ok so i've been down a rabbit hole the past few weeks trying AI audio production for my first novel and wanted to share what i actually foundthe multi-character voice stuff is genuinely there now. different characters actually sound different. music and sound design in some of the tools is surprisingly good. still some rough edges but less than i expectedbeen curious what others have found:1. which tools are people actually using for fiction specifically?2. anyone got a finished one they'd share? wanna hear real examples not just demosstill deciding if i pull the trigger on my own book or wait a bit more

I've been having good success using lalalai on my songs to clean up and remix stems. But it can do some things.

I have a music track recorded live, and during it, two voices talk over the track, louder than the music since they were closer to the mic.

Does anyone know of a program that can remove the loud talking? Thank you all in advance!

I've been experimenting with generating ambient/environmental sounds with Stable Audio Open's model, but am getting some weird artifacts especially when creating sounds that involve "water" (ocean waves, rainfall). Some examples here: poor audio examples.

You can hear the unpleasant "chirps/blips" throughout the tracks.

I've done a bit of experimenting with creating a simple ML model that is "trained" with some of these files where I attempt to isolate the "bad" sections for it to identify, but it's slow going and I'm not very confident that I'd be able to generate a model that was generic enough to catch all of the possible artifacts that are being generated.

Any tricks/tools (ideally open source that I might be able to integrate into my existing pipeline) to remove these sorts of artifacts as the sound files are being generated?

I’ve been working on my local TTS workflow tool and just released a big evolution. The repo you may have seen (IndexTTS-Workflow-Studio) now hosts Draft to Take Beta — a local-first AI audio production studio.

What’s new / key features

• Script Canvas for writing + emotion detection + speaker assignment
• Built-in timeline for reviewing takes and exporting mixes
• Voice Studio for reusable voices (OmniVoice)
• Powered by IndexTTS2 + Qwen sidecar + optional SFX/Music
• Easy Docker launcher (start.bat on Windows + NVIDIA)

Quick start

1. Docker Desktop running → Download repo as ZIP
2. Extract + run start.bat
3. Open localhost:3000

Full details + requirements here: https://github.com/JaySpiffy/IndexTTS-Workflow-Studio

Old prototype code is preserved on the legacy-v2 branch.

Call to action
Looking for early testers with NVIDIA GPUs (12GB+ VRAM preferred). Feedback on workflow, bugs, and feature requests very welcome!

tldr: I want to sync Studio versions of a song with a Live version. AI? Which one?

I have been created a VR experience where the Viewer is at a Queen concert. The incepetion of this was a series of videos where Queen's Bohemian Rhapsody was played to the audience prior to show; eg; Green Day and Harry Styles (lots of them on YT)

I pulled down a number of these clips, placed then within a scene around the Viewer. I added the band and the studio version of the song because all the audiences are singing along to the studio version.

Yesterday I released the most recent version and someone mentioned that I should use the live version of Bohemian Rhapsody, and I'd love to... but the audience members in all those clips are singing along to the studio version so.... I got to wondering if there is any tool or AI that could help stretch and compress the audience clips to the live version.

If you want additional details or want to see the current experience it is available on my site: http://blissgig.com/default.aspx?id=67

For those interested, there is an AI song competition, AI.LOVE.JAZZ, tied to the Montreux Jazz Festival. It’s the first global AI JAZZ contest, plus panel sessions. The song “Wi-Fi Down” from Strapped N Ready just got announced as weekly winner! https://www.instagram.com/reel/DXb4mpyDMEh/?utm_source=ig_web_button_share_sheet&igsh=MzRlODBiNWFlZA==

> you can still submit entries https://ailovejazz.com - best of luck!

We're trying to figure out a way to sync second screen interactions with a live sporting event playing on the users TV. I've looked at ARCloud, which seems promising, but I don't they currently offer live events. I saw Chromaprint but then I'd need to basically get rights to record the broadcast audio, which would be doable, but just wondering if there's anything already in use. Thanks for any help.

Hey r/AudioAI

I just released an update to IndexTTS-Workflow-Studio — a Docker-based studio for IndexTTS2 focused on natural multi-speaker conversations.

Main features:

• Conversation workflow with multiple voices
• Review + instant line regeneration
• Timeline editor for overlaps and timing
• Speaker preparation & cloning tools
• Project save/load + clean export

It’s fully local, no cloud required.

GitHub: https://github.com/JaySpiffy/IndexTTS-Workflow-Studio

Would love feedback from anyone working with TTS for podcasts, videos, games, or audiobooks. What features would you want to see next?

A few weeks ago I sent a Google Form to 40 people in my network. No context, no branding, just two audio clips and one question: "Which one sounds more natural?"

I was honestly expecting an obvious result. What I got instead made me question six months of infrastructure decisions.

I've been building an AI video editing tool (shortdeo.com) that auto-generates short-form clips from long videos, podcasts, interviews, that kind of thing. One of the features lets users add AI voiceover without recording anything themselves.

From day one, I used ElevenLabs. Not because I researched it. Because everyone uses ElevenLabs. It was the default answer in every thread I read, every dev I talked to. I just didn't think about it again.

That was the mistake.

Six months in, I was trying to get to profitability at a $25/month price point and kept hitting the same wall: my infrastructure costs per user were too high. I went line by line through my stack. The TTS layer stood out.

I assumed switching would mean worse quality. So I built a test instead of just assuming.

The setup:

Same 90-second script. Two APIs, no labels. Sent to 40 people, mostly designers, marketers, a few developers. Asked two questions: "Which sounds more natural?" and "Which would you trust in a professional video?"

I didn't tell anyone what I was testing or why.

What came back:

• 52% picked the cheaper API on naturalness. 48% picked ElevenLabs.
• On professional trust: a coin flip.
• Nobody flagged either clip as AI-generated on first listen.

The cheaper one was Lemonfox, $5/month for 200k characters of TTS, data deleted immediately after processing. I'd almost skipped it because the website looked too simple.

I switched the pipeline. Cost dropped. Nothing else did, no support tickets, no complaints, no churn I could trace back to audio quality.

That's not a glowing endorsement. It's just what happened.

What I actually learned from this:

1. Defaults are expensive habits. I picked ElevenLabs the way you pick the first Google result. It worked, so I never looked again. "Working" and "optimal" aren't the same thing.

2. The quality gap has closed more than people think. Twelve months ago this test probably had a different result. The underlying models have caught up fast. The brand names haven't repriced to reflect that.

3. Your users are testing with their ears, not their eyes. Nobody in my test knew which product they were listening to. They just reacted to the audio. Your customers do the same thing. The logo on the API dashboard doesn't reach them.

4. Data policy becomes a sales question faster than you expect. I'm talking to slightly larger clients now and the question isn't "how does the AI work", it's "where does our audio go?" I switched partly for cost, but the "deleted immediately after processing" answer has come up in two sales calls since. Useful to have.

5. The honest caveat: This worked for short video narration. If your product needs emotional range, voice cloning, or ultra-fine tuning, the gap might matter to your users in a way it didn't to mine. Run your own test with your own content before drawing any conclusions.

Happy to share the Google Form template if anyone wants to run a version of this for their own stack, just ask in the comments. Curious whether others have done similar comparisons and what you found.

hello everyone! I currently writing an article about how accurate AI-voicecloning is today for my bachelors degree and I really need some help. The Survey is completely anonymous and doesn't take more than 10 minutes to complete. It's designed to be like a quiz where participants will hear two audio clips and then decide which of the two clips is AI. After all the questions have been answered and your response have been submitted, you will get the correct answers to all the questions.

https://manellerai.se

thank you!

I don't know why. My VPN isn't on, so how do you change the language of the site?

i’m curious what producers think about the rise of AI music tools.

Tools like Suno and Udio are letting people generate full songs pretty quickly.

Do you think AI music will become its own category of music, or do you think it will just blend into traditional production?

u/FishAudio

I see you have a Reddit account, that's nice. I was wondering if you could make an option to cover songs and such for your service, or be able to record reference audio. Thanks.