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This is the best Open Source TTS available on Mac right now. Watch Peter break it down. I combined two Qwen3-TTS projects. One for MLX speed & the other for its beautiful UI (but was slow on Mac). Then added a bunch of features. Qwen3-TTS-MLX-WebUI-Enhanced Install via Pinokio

Blaine Brown

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167,961 次观看 • 6 个月前 •via X (Twitter)

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Run Qwen-TTS on Your Mac, natively. Blaine Brown has created Qwen3-TTS MLX WebUI Enhanced, a Web UI for running Qwen-TTS using the Mac-native MLX framework, powered by MLX-Audio from Prince Canuma, it runs very smooth! 1-click install on pinokio (Apple Silicon Macs)

Run Qwen-TTS on Your Mac, natively. Blaine Brown has created Qwen3-TTS MLX WebUI Enhanced, a Web UI for running Qwen-TTS using the Mac-native MLX framework, powered by MLX-Audio from Prince Canuma, it runs very smooth! 1-click install on pinokio (Apple Silicon Macs)

cocktail peanut

168,965 次观看 • 6 个月前

Sam-Audio by AI at Meta is now on MLX-Audio 🔊 You can now isolate voices or sounds from any audio locally in your Mac. I added this simple UI to the MLX-Audio studio app to help you get started Note: - The release is coming soon for now please install from source. - Vision part was excluded for now. Let me know if you need it. - PR for UI:

Sam-Audio by AI at Meta is now on MLX-Audio 🔊 You can now isolate voices or sounds from any audio locally in your Mac. I added this simple UI to the MLX-Audio studio app to help you get started Note: - The release is coming soon for now please install from source. - Vision part was excluded for now. Let me know if you need it. - PR for UI:

Prince Canuma

19,027 次观看 • 7 个月前

Run ZONOS Locally ZONOS, the new SOTA Open Source Voice Cloning TTS, is here. I've managed to write a 1-click launcher for Zonos that works on Mac, Windows, and Linux (ALL platforms!) Here's me cloning Peter Griffin's voice on my Mac.

Run ZONOS Locally ZONOS, the new SOTA Open Source Voice Cloning TTS, is here. I've managed to write a 1-click launcher for Zonos that works on Mac, Windows, and Linux (ALL platforms!) Here's me cloning Peter Griffin's voice on my Mac.

cocktail peanut

69,845 次观看 • 1 年前

This is MiniMax-M2.5 MLX running in LM Studio on an Apple Mac Studio M3 Ultra 512GB. Fast enough out of the box for hosting OpenClaw, n8n workflows, and Open WebUI for the team.

This is MiniMax-M2.5 MLX running in LM Studio on an Apple Mac Studio M3 Ultra 512GB. Fast enough out of the box for hosting OpenClaw, n8n workflows, and Open WebUI for the team.

Patrick J Kennedy

76,574 次观看 • 6 个月前

Run Orpheus — the state-of-the-art TTS — on your local PC! Orpheus-FastAPI is a high-performance TTS app with OpenAI-compatible API, running efficiently with LM Studio backend. And Death wrote this 1-click launcher for us! Cross platform (Mac, Linux, Windows)

Run Orpheus — the state-of-the-art TTS — on your local PC! Orpheus-FastAPI is a high-performance TTS app with OpenAI-compatible API, running efficiently with LM Studio backend. And Death wrote this 1-click launcher for us! Cross platform (Mac, Linux, Windows)

cocktail peanut

30,930 次观看 • 1 年前

Over the past few days, I’ve been actively testing Qwen3-Coder-Next, and I’ve built quite a few fun and creative mini-games with it. Its frontend capabilities are genuinely impressive. Because it’s based on the Qwen3-Next model, the speed is outstanding. In fact, we managed to build an Angry Birds–style game in just 45 seconds, which really pushed the upper bound of what I thought was possible in terms of efficiency. It’s also been extremely useful in my day-to-day work. I used it to read through the Qwen3-TTS GitHub repository, quickly write a Gradio app, and deploy it on ModelScope. The overall performance and accuracy were excellent—especially considering that Qwen3-TTS is not a trivial application at all. Overall, this is a model I genuinely enjoy using. I warmly welcome any feedback, and bad cases are especially appreciated—they’re incredibly helpful for us as we continue to optimize and improve the model.

Over the past few days, I’ve been actively testing Qwen3-Coder-Next, and I’ve built quite a few fun and creative mini-games with it. Its frontend capabilities are genuinely impressive. Because it’s based on the Qwen3-Next model, the speed is outstanding. In fact, we managed to build an Angry Birds–style game in just 45 seconds, which really pushed the upper bound of what I thought was possible in terms of efficiency. It’s also been extremely useful in my day-to-day work. I used it to read through the Qwen3-TTS GitHub repository, quickly write a Gradio app, and deploy it on ModelScope. The overall performance and accuracy were excellent—especially considering that Qwen3-TTS is not a trivial application at all. Overall, this is a model I genuinely enjoy using. I warmly welcome any feedback, and bad cases are especially appreciated—they’re incredibly helpful for us as we continue to optimize and improve the model.

Chen Cheng

18,396 次观看 • 6 个月前

Introducing Marvis-TTS 🔥🚀 A new local-first TTS model Lucas Newman and I built for efficiency, accessibility, and real-time performance right on consumer devices like Apple Silicon, iPhones, iPads, and more. Traditional TTS models often demand full text inputs or sacrifice real-time capabilities, Marvis flips the script. It streams audio chunks as text is processed, creating a truly conversational experience. No more awkward pauses or unnatural breaks—Marvis handles the entire text context intelligently to deliver coherent, expressive speech. Get started today: > pip install -U mlx-audio

Introducing Marvis-TTS 🔥🚀 A new local-first TTS model Lucas Newman and I built for efficiency, accessibility, and real-time performance right on consumer devices like Apple Silicon, iPhones, iPads, and more. Traditional TTS models often demand full text inputs or sacrifice real-time capabilities, Marvis flips the script. It streams audio chunks as text is processed, creating a truly conversational experience. No more awkward pauses or unnatural breaks—Marvis handles the entire text context intelligently to deliver coherent, expressive speech. Get started today: > pip install -U mlx-audio

Prince Canuma

81,404 次观看 • 1 年前

🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

raullen

22,760 次观看 • 2 个月前

Text-to-speech is moving way too fast. Just a few days ago, I tweeted about PersonaPlex-7B, NVIDIA's new open source TTS ( And today, Qwen just open-sourced Qwen3-TTS 🤯 It’s a revolutionary text-to-speech model built for control. Not just about generating speech, but about shaping how it sounds directly from language. You can guide the pace, the tone, and the expressiveness straight from text, without touching audio graphs or hand-tuning parameters. That’s the real shift! What makes Qwen3-TTS stand out is how practical it already is: → voice cloning from just a few seconds of audio → voice creation without any reference sample → support for 10 languages out of the box → end-to-end latency down to ~97ms → works in both streaming and non-streaming setups The models come in two sizes (0.6B and 1.7B), so you can trade off quality and hardware cost depending on your setup. You can work with curated voices, designed voices, or cloned ones, and it integrates cleanly with vLLM for production use. It also ships as a simple Python package you can pip install. If you’re building real-time voice systems, this removes a lot of friction! 100% free and open source. I put the repo in the 🧵↓

Text-to-speech is moving way too fast. Just a few days ago, I tweeted about PersonaPlex-7B, NVIDIA's new open source TTS ( And today, Qwen just open-sourced Qwen3-TTS 🤯 It’s a revolutionary text-to-speech model built for control. Not just about generating speech, but about shaping how it sounds directly from language. You can guide the pace, the tone, and the expressiveness straight from text, without touching audio graphs or hand-tuning parameters. That’s the real shift! What makes Qwen3-TTS stand out is how practical it already is: → voice cloning from just a few seconds of audio → voice creation without any reference sample → support for 10 languages out of the box → end-to-end latency down to ~97ms → works in both streaming and non-streaming setups The models come in two sizes (0.6B and 1.7B), so you can trade off quality and hardware cost depending on your setup. You can work with curated voices, designed voices, or cloned ones, and it integrates cleanly with vLLM for production use. It also ships as a simple Python package you can pip install. If you’re building real-time voice systems, this removes a lot of friction! 100% free and open source. I put the repo in the 🧵↓

Charly Wargnier

59,144 次观看 • 7 个月前

Introducing Open TTS Tracker! 🗣️ *sound on* A one-stop shop to track all open access/ source TTS models! Ranging from XTTS to Pheme, OpenVoice to VITS, and more... ⚡ For each model, we compile: 1. Souce-code 2. Checkpoints 3. License 4. Fine-tuning code 5. Languages supported 6. Paper 7. Demo Help us make it more complete! Let's 2024 the year of open TTS models! ❤️

Introducing Open TTS Tracker! 🗣️ sound on A one-stop shop to track all open access/ source TTS models! Ranging from XTTS to Pheme, OpenVoice to VITS, and more... ⚡ For each model, we compile: 1. Souce-code 2. Checkpoints 3. License 4. Fine-tuning code 5. Languages supported 6. Paper 7. Demo Help us make it more complete! Let's 2024 the year of open TTS models! ❤️

Vaibhav (VB) Srivastav

68,037 次观看 • 2 年前

AI researcher breaks down why your $5,000 Mac Studio runs local AI slower than benchmarks promise: "Performance isn't about the hardware - it's about three software layers stacked on top of it." in 15 minutes he tests MLX, Ollama, llama.cpp, and the new vllm-mlx head to head no GPU needed, just the Mac you probably already own - why Ollama, the most popular tool, is actually one of the slower options on Mac - the real bottleneck nobody benchmarks: 'prefill' time before the first token even appears - the exact quantization setting (4-bit) that's the best speed-to-quality tradeoff - why unified memory makes Macs uniquely good for big models compared to Nvidia GPUs complete guide to choosing the right hardware for local AI is below 👇

AI researcher breaks down why your $5,000 Mac Studio runs local AI slower than benchmarks promise: "Performance isn't about the hardware - it's about three software layers stacked on top of it." in 15 minutes he tests MLX, Ollama, llama.cpp, and the new vllm-mlx head to head no GPU needed, just the Mac you probably already own - why Ollama, the most popular tool, is actually one of the slower options on Mac - the real bottleneck nobody benchmarks: 'prefill' time before the first token even appears - the exact quantization setting (4-bit) that's the best speed-to-quality tradeoff - why unified memory makes Macs uniquely good for big models compared to Nvidia GPUs complete guide to choosing the right hardware for local AI is below 👇

rewind

207,513 次观看 • 2 个月前

Check out the actual speed (not yet the final version) of Qwen3-Next-80B-A3B-Instruct on Apple MLX! 🔥 4-bit: 67 TPS 8-bit: 58 TPS bf16: 48 TPS Movie normal speed, only waiting times removed. Awni Hannun and Gökdeniz Gülmez did it and I bet there is still room for improvement 💪

Check out the actual speed (not yet the final version) of Qwen3-Next-80B-A3B-Instruct on Apple MLX! 🔥 4-bit: 67 TPS 8-bit: 58 TPS bf16: 48 TPS Movie normal speed, only waiting times removed. Awni Hannun and Gökdeniz Gülmez did it and I bet there is still room for improvement 💪

Ivan Fioravanti ᯅ

15,946 次观看 • 11 个月前

Qwen3-Next (thinking & non-thinking) are now live in BF16 at Hyperbolic! Qwen3-Next is a huge efficiency leap: - 80B MoE with just 3B active params - 10x cheaper to train vs Qwen3-32B - 10x inference throughput for >32K tokens Proud to be a launch partner with Qwen - kudos to this amazing team for keeping pushing open-source AI forward. We’re the first to serve Qwen3-Next on Hugging Face. Give it a try!

Qwen3-Next (thinking & non-thinking) are now live in BF16 at Hyperbolic! Qwen3-Next is a huge efficiency leap: - 80B MoE with just 3B active params - 10x cheaper to train vs Qwen3-32B - 10x inference throughput for >32K tokens Proud to be a launch partner with Qwen - kudos to this amazing team for keeping pushing open-source AI forward. We’re the first to serve Qwen3-Next on Hugging Face. Give it a try!

Yuchen Jin

84,342 次观看 • 11 个月前

🚀 VoxCPM 2 is live! 🎉 Another open-source AI #TTS model from China — and one that stands shoulder to shoulder with Qwen3-TTS, while bringing everything into a single unified model. After rapid iterations from V1 (zero-shot cloning) to V1.5 (long-form + fine-tuning), #VoxCPM has consistently pushed quality and usability forward. Now, VoxCPM 2 takes it further: 🔹30+ languages — truly global, truly local. 🔹Infinite voice design — type it, hear it, control it. From a whisper to a booming cinematic voice. 🔹Studio-grade audio — 48kHz ultra-high fidelity with emotional depth 🔹Diffusion-Autoregressive cloning — preserves more acoustic and emotional detail than token-based models like Qwen3-TTS 💡 Big shoutout to Grok — used your multi-image video magic for our launch demo. It’s scarily good at keeping visuals consistent across shots. Elon Elon Musk, this one’s for you. 😉 Check the demo & start cloning your dream voice: 🌐 Hugging Face Space: 🤗 Hugging Face Model: 🤖 ModelScope Model: 💻 GitHub： #TTS #AI #VoiceCloning #GrokImagine #ElonMusk #OpenBMB #VoxCPM

🚀 VoxCPM 2 is live! 🎉 Another open-source AI #TTS model from China — and one that stands shoulder to shoulder with Qwen3-TTS, while bringing everything into a single unified model. After rapid iterations from V1 (zero-shot cloning) to V1.5 (long-form + fine-tuning), #VoxCPM has consistently pushed quality and usability forward. Now, VoxCPM 2 takes it further: 🔹30+ languages — truly global, truly local. 🔹Infinite voice design — type it, hear it, control it. From a whisper to a booming cinematic voice. 🔹Studio-grade audio — 48kHz ultra-high fidelity with emotional depth 🔹Diffusion-Autoregressive cloning — preserves more acoustic and emotional detail than token-based models like Qwen3-TTS 💡 Big shoutout to Grok — used your multi-image video magic for our launch demo. It’s scarily good at keeping visuals consistent across shots. Elon Elon Musk, this one’s for you. 😉 Check the demo & start cloning your dream voice: 🌐 Hugging Face Space: 🤗 Hugging Face Model: 🤖 ModelScope Model: 💻 GitHub： #TTS #AI #VoiceCloning #GrokImagine #ElonMusk #OpenBMB #VoxCPM

OpenBMB

559,710 次观看 • 4 个月前

First Odia. Now Punjabi, Opensourced! 1.8B Param TTS model for one of the world's most spoken languages This is my real voice and then my voice speaking Punjabi 4 seconds of reference audio. perfect Gurmukhi. z e r o competition for this model amongst other open models

First Odia. Now Punjabi, Opensourced! 1.8B Param TTS model for one of the world's most spoken languages This is my real voice and then my voice speaking Punjabi 4 seconds of reference audio. perfect Gurmukhi. z e r o competition for this model amongst other open models

Arnav Mehta

24,711 次观看 • 2 个月前

I've been building voice agents for the last 6mo and I think the chat-supervisor pattern is a game changer. Stitched model (STT-LLM-TTS) is slow, but realtime audio models aren't (yet) as smart as text. This has the best of both worlds. Here's how it works:

I've been building voice agents for the last 6mo and I think the chat-supervisor pattern is a game changer. Stitched model (STT-LLM-TTS) is slow, but realtime audio models aren't (yet) as smart as text. This has the best of both worlds. Here's how it works:

Noah MacCallum

73,462 次观看 • 1 年前

Time for the open-source AI robots revolution 🚀 We’ve been playing with a low-cost DJI robot controlled by 3 local open-source AI models (Whisper, Idefics2, Parler-TTS - all Apache2) & orchestrated by Dora-cs In comments a 250 lines code gist to build on top of it => enjoy!!

Time for the open-source AI robots revolution 🚀 We’ve been playing with a low-cost DJI robot controlled by 3 local open-source AI models (Whisper, Idefics2, Parler-TTS - all Apache2) & orchestrated by Dora-cs In comments a 250 lines code gist to build on top of it => enjoy!!

Thomas Wolf

138,094 次观看 • 2 年前

oMLX is working really well as single machine inference engine for coding agents! Caching is managed perfectly (it can use a ton of disk space, be aware!) and oQ quantization delivers great results. Behind the scenes it uses the standard MLX building blocks (75% created by Prince Canuma 🙏): - mlx-lm - mlx-vlm - mlx-embeddings - mlx-audio I tested Qwen3.6-35B-A3B-oQ6 on M5 Max with two pi instances and it was fast and furious and leveraging cache like crazy as you can see in the video. Let me try to create some oQ versions (2,4,6?) of MiniMax M2.7 now and then I'll pass to distributed inference. I must win! 💪

oMLX is working really well as single machine inference engine for coding agents! Caching is managed perfectly (it can use a ton of disk space, be aware!) and oQ quantization delivers great results. Behind the scenes it uses the standard MLX building blocks (75% created by Prince Canuma 🙏): - mlx-lm - mlx-vlm - mlx-embeddings - mlx-audio I tested Qwen3.6-35B-A3B-oQ6 on M5 Max with two pi instances and it was fast and furious and leveraging cache like crazy as you can see in the video. Let me try to create some oQ versions (2,4,6?) of MiniMax M2.7 now and then I'll pass to distributed inference. I must win! 💪

Ivan Fioravanti ᯅ

19,420 次观看 • 4 个月前

Thanks to Qnoox I discovered this: Dr. Sbaitso from 1992 (33 years ago!) One of the first AI chat apps, I guess you could call it a highly primitive LLM, running on MS-DOS The cool thing is it already had speech synthesis so it could speak back via TTS! And when I was talking to it, within a few questions it was already asking for more compute, it wanted to become ChatGPT :D Installed it on now, you have to exit to DOS first, then CD SBAITSO, and then run SBAITSO2.BAT

Thanks to Qnoox I discovered this: Dr. Sbaitso from 1992 (33 years ago!) One of the first AI chat apps, I guess you could call it a highly primitive LLM, running on MS-DOS The cool thing is it already had speech synthesis so it could speak back via TTS! And when I was talking to it, within a few questions it was already asking for more compute, it wanted to become ChatGPT :D Installed it on now, you have to exit to DOS first, then CD SBAITSO, and then run SBAITSO2.BAT

@levelsio

258,945 次观看 • 10 个月前

🎉 Congrats to MOSI + OpenMOSS on the release of MOSS-Transcribe-Diarize-0.9B — a genuinely impressive end-to-end ASR model for real-world, multi-speaker conversations. We're proud to partner with them for day-0 support on mlx-audio (Python & Swift). Now running local-first on Apple Silicon: 🎙️ transcript + speaker labels + timestamps in one pass 🗣️ unlimited-speaker diarization ⏱️ up to ~90 min of audio per input 🌍 90+ languages 🧠 128K context • Whisper-Medium encoder + Qwen3-0.6B decoder 🎯 hotword boosting for names, products & domain terms Perfect for meetings, podcasts, interviews, and long-form call analysis — no cloud, no data leaving your Mac. Get started now: 🐍 Python > uv pip install -U mlx-audio 🍎 Swift .package(url: " from: "0.1.3") Go build. 🚀

🎉 Congrats to MOSI + OpenMOSS on the release of MOSS-Transcribe-Diarize-0.9B — a genuinely impressive end-to-end ASR model for real-world, multi-speaker conversations. We're proud to partner with them for day-0 support on mlx-audio (Python & Swift). Now running local-first on Apple Silicon: 🎙️ transcript + speaker labels + timestamps in one pass 🗣️ unlimited-speaker diarization ⏱️ up to ~90 min of audio per input 🌍 90+ languages 🧠 128K context • Whisper-Medium encoder + Qwen3-0.6B decoder 🎯 hotword boosting for names, products & domain terms Perfect for meetings, podcasts, interviews, and long-form call analysis — no cloud, no data leaving your Mac. Get started now: 🐍 Python > uv pip install -U mlx-audio 🍎 Swift .package(url: " from: "0.1.3") Go build. 🚀

Prince Canuma

11,493 次观看 • 1 个月前