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Introducing MLX-Audio Studio 🚀 An open-source UI for audio gen. This new UI will allow you to easily generate and transcribe audio locally using MLX-Audio, Transformers or any other backend you prefer (i.e. OpenAI). We will be adding more tasks soon, stay tuned! Get started on our GH:

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Prince Canuma

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51,071 просмотров • 8 месяцев назад •via X (Twitter)

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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:
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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:

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Day 1 of 3 MLX Releases: Introducing MLX-Audio 🚀🔥 A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon. Features ⚡️Fast inference on Apple Silicon (M series chips) 🤖Multiple language support 🗣️Voice customization options 🚀Quantization support for optimized performance Supported models: 🪶Kokoro - A multilingual TTS model with 82M params that supports various languages and voice styles. With more models coming soon. Get started: > pip install mlx-audio Please leave us a star and send a PR :)
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Day 1 of 3 MLX Releases: Introducing MLX-Audio 🚀🔥 A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon. Features ⚡️Fast inference on Apple Silicon (M series chips) 🤖Multiple language support 🗣️Voice customization options 🚀Quantization support for optimized performance Supported models: 🪶Kokoro - A multilingual TTS model with 82M params that supports various languages and voice styles. With more models coming soon. Get started: > pip install mlx-audio Please leave us a star and send a PR :)

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So you can clone any voice 100% locally using this new open source model?! Alibaba has released Qwen3-TTS on Hugging Face. You can easily: - Create custom voices - Clone any voice from a VERY short audio - Generate speech with style instructions Only 0.6B & 1.8B! Sound on🔊
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So you can clone any voice 100% locally using this new open source model?! Alibaba has released Qwen3-TTS on Hugging Face. You can easily: - Create custom voices - Clone any voice from a VERY short audio - Generate speech with style instructions Only 0.6B & 1.8B! Sound on🔊

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Day 1 of 3 days of MLX: Introducing MLX-Audio-Swift SDK 🚀 A modular Swift SDK for voice agents and tasks on Apple Silicon built by Lucas Newman and yours truly. iOS, macOS, and visionOS developers can now build native apps with real-time, on-device audio intelligence: 🗣️ Text-to-Speech (TTS) 👂 Speech-to-Text (STT) 🔄 Speech-to-Speech (STS) 🎙️ Voice Activity Detection (VAD) and more. Only import the capabilities you need, nothing extra. Get started today and leave us a star ⭐️
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Day 1 of 3 days of MLX: Introducing MLX-Audio-Swift SDK 🚀 A modular Swift SDK for voice agents and tasks on Apple Silicon built by Lucas Newman and yours truly. iOS, macOS, and visionOS developers can now build native apps with real-time, on-device audio intelligence: 🗣️ Text-to-Speech (TTS) 👂 Speech-to-Text (STT) 🔄 Speech-to-Speech (STS) 🎙️ Voice Activity Detection (VAD) and more. Only import the capabilities you need, nothing extra. Get started today and leave us a star ⭐️

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160,927 просмотров • 5 месяцев назад

Introducing Audio Native. Our embeddable audio player that automatically narrates your blog or news site. Head to to customize your player. Check out for CMS platform integration guides. Your readers (and soon to be listeners) will thank you.
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Introducing Audio Native. Our embeddable audio player that automatically narrates your blog or news site. Head to to customize your player. Check out for CMS platform integration guides. Your readers (and soon to be listeners) will thank you.

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Congratulations to the Cohere team on the release of Cohere Transcribe Arabic! 🎉 Runs natively on mlx-audio (Python + Swift) from day-0 🚀 What's inside: → 2B params, Conformer encoder-decoder (audio-in, text-out) → Large Conformer encoder for acoustic representations + lightweight Transformer decoder for token generation → Built for Arabic dialects and Arabic-English code-switching → Auto-resampling to 16kHz + stereo→mono handling built into preprocessing → Apache 2.0 — fully open for community research Currently topping the Open Universal Arabic ASR Leaderboard 🥇 Get started today: 🐍 Python uv pip install -U mlx-audio 🍎 Swift

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16,454 просмотров • 18 дней назад

BREAKING 🚨: NotebookLM will be getting a new updated UI with 3 separate sections for Sources, Chat and Notes & Audio Overview 👀 This also comes with an "Interactive Audio Beta" where users will be able to interact with hosts in realtime 🤯
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BREAKING 🚨: NotebookLM will be getting a new updated UI with 3 separate sections for Sources, Chat and Notes & Audio Overview 👀 This also comes with an "Interactive Audio Beta" where users will be able to interact with hosts in realtime 🤯

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🗓️ Release Week Recap Big week. mlx-audio and mlx-vlm are now among some of the fastest-growing OSS projects. Here’s what we shipped last week. Gemma 4 on Apple Silicon Two awesome releases by our partner Google DeepMind & Google Gemma : > Gemma 4 12B — their new dense, unified multimodal model. It uses an encoder free audio path and simplified vision encoder. > Gemma 4 QAT — quantization-aware training checkpoints, optimized to run locally on consumer GPUs and edge devices, compressing the model while preserving the quality you expect from Gemma 4. On the audio 🎧 side we added support for 15+ new TTS, ASR & VAD models, faster long-form transcription, and an expanded OpenAI-compatible audio server. All local on Apple Silicon. Huge thanks to every contributor and my co-maintainer Lucas Newman. 🙏🏽
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🗓️ Release Week Recap Big week. mlx-audio and mlx-vlm are now among some of the fastest-growing OSS projects. Here’s what we shipped last week. Gemma 4 on Apple Silicon Two awesome releases by our partner Google DeepMind & Google Gemma : > Gemma 4 12B — their new dense, unified multimodal model. It uses an encoder free audio path and simplified vision encoder. > Gemma 4 QAT — quantization-aware training checkpoints, optimized to run locally on consumer GPUs and edge devices, compressing the model while preserving the quality you expect from Gemma 4. On the audio 🎧 side we added support for 15+ new TTS, ASR & VAD models, faster long-form transcription, and an expanded OpenAI-compatible audio server. All local on Apple Silicon. Huge thanks to every contributor and my co-maintainer Lucas Newman. 🙏🏽

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First, we are excited to share a number of new updates to our frontier video generation model, Gen-4.5 Soon you will be able to both generate and edit native audio with Gen-4.5 and edit video at arbitrary lengths with multi-shot editing.
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First, we are excited to share a number of new updates to our frontier video generation model, Gen-4.5 Soon you will be able to both generate and edit native audio with Gen-4.5 and edit video at arbitrary lengths with multi-shot editing.

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Our Spotify Canvas will be updated daily! Today's video is from SOUMA! Tomorrow will be...? 🤫 Share the Canvas screen on IG Stories or X, and you'll get an audio message from the member you applied for! 👇For details SpotifyJP #ONEOREIGHT #1OR8 #365withyou

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finetuning LLMs will never be the same! Unsloth just launched an open-source web UI to run and fine-tune 500+ LLMs without writing any code. key features - run models locally on Mac, Windows, Linux - train 500+ models 2x faster with 70% less VRAM - supports GGUF, vision, audio, embedding models - auto-create datasets from PDF, CSV, DOCX - self-healing tool calling and code execution - compare models side by side + export to GGUF to get started, I've shared links in the next tweet.

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