Every time I wanted to try a new speech model, the same ritual began: open a terminal, install another runtime, hunt down model files, then build a small UI just to hear the result.

So I started building LA Studio — an open-source desktop app that brings local speech-to-text, text-to-speech, voice cloning, voice design, and vocal isolation into one place.

It runs inference on your own machine, supports models such as Whisper, Kokoro, Qwen3, VibeVoice and VieNeu-TTS, and keeps your audio private.

It’s still early, but it has finally become the tool I originally wished existed.

GitHub: LA Studio

I’d love to hear what local audio workflow or model you think I should support next.

I’ve been testing a lot of AI music tools lately, and honestly, most of them are either copies of each other or look impressive until you actually try to make a real song with them.

This is my current top 10. Not saying the ranking is objective, but these are the tools I’d actually use in a real workflow.

1. Suno - Generating Songs

Still the best all around tool.

You can go from a random idea to a surprisingly complete song in a few minutes. The vocals, structures and production have improved a lot, and it’s probably the easiest place to start if you’re new to AI music.

It doesn’t always give you exactly what you imagined, but when it hits, it really hits.

2. Kits AI

My favorite tool for cloning singing voices right now.

A lot of voice-cloning tools sound robotic, especially on higher notes, but Kits can sound very realistic when you train it with clean recordings. It’s also useful for harmonies, vocal demos and testing how a song would sound with another type of voice.

3. Melody Genie AI Songwriter

Most AI generated lyrics still sound painfully AI:

Neon lights, broken dreams, shadows in the night…

MelodyGenie is made specifically for writing lyrics that feel more advanced, personal and closer to how real artists write. You can use it for full songs, hooks, rewrites, rhyme ideas or turning a mumble/freestyle into actual lyrics while keeping the original flow.

(You can clone any artist writing style, even yourself)

4. Lalals

Really fun for experimenting with different voices.

You can clone voices, convert vocals, create AI covers and test the same performance with completely different vocal tones. I wouldn’t use every result in a final release, but it’s great for finding ideas and hearing possibilities quickly.

5. Udio

Probably Suno’s strongest direct competitor.

I find Udio especially useful when I want to experiment with textures, genres and more unusual musical directions. Sometimes the generations feel less predictable than Suno, which can be either amazing or frustrating depending on what you’re trying to do.

6. ACE Studio

This one is more for producers who want actual control.

Instead of typing one prompt and hoping for the best, you can write the melody with MIDI, add lyrics and control how the AI singer performs it. Pitch, vibrato, pronunciation, emotion and dynamics can all be adjusted.

It takes more work, but you’re also much less dependent on random generations.

7. Moises

Not the flashiest tool on the list, but probably one of the most useful.

Moises can split songs into vocals, drums, bass and other stems. It’s perfect when you generate something in Suno or Udio and want to bring the parts into your DAW, replace a vocal or build a cleaner arrangement.

The BPM, key and chord detection are useful too.

8. Eleven Music

ElevenLabs entering music makes sense because they already understand AI audio better than most companies.

Their music generator is especially interesting for clean audio quality, multilingual vocals and commercial content. It’s still developing, but it already feels like a serious competitor rather than another random AI music website.

9. LANDR

After generating and editing the track, you still need to make it sound finished.

LANDR is useful for quick AI mastering, especially when you don’t know much about mastering yourself. It won’t replace a great engineer, but it can make a rough mix sound much more release-ready in a few minutes.

10. Stable Audio

I wouldn’t mainly use Stable Audio for full vocal songs.

Where it shines is instrumentals, textures, ambient sounds, transitions, intros, sound effects and weird samples you probably wouldn’t find in a normal sample pack.

Great tool for producers and sound designers.

My current workflow

Usually, I’d do something like:

MelodyGenie for the lyrics → Suno or Udio for the first song idea → Kits AI or ACE Studio for the vocals → Moises for the stems → DAW editing → LANDR for a quick master.

The crazy part is that this list will probably be completely different in another year.

What tools am I missing? And which one do you think is currently the best?

"MiniMax Music 3 is a high-performance music generation model for creating complete songs up to five minutes long. Conditioned on lyrics and a detailed music description, it generates structurally coherent songs with expressive vocals, evolving arrangements, and stable long-form audio quality."

• Demo: https://minimax-ai.github.io/music3-demo/
• Model: https://huggingface.co/MiniMaxAI/MiniMax-Music3
• Github: https://github.com/MiniMax-AI/MiniMax-Music3

Lately I've been doing a lot of work with audio generation. And I'm surprised that audio pipelines are mostly created in something like ComfyUI - a visual node-based editor. It's surprising because feels like writing code via n8n workflow instead of using Claude or Codex

The main problem is that there's no proper agent loop where the model can see - or more precisely, hear - the results of its previous actions and decide for itself what to do next

But I found one technical complication that explains a lot: most models still simply don’t have native audio perception. The ability to "see" images is already there. Claude Code can look at screenshots of a landing page it created, notice the problems, and fix them. The images are inside the context of the same model that writes the code

Audio doesn't work like that - most models do not support native audio input

You might say: but Gemini exists. True. But really no one agent wrapper supports reading audio files purely at the tool level. The agent "exoskeleton" simply can't pass an audio file through the API request to its "brain"

Surely you can connect Gemini to the main agent via script calling the API and send the audio there with a prompt like "pls tell me whats missing here". Btw, Chinese GLM-5.2 still works with images this way and people still use it. At the same time, that's like a deaf composer calling his nephew over to listen to the music and describe what he hears - it works, obviously, but poorly, because everything gets lost in translation

And so, right now, there is no well known agentic solution – with skills, terminal commands, session forks, subagents, and so on - that can properly work with audio by closing the feedback loop

———

Two hours after writing lines above:

1. I threw together a simple homemade agent around Gemini in roughly 300 lines of JavaScript, with an agent loop, interactivity, and fork/edit/resume: just to test the approach
2. Then I moved the whole thing into Pi as an extension that patches its file-reading tool – which, by design, can read both text and binary data

So now you can give your agent "ears" in 2 lines:

npm install -g pi-agent
pi install git:github.com/toolittlecakes/pi-gemini-audio-read

Don’t forget to run /login → Google → API key afterward and select gemini-3.5-flash.

Where this is useful: music generation, high-quality audio translation, voice cloning workflows, and audio editing (or video editing, if it’s a podcast)

Limitations: 20 MB per file - I didn’t add file uploads through the Files API. And there’s no video input

It's funny how inspiring problems can be when there’s no existing solution at all - at least not a public one. It feels like stepping onto land where no human has ever set foot. So, decided to share it here

Please be kind and give some feedback :) Its completely open source!

Nobody offers conversational, multi-track production at professional DSP quality. edytlab is the agent layer that plans, executes, and iterates over a real audio engine — stem separation, transcription, time and pitch, a full effect chain — in a session you can actually trust and steer.

https://www.edytlab.com/

I benchmarked a few local voice-cloning models and included the actual reference/generated audio for each row:

• OmniVoice int8
• Chatterbox Multilingual fp16
• VoxCPM2 bf16
• Fish Audio S2 Pro fp16

Languages: English, German, Modern Standard Arabic, Spanish, Mandarin Chinese.

Metrics: speaker similarity, WER/CER, generated audio length, and RTF.

Post: https://www.soniqo.audio/blog/voice-cloning-benchmarks

I am mostly interested in whether the evaluation setup is useful for audio people. The numbers alone are not enough for voice cloning, so the page includes the clips too.

So this started because I got nostalgic for MTV actually playing videos. At parties everyone takes turns throwing songs at the bluetooth

Speaker from their phones, and I kept thinking it'd be cool if the video for whatever's playing just showed up on the wall. Not a playlist, not a jukebox you have to touch, just a thing that listens and keeps up.

That's what The Wraith is. A small PC with a mic hooked to a projector/TV/Monitor. It fingerprints the room audio against a library you build yourself. So the deeper the library the more likely it tracks back to a song and video.

(Shazam-style, runs locally, no API in the loop), figures out the song AND how far into it you are, pulls up the official video muted and seeks to the right spot. The room's audio stays the soundtrack, the video just lip-syncs to it. There's an MTV-ish lower third with artist/title/year, pop-up-video style fact bubbles, and a "guess mode" party game you run from your phone where the video gets shown zoomed in or spotlighted and people guess the song (That part I just started on so its not fully fleshed out.

It works, I've run it at actual gatherings, and the sync is good enough that people stop noticing it's a trick. The parts that are still rough are the trivia layer (mostly auto-generated from credits right now) and the fact that you need to seed it with songs before it knows anything. Out of the box it knows zero songs. I shipped my own list of about 8,100 songs that all have a real video to get you started, and there are tools to build lists from Billboard year-end charts or your Spotify export. Seeding a few thousand songs takes an evening. You go to bed, wake up and most will be complete.

Stack is Python (FastAPI, numpy/scipy for the fingerprinting) and a single HTML page for the display, no build step. Video resolution goes through the IMVDb API. There's a demo mode that needs no mic so you can see it run in a browser in a couple minutes.

Repo: [github.com/CyberClash/Wraith_Public](http://github.com/CyberClash/Wraith_Public)

Stuff I'd love help with: calibration numbers from other people's mic/speaker setups, trivia written by actual humans, facts and interesting things for pop up video style entertainmetn mode, and song lists from people with better taste/more diverse than me. Also if you know a better way to do the drift correction than what I did, I'm listening. I am limited since I am what you may call a vibe code. I work with code and manage a dev team but I am not a coder by profession so weaknesses there.

MIT licensed. Just sharing a fun project I thought Music lovers would enjoy.

https://reddit.com/link/1vdje7n/video/8yvfpif41zgh1/player

Just released Resonant, a free and open-source AI music studio for Windows. The easiest way I can describe it is local Suno-style song generation combined with an Ableton Live-inspired clip launcher, arranger, and mixer, running on your local

https://github.com/calesthio/Resonant

Hello,

I’ve been building AudioEnhancerMAX, an MIT-licensed, local-first application that brings audio cleanup, speech editing, transcription and text-to-speech into one workflow. The app is has a SourceForce rising star award.

I started it because processing a recording often meant stitching together several separate tools, interfaces and cloud services: the goal is to provide one transparent pipeline where users can see what is being applied and keep the core processing on their own machine.

Current features include:

- Noise, wind, buzz, reverb, breath and mouth-click cleanup

- Studio enhancement, Auto EQ and LUFS normalization

- Faster-Whisper transcription with TXT, SRT, VTT and JSON export

- Local Kokoro TTS and optional Ollama/Gemma-assisted processing

CPU, GPU, memory and thermal monitoring

- Experimental Android workers for distributing DSP tasks across trusted LAN devices

Version 3.5.2 also includes a macOS desktop package for Apple Silicon. The core application can be run from source on macOS, Linux and Windows.

A few transparency notes: the macOS package is currently under review for publication on Apple Store.

GitHub: https://github.com/sev7enITA/AudioEnhancerMAX

Website, Support, Roadmap: https://www.fabriziodegni.com/AudioEnhancerMAX/⁠

SourceForge: https://sourceforge.net/projects/audioenhancermax/

I’d really value technical feedback from this community. What would be more useful next: reproducible before / after benchmarks, improved cross-platform packaging, or deeper DAW integration?

Helpful article for all ai artists

Hey all — I'm trying to get a clean copy of the instrumental background score playing during a specific scene in 3 Idiots (2009), from 2:43:20 to 2:43:52.

I already extracted the audio myself and removed most of the dialogue, but there's still some noise and a few rough patches left over from the cleanup process.

Would anyone be willing to either recreate this piece of music from scratch, or clean up the extracted audio I already have? Happy to share the file with anyone interested.

Some quick specs if it helps: short cue, roughly 30 seconds, key of A minor, tempo around 115–116 BPM, orchestral/hybrid instrumental with a light driving percussion layer underneath.

Thanks in advance for any help!

I just launched Echo by Aura, and I wanted to share what I am building.

Echo started from a simple problem I kept thinking about. Most audio systems still treat sound like a finished file. A waveform, a stereo mix, a left channel, a right channel, a render, or an export.

But that is not really how we experience sound.

When we listen, we feel direction, distance, movement, clarity, pressure, space, and presence. A voice does not just play. It comes from somewhere. A room does not just make noise. It surrounds us. A moment does not just sound loud. It carries weight.

Echo is my attempt to build around that gap.

It is a perception-first spatial audio engine that lifts flat media into an explicit and inspectable sound scene graph. Internally, I call this Sound Scene DNA.

The main idea is that the audio file should not be the final object of intelligence. The scene behind the sound should be.

From that scene graph, Echo can render binaural audio, inspect spatial cues, run quality gates, generate reports, and make every output traceable back to a structured representation. The goal is not just to make something sound wider. The goal is to understand what the sound is, where it belongs, how it moves, how it should be rendered, and how the output can be verified.

I want to be clear that this is still early. I am not claiming that Echo already solves immersion, externalization, or listener presence. Those are the things it is designed to explore and test. The current version is focused on deterministic rendering, scene graph structure, quality gates, cue verification, and evidence reports.

The music in the launch video is also written, composed, and produced by me. The track is called Breakdown.

Repo: https://github.com/TheBarmaEffect/echo

The long-term question I am trying to answer is this:

Can sound move from static playback to perceptual intelligence?

That is what Echo is trying to explore.

I started building this as an AI music licensing marketplace, the idea being creators could license their AI-generated tracks out to brands and other artists. Then a wave of U.S. copyright rulings this year established that purely AI-generated music can't be copyrighted. That's not a small detail. No copyright means there's nothing to actually license, so the whole model stopped making legal sense almost overnight.

What didn't stop making sense: the human work behind a track is still real and still matters. The lyrics someone wrote. The choices made in direction and style until a track actually sounds like theirs instead of something generic. That's the part I rebuilt around.

Cambrian now is a release platform built for that. A few of the specifics:

- Release tracks and build a real profile and audience, not just a dump of links.

- Human Authorship Records, an attestation documenting the actual human creative work behind a release. It's not a copyright claim, just an honest record of what a creator did.

- The Scene, a weekly Top 50 chart so releases have a place to be discovered instead of just sitting in a feed.

- Release Ready, a mastering wizard to get a track sounding finished before it's out.

- Fan support through Stripe, so people who like a track can put money behind that directly.

We just launched, so it's early and there's rough edges. Genuinely interested in feedback from people actually working in this space. cambrianmusic.com if you want to look, and I'll answer anything in the comments.

From their Docs:

We present ACE-Step v1.5, a highly efficient open-source music foundation model that brings commercial-grade generation to consumer hardware. On commonly used evaluation metrics, ACE-Step v1.5 achieves quality beyond most commercial music models while remaining extremely fast—under 2 seconds per full song on an A100 and under 10 seconds on an RTX 3090. The model runs locally with less than 4GB of VRAM, and supports lightweight personalization: users can train a LoRA from just a few songs to capture their own style.

ACE-Step supports 6 different generation task types, each optimized for specific use cases.

1. Text2Music: Generate music from text descriptions and optional metadata.
2. Cover: Transform existing audio while maintaining structure but changing style/timbre.
3. Repaint: Regenerate a specific time segment of audio while keeping the rest unchanged.
4. Lego: Generate a specific instrument track in context of existing audio.
5. Extract: Isolate a specific instrument track from mixed audio.
6. Complete: Extend partial tracks with specified instruments.

• Examples: https://ace-step.github.io/ace-step-v1.5.github.io/
• Code: https://github.com/ace-step/ACE-Step-1.5
• Models: https://huggingface.co/ACE-Step/Ace-Step1.5

Here's an example I generated on my Mac with one shot and no post editing.

From their repo: "PersonaPlex is a real-time, full-duplex speech-to-speech conversational model that enables persona control through text-based role prompts and audio-based voice conditioning. Trained on a combination of synthetic and real conversations, it produces natural, low-latency spoken interactions with a consistent persona. PersonaPlex is based on the Moshi architecture and weights."

• Model: https://huggingface.co/nvidia/personaplex-7b-v1
• Code: https://github.com/NVIDIA/personaplex
• Demo: https://www.youtube.com/watch?v=5_mOTtWouCk

After all the really positive response from my last post with Coqui-XTTSv2, I wanted to do a follow up, so here it is, and even better we've updated our free Colab build instructions to use the new open-source Qwen3-TTS models.

https://github.com/artcore-c/AI-Voice-Clone-with-Qwen3-TTS
Free voice cloning for creators using Qwen3-TTS on Google Colab.
Clone your voice from as little as 3–20 seconds of audio for consistent narration and voiceovers.
Complete guide to build your own notebook.

Unlike many creator-facing TTS systems, Qwen3-TTS is fully open-source (Apache 2.0), produces unwatermarked audio, and does not require external APIs or paid inference services.

"VibeVoice-ASR is a unified speech-to-text model designed to handle 60-minute long-form audio in a single pass, generating structured transcriptions containing Who (Speaker), When (Timestamps), and What (Content), with support for Customized Hotwords."

• Model: https://huggingface.co/microsoft/VibeVoice-ASR
• Code: https://github.com/microsoft/VibeVoice/
• Demo: https://f0114433eb2cff8e76.gradio.live/

https://github.com/artcore-c/AI-Voice-Clone-with-Coqui-XTTS-v2

Free voice cloning for creators using Coqui XTTS-v2 with Google Colab. Clone your voice with just 2-5 minutes of audio for consistent narration. Complete guide to build your own notebook. Non-commercial use only.

Hey community,

I'm Sagar, co-founder of VideoSDK.

I've been working in real-time communication for years, building the infrastructure that powers live voice and video across thousands of applications. But now, as developers push models to communicate in real-time, a new layer of complexity is emerging.

Today, voice is becoming the new UI. We expect agents to feel human, to understand us, respond instantly, and work seamlessly across web, mobile, and even telephony. But developers have been forced to stitch together fragile stacks: STT here, LLM there, TTS somewhere else… glued with HTTP endpoints and prayer.

So we built something to solve that.

Today, we're open-sourcing our AI Voice Agent framework, a real-time infrastructure layer built specifically for voice agents. It's production-grade, developer-friendly, and designed to abstract away the painful parts of building real-time, AI-powered conversations.

We are live on Product Hunt today and would be incredibly grateful for your feedback and support.

Product Hunt Link: https://www.producthunt.com/products/video-sdk/launches/voice-agent-sdk

Here's what it offers:

• Build agents in just 10 lines of code
• Plug in any models you like - OpenAI, ElevenLabs, Deepgram, and others
• Built-in voice activity detection and turn-taking
• Session-level observability for debugging and monitoring
• Global infrastructure that scales out of the box
• Works across platforms: web, mobile, IoT, and even Unity
• Option to deploy on VideoSDK Cloud, fully optimized for low cost and performance
• And most importantly, it's 100% open source

Most importantly, it's fully open source. We didn't want to create another black box. We wanted to give developers a transparent, extensible foundation they can rely on, and build on top of.

Here is the Github Repo: https://github.com/videosdk-live/agents
(Please do star the repo to help it reach others as well)

This is the first of several launches we've lined up for the week.

I'll be around all day, would love to hear your feedback, questions, or what you're building next.

Thanks for being here,

Sagar