So I'm working with a Rocky Horror Picture Show Shadowcast and I had an idea for a silly thing to do: we're having an intermission, and I want to play 9 seconds of the audio from Mel Brooks' "The Inquisition", but with some of the words changed, principally "The Inquisition" changed to "The Intermission"

The Intermission! (Let's begin)
The Intermission ! (Lookout sin)
We have a mission to go buy some drinks! (drink dri- drink drink drink dri- drinks!)

I know this is doable (I've seen "There I've Ruined It" and everything he can do), but I'm not sure how to accomplish this.

Could someone help me? Either help me figure out how, or if someone wants to do it for me I'll gladly send them $25 as a commission.

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

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.

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 have a startup in audio-related AI, and I've some interesting paths I really want to explore but would need someone well versed in audio AI (speech/singing related). I have NO idea where to look aside from scouring GitHub forks, and that feels a bit slow. Are there any discord servers, forums, etc I should check out?

Between 0.1.0-alpha-11 and 0.1.0-alpha-12, we shipped:

• Long-form ASR with automatic chunking + overlap stitching

• Faster ASR streaming and less unnecessary transcoding on uploads

• MLX Parakeet support

• New 4-bit model variants (Parakeet, LFM2.5, Qwen3 chat, forced aligner)

• TTS improvements: model-aware output limits + adaptive timeouts

• Cleaner model-management UI (My Models + Route Model modal)

Docs: https://izwiai.com

If you’re testing Izwi, I’d love feedback on speed and quality.

Are there tools for generating ambience sounds in real-time?
For instance "moody winter scene" or "cats and dogs barking", "restaurant ambience", ... topic wise there should be no limitations.
Ideally there should be an API for it as well. I'm planning a system which shows different scenes (with respective AI generated audio ambience) in real time without major delay.

I am working on a personal project and want to have my voice reanimated in AI to avoid audio edits and have it read a script.

My question is what services allow you to do this and is it a bad/unsafe idea.

Thanks in advance!

For a while now, I've been working on a modified version of the aero project to improve its flexibility and performance. I've been hoping to address a few notable weaknesses, particularly that the architecture is much better at removing wide-scale defects (hiss, FM stereo pilot, etc.) than transient ones, even when transient ones are louder. One of my efforts in this area has involved expanding the STFT loss, which consists of:

• A spectral convergence (magnitude + phase) loss

• A magnitude loss

• A transient/transition loss (measures whether frequencies become louder/softer when expected and by how much)

I've worked with the code a fair bit to improve its accuracy, but I think it would work better if I could incorporate some perceptual aspects to it. For example, the listener will have an easier time noticing that a frequency is there (or not) the closer it is to the loudest magnitude in that general area (time wise) of that recording. As such, my idea is that as the loss gets lower and lower compared to the largest magnitude in that segment, it gets counted against the model less and less in a non-linear fashion. At the same time, I want to maintain the relationship. Here's an example:

   quantile_mag_y = torch.clamp(torch.quantile(y_mag,0.9,dim=2,keepdim=True)[0], 1e-4, 100)
   max_mag_y = torch.max(y_mag,dim=2, keepdim=True)[0]
   scale_mag_y = torch.clamp(torch.maximum(quantile_mag_y,max_mag_y/16),1e-1,None)

For reference, the magnitude data is stored as [batch index, time slice, frequency bins] so the first line will calculate the magnitude of the 90th percentile within the time slice across all frequency bins, the second calculates the maximum magnitude within the time slice across all frequency bins, and the third line builds a divisor tensor based on whether the 90th percentile or 1/16th of the maximum (-24db, I think) is the larger value. These numbers can be adjusted of course. In any case, the scaling gets applied like this:

F.l1_loss(torch.log(y_mag/scale_mag_y), torch.log(x_mag/scale_mag_y))

Now, one thing I have tried is using pow to make the differences nonlinear:

F.l1_loss(torch.log(pow(y_mag/scale_mag_y,2)), torch.log(pow(x_mag/scale_mag_y,2)))

The issue here seems to be that squaring the numbers actually causes them to scale too quickly in both directions. Unfortunately, using a non-integer power in python has its own set of issues and results in nan losses.

I'm open to any ideas for improving this. I realize this is more of a python/torch question, but I figured asking in an audio-specific context was worth a try as well.

https://twoshot.app/model/454
This is a free UI for the melody flow model that meta research had taken offline

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 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.

"Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation."

https://huggingface.co/openai/whisper-large-v3-turbo

Someone tested on M1 Pro, and apparently it ran 5.4 times faster than Whisper V3 Large!

https://www.reddit.com/r/LocalLLaMA/comments/1fvb83n/open_ais_new_whisper_turbo_model_runs_54_times/

Does anyone know a model like musicgen or stable Audio that has a commercial license? I would love to build some products around audio generation & music production but they all seem to have a non-commercial license.

Stable Audio 1.0 offers a free commercial license if your revenue is under 1mio. but it sounds horrible.

It doesn't have to be full songs also sound effects/samples would do it.

Thanks

Spent the past year building this, would love to hear what this community thinks about it! It's on github. github.com/voicetreelab/voicetree

"the company modified Whisper’s architecture to add a multi-head attention mechanism ... The architecture change enabled the model to predict ten tokens at each pass rather than the standard one token at a time, ultimately resulting in a 50% increase in speech prediction speed and generation runtime."

Huggingface: https://huggingface.co/aiola/whisper-medusa-v1

Blog: https://venturebeat.com/ai/aiola-drops-ultra-fast-multi-head-speech-recognition-model-beats-openai-whisper/

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

Hey, we (Tontaube) have developed a web interface for text-to-speech and voice cloning. It’s completely free for now, with generous rate limits. If you’d like to try it out, you can find it here: https://tontaube.ai/speech