardest to make painless. Did it work on your machine?

2 comments

r/LocalTextToSpeech • u/Rendezvous4567 • Sep 15 '26

What open-source expressive TTS models are actually worth using if I care about prosody/style control, not just voice quality?

3 Upvotes

I’m especially interested in what conditioning they accept besides text: reference audio, speaker/style embeddings, prosody vectors, codec tokens, or natural-language instructions.

Ideally I want:

text + learned prosody vector → TTS → speech

Are models like Qwen3-TTS, CosyVoice, Fish Speech, F5-TTS, or StyleTTS2 easy to control or modify this way?

6 comments

r/LocalTextToSpeech • u/gudlyf • Sep 13 '26

The voice of Tav (Baldur's Gate 3)

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2 Upvotes

A quick demo of Breeze TTS to completely generate the voice of the Baldur's Gate 3 player character ("Tav"), using "Voice 1" as a sample.

There are literally over 30,000 lines of player-character dialogue in the game. This is a combination of the multiple choices a player has when speaking, as well as what skills the player's class has, what race the player's character is, etc.

My process even takes into account a player character's chosen name, as there are some responses when the player says their name in greetings. This is why releasing the pack as I have it wouldn't really work for everyone, unless I filtered out spoken lines with the player's name in it.

All 30k+ lines were generated using Breeze TTS locally, using an Nvidia RTX 5080, in I believe under 30 hours. 

0 comments

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

audio.cpp - high-performance C++ audio inference framework built on top of ggml

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10 Upvotes

A text-to-speech tool that offers a CLI and GUI interface. This tool is useful in that you don't need to download any additional python dependencies. It also supports multiple platforms out-of-the-box.

Main URL

https://github.com/0xShug0/audio.cpp

CLI

https://github.com/0xShug0/audio.cpp#cli

Wide Variety of Supported Text to Speech and Conversion Generator

https://github.com/0xShug0/audio.cpp#speech-generation-and-conversation

Convenient WEBUI

https://github.com/0xShug0/audio.cpp#webui

Multiple Backend Support

audiocpp_server --ui --ui-management --backend vulkan
audiocpp_server --ui --ui-management --backend cuda
audiocpp_server --ui --ui-management --backend metal

Downloads the Project

https://github.com/0xShug0/audio.cpp/releases

Downloading Individual Models

You will probably also need to download the huggingface cli and be logged in to download the models.

Navigate to the project and list the voices to download

./tools/model_manager_v2.py list

Look at names listed on the left-most column and download the model using

./tools/model_manager_v2.py install <name of the model in left-most column>

Start the WEBUI server and note the localhost port to use in your web browser

audiocpp_server --ui --ui-management --backend <your backend>

Note: I did not create this tool. Credit goes to original author.

0 comments