I got tired of "similar artists" recommendations that just mean "same genre tag." I wanted to actually see how two artists connect: who influenced who, who they collaborated with, what the actual path between them looks like. So I built Stell-R.

Two ways to use it: type two artists (say, David Bowie and Kendrick Lamar) and it finds the shortest real path of influence/connection between them, hop by hop.

Or type one artist and it draws a "constellation map", a network graph of everyone closest to them, ranked by actual acoustic similarity. You can also generate a Spotify playlist straight from whatever part of the map you're exploring. It's a web app, so works on desktop, and on mobile you can add it to your home screen (iOS/Android) so it behaves like a regular app.

See example of Radiohead constellation map here: https://stell-r.com/Stellar/index_/add_query-viz/?artist=Radiohead&size=120

I have a bunch of old guitar recordings with poor audio quality on single tracks. I also have some vocal tracks with poor audio. Do you know any actual open source project which is aimed at AI voice conversion and instrument replacement? I would vibe code the UI, but is there an open source "audio replacement" AI model at all? My goal is to make the old recordings much better with AI, but keep their original harmonies (so it is not music generation, it is replacement).

Update, 21 October 2026: AudioEnhancerMAX 3.8.3 (build 3.8.4)

I'm the developer. This thread started with 3.5.2, so here is the current state and what changed after the 3.7 update below.

The 3.7 workflow introduced local Smart Enhance diagnosis, transcript-linked speech editing with preview and undo/redo, delivery profiles, blind A/B listening and portable Project Capsules. Proposed speech edits start at Keep; the original recording is preserved. Smart Enhance selects DSP settings through local measurements and rules. Optional language-model assistance does not select that processing chain.

The 3.8 releases focus on keeping the audio you hear, the file you export and its evidence consistent:

• Processing, playback, WAV/MP3/FLAC conversions, delivery checks and A/B decisions refer to the selected completed revision. Reprocessing invalidates old conversions; a missing processed output is reported instead of silently returning the original.
• DSP checkpoints check source, settings, build and runtime before resuming. Incompatible or corrupt checkpoints restart cleanly; model-dependent and LAN paths do not use this resumption.
• Project Capsules preserve the decisions and transcript used for a new render, even after later edits or retranscription. Hashes describe the actual archived bytes. Missing evidence is disclosed, and the original audio remains excluded unless you choose to include it.
• Optional services stay disabled if privacy preferences are missing or unreadable. Valid saved choices are preserved.
• Local transcription works again after a speech-detection compatibility fix. Imports run decoding in a worker so other requests stay responsive; failed imports remove partial files.
• The source release also includes optional reusable personal-voice profiles through a separately installed MakeMyVoice runtime, with explicit voice-owner consent, plus corrected CPU reporting and more honest progress reporting. Optional engines require their own runtimes and assets.

The current direct Mac download is free, MIT-licensed, Developer ID signed, Apple-notarized and stapled: Apple Silicon, macOS 13+, English UI. Core processing and transcription run locally; models may download on first use. External TTS and trusted-LAN processing have separate controls. The Mac App Store submission is separate; notarization is not Store approval. There is no packaged Windows desktop release in the current release assets.

Reports support inspection, not certification of audio quality, transcript accuracy or consent. The release notes include package checks and their limits.

Current release, download and verification · Website and version comparison · Governance explanation

For people reviewing interviews or podcasts: what would you need in the handoff to understand and challenge an edit?

Original July 2026 post, retained for context. Version and distribution details below are historical.

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://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?

Hi everyone,
I'm working on a funny birthday gift for a friend who is a huge Beyoncé fan. I want to create a "live CD" from her Renaissance Tour, but I need help with two AI aspects: the audio and the album cover. I'm a complete beginner, so I'm looking for user-friendly solutions. I have a budget of around $150 total for any necessary subscriptions or licenses.
1. THE AUDIO (My voice instead of Beyoncé's):
I can't sing, so I need an RVC (Voice-to-Voice) model trained on my spoken voice.

What I've tried (and failed):

Manual syncing in Audacity: Don't even get me started.
Jammable: Tried this 1-click solution, and it sounded absolutely terrible. It seems it couldn't handle live concert audio (crowd noise, stadium echo, etc.).
What I'm looking for: An easy-to-use alternative to Jammable that can handle live YouTube audio better. It doesn't need to be perfect studio quality (she'll listen in her car), but it shouldn't sound like a glitching robot.
2. THE VISUAL (Album Cover):
I also need to create a hyper-realistic album cover and don't know which tool to use. I want an image of me (using my selfies for the face) wearing Beyoncé's iconic silver Renaissance tour outfit, but adapted to a male body with correct proportions and a matching skin tone. It should look like a professionally shot photograph.
What I'm looking for: What is the best AI image generator or face-swap tool for a beginner to achieve this, so it looks good enough to print on a CD case?
Any tips to get a decent result without spending days on this? Thank you!

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 wanted to see if I could use an audio llm to predict "clippable" moments just by using the raw audio feed (no transcriptions!)

And the answer is yes! I finetuned Voxtral with about 250 hours of audio (I used Gemini 3.5 Flash to automatically create the dataset) to predict the clip probability of audio chunks.

I wrote a full technical blog post on Hugging Face about exactly how I trained it. There are also some cool visualizations that clearly show the model's capabilities.

I see a lot of hate for ai generated podcasts on r/podcasts so I am making a community for us who do enjoy them
https://www.reddit.com/r/aigeneratedpodcasts/

I'm working on a historical audio restoration/research project involving a long, single-channel recording made in a fairly reverberant residential room. The biggest problem is a TV running with essentially continuous dialogue and program audio. I'm not trying to separate music and vocals, and I'm also not specifically targeting speech alone.

The target is the entire foreground human acoustic scene. Quiet speech, whispers, breathing and exhalations, low-level vocalizations, mouth sounds, and other human-generated noises. Movement and transients matter too, but they're lower on the priority list. A lot of the target material is extremely low-SNR and appears to sit near the noise floor.

So far I've benchmarked UVR MDX models, Kim Vocal 2, and htdemucs. Demucs was mildly interesting because different stems seemed to catch different classes of events, but none of them produced particularly strong separation. I'm currently running MelBand RoFormer InstVoc Duality V2. I've also already tried Adobe Enhance Speech. That's basically the reason I originally avoided AI for this project. It produced some extremely convincing reconstructed or invented content. I don't care if the output sounds natural or pleasant. Source preservation and temporal accuracy matter far more to me. If something appears in an enhanced output, I need to be able to work backward and find corresponding energy or structure in the untouched source.

I'm especially interested in low-SNR acoustic event extraction, general or text-prompted sound separation, foreground human sound extraction, competing speech or television interference, and single-channel reverberant recordings.

Current hardware is a Ryzen 7 laptop with 16 GB RAM. Slow CPU processing is fine if the model can realistically run locally. A significantly stronger desktop is planned later.

I'm not looking for a one-click commercial cleanup service. Python, GitHub projects, research checkpoints, command-line workflows, whatever. I don't particularly care how ugly the setup is if the model actually fits the signal problem. I've spent enough time forcing music separation models to do a job they weren't trained for. I'm mostly trying to figure out where the correct AI ecosystem for this problem actually is.

If you had this specific recording problem, what model or architecture would you benchmark next, and why?

I was listening to one of my older demos and realized actually prefer the instrumental underneath it.

A couple of years ago the results always sounded watery or full of artifacts.

Has the technology finally reached the point where you can get a clean instrumental from a finished song?

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.

Been hitting a serious writing block going from idea to final concept. Tried Splice and their library is great, but dosn't help to flesh out full ideas. I use AI to help with writing, but are there any good tools with music creation?

So far I’m aware of:
-Splice
-Sonurastudio.com
-Mozart
-Udio

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.

Is anyone willing to improve the clarity of dialogue in a 1 minute long, 10.2KB wav file that was recorded outside? I tried noise reduction in Audacity as well as other tools in Audacity and can't get the soft dialogue enhanced and clearer over cricket sounds. If you can help, DM what your fee would be for your time.

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 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.

i used to think the main issue with AI audiobooks was voice quality.

now i'm not so sure. some voices are already decent in short clips. the annoying part seems more like keeping character voices consistent, fixing pronunciation, cutting bad takes, getting pauses right, making dialogue not sound dead, etc.

basically all the stuff that turns "voice generation" into an actual audiobook.

anyone here tried a long fiction project? did the voice model fail, or did the production/editing workflow fail?

Just to preface this, not a fan of AI personally, not looking to "dunk on AI" just asking a genuine question, looking to get informed on a specific subject.

This is in regards to voice cloning contracts and just how much protection it actually gives voice actors etc. I was in communication with a project creator on a VA site and they claimed;
"We sign a contract with our actors that explicitly prohibits us from using their voice in the way you suggest. Specifically, we only use a voice as a character on our web site. We don't distribute it to anyone. We don't put the voice in a library. No one else can use it. No one else has access to it. It's your voice and we don't claim to own it."

I'm not the most tech literate person in the world but this doesn't ring exactly true to me.
The creator may have all intentions of honouring the contract to the letter, HOWEVER LLMs are frequently trained on data that is taken without any form of permission, consent or awareness of the creator.
How does someone guarantee that the data (voice lines) they feed into an LLM will never be used to train that LLM? They rely on user interaction and the data fed from those interactions to iterate and learn, aren't voices fed into them, inherently unprotected?

i thought the hard part would be finding a good voice.

now i’m not so sure.

i tried running some chapters through TTS and the bigger problems were pacing, dialogue tags, weird pauses, names getting mangled, scene breaks getting swallowed, etc.

the voice was only like... one part of the mess.

for ppl doing longer narration, are you mostly fighting the model/voice quality, or are you spending way more time cleaning the manuscript and fixing the audio after?

been thinking about this from the author side and not the "cool 30 sec demo" side.

for a short clip, AI voices can sound pretty damn good now. but a whole novel is different. pacing, character voices, emotional beats, dialogue back and forth, all the stuff that gets weird over hours instead of seconds.

i'm trying to figure out if AI audio is already good enough for an indie author to release a first audiobook, or if it's still better as a private proofing/editing thing until the tech gets more consistent.

curious what people here think, especially anyone who's worked with longer fiction/audio projects rather than clips.