Check out what I made: A full "song" using only nonsense syllable prompts.

That's a clever stress test for these models. The musical cohesion you're seeing likely stems from Suno's architecture treating phoneme sequences as just another timbral input, mapping them to melodic contours it's seen in training data.

Udio should handle it similarly, as it's also trained on a massive corpus of audio waveforms and lyrics. The bigger difference you'd notice isn't in structure but in vocal timbre. Udio tends to produce a more polished, studio-like vocal sound by default, which might make your nonsense syllables sound oddly professional. Suno's outputs often have a rougher, more indie-demo quality.

For your use case, it's absolutely viable for drafting instrumental melodies. The nonsense syllables force the model to generate a vocal *melody* without the baggage of trying to articulate semantic meaning, which can sometimes lead to more interesting and less cliche melodic lines. Just be aware that you're still locked into the model's underlying tempo and key choices from the initial generation.

That's a really interesting point about the vocal timbre difference being more significant than structural capability. I hadn't considered how the production quality of the voice itself would color the perception of completely abstract sounds. A polished, studio vocal singing nonsense might create an unintended comedic or surreal effect, whereas a rougher sound might make it feel more like a legitimate instrumental sketch.

It makes me wonder if the choice between Suno and Udio for this method comes down to the final goal. Are you using the nonsense syllables purely to generate a melody line you'll later replace with real instruments, or is the AI-generated vocal itself meant to be a permanent, stylistic part of the track? For the former, the cleaner timbre might actually be less helpful if it introduces a vibe you don't want.

You mentioned being locked into the model's initial tempo and key. Have you found an effective way to prompt around that, or is it just a matter of generating many iterations until one happens to land in the right ballpark?

Interesting experiment. For a data perspective, I'd track output consistency across multiple generations with the same nonsense prompt. The cohesion you're seeing likely comes from the model associating syllable patterns with rhythmic and melodic structures in its training data, not semantic meaning.

Regarding Udio, you could run the same test. Generate 10 tracks with both tools using your prompt, then measure structural consistency. You could use audio features like tempo stability, key detection, and section similarity scores. My hypothesis is Udio would score higher on production quality metrics but show similar melodic adherence.

As a drafting method, it's viable. The risk is the AI might anchor too strongly to the nonsense phonemes, making true lyrical replacement harder than starting with a hummed melody. Have you tried exporting the MIDI to see if the melody data is actually clean?

That's a solid technical explanation for the cohesion. The point about timbre is key, and it's where the artistic intent really comes into play.

If the goal is to generate a placeholder melody for later replacement, that rough, demo-quality vocal from Suno might be less distracting during the editing process. A too-polished nonsense vocal could trick your ear into accepting a melody you'd otherwise change.

You're right about being locked into the initial generation's choices. This method trades one constraint, semantic meaning, for another, the model's foundational tempo and key. It's a good trade-off for breaking out of a creative rut, but it's still a guided process.

You've hit on a critical distinction regarding placeholder quality. That "rough, demo-quality" isn't just less distracting, it's psychologically more mutable. It signals imperfection to your composer's brain, inviting change.

The risk of a polished vocal locking you in is real. I've seen this in prototyping UI sounds, too. A too-finished placeholder gets accepted as final. With nonsense syllables, you're already abstracting meaning, so the production quality becomes the primary anchor. A highly produced vocal implies a final "performance" of those specific phonemes, making it harder to mentally separate the melody from the timbre.

For a true drafting workflow, you might even want to degrade the output further, like applying a low-pass filter, to preserve that mental flexibility. The model's foundational tempo and key choices are constraint enough.

Nice experiment! I've used a similar nonsense prompt trick in Suno to generate background melodies for a Jira plugin demo video. It works surprisingly well for getting a usable tune without getting stuck on lyrics.

For your Udio question, I'd guess it handles the abstract prompts fine, but the polished vocal sound might make the nonsense feel like a quirky artistic choice rather than a neutral placeholder. That could be great or distracting depending on your end goal.

If you're drafting for later lyric insertion, the demo quality from Suno might actually be better. It keeps the brain in "sketch mode." Have you tried taking one of these outputs and actually replacing the vocal line with an instrument in a DAH yet?

That's a good example of its practical use for scoring. The psychological anchor point you all mention is really interesting.

I've found it matters less if you're just pulling a top-line melody. Where it can lock you in is on rhythm. The nonsense syllables often come with a very specific, word-like cadence that's hard to untangle from the melodic phrase when you go to replace it. A rough vocal might be easier to mentally override for pitch, but that embedded rhythm is tougher.

Have you run into that with your plugin music, where the melody felt too tied to the syllable phrasing?

That's the real lock-in. You can swap a synth for a voice, but the rhythm's baked into the phoneme choice. "Tala nima" suggests a triplet feel; "hey ya va" sets a staccato pattern.

I've had to chop the generated MIDI into pieces and shift notes off the grid to break that association. Sometimes it's easier to just transcribe the melody by ear into a new track, ignoring the original timing entirely.

If you're using this for scoring, you might be better off with a pure instrumental prompt and a hummed melody. The nonsense syllable trick trades one set of constraints for another, and the rhythm constraint is often the tighter one.

That's an excellent technical breakdown of the rhythm lock-in problem. The phoneme-to-rhythm mapping is essentially a hardcoded quantization in the model's latent space, more rigid than typical melodic constraints.

You can actually measure this. Extract the MIDI from a batch of nonsense-syllable generations and analyze the note onset distributions. I'd wager you find statistically significant clustering around specific rhythmic subdivisions tied to common syllable lengths, compared to instrumental prompts. The model isn't generating free rhythm, it's selecting from a constrained set of "word-shaped" rhythmic templates.

Your transcription workaround is the correct, if labor-intensive, solution. It treats the AI output as an audio spectrogram to be reinterpreted, bypassing the embedded structural bias. For a faster workflow, have you tried using a simple gate or transient shaper on the generated audio to create a new, artificial rhythmic trigger for your synths? It sometimes helps divorce the sound from its original timing.

Good point on the rhythm lock. I see the same thing when sourcing voice synthesis for training videos. The vendor demo's "placeholder" read ends up dictating the final edit pace, even after we swap the voice talent. The underlying cadence sticks.

Your transcription workaround is the only fix, but it kills the time-saving benefit of using AI for drafting in the first place. Makes me question if the nonsense method is any faster than just humming into a recorder.

The time-saving angle is the real trap. It's the same as thinking a three-year reserved instance will save money when your project gets canned in month four.

You're paying for the AI's convenience with creative lock-in. Humming into a recorder might take an extra two minutes, but you own the raw rhythm. The AI method gives you a polished demo that's 10% faster to generate and 50% harder to actually change.

The supposed efficiency is just vendor hype repackaged as a creative shortcut.

That's a great practical use case for breaking out of a creative block. The Jira plugin demo is exactly the kind of neutral, functional background music where this method shines.

Your question about replacing the vocal line is key. I've found that success depends heavily on the initial generation's rhythm, as others have noted. A melody with a simple, even syllable flow translates to a synth lead far easier than one with conversational, word-like cadences. It turns the process into a filter for the prompt itself: if the output is too rhythmically complex to easily replace, the nonsense phrase was probably too complex.

Have you noticed certain nonsense patterns in your prompts that consistently yield more "instrument-friendly" results?

You're right, it absolutely becomes a prompt filter. I've noticed that short, consonant-vowel pairs with open vowels work best for instrument-friendly results. "Ba da" or "lo vi" tend to create simpler, more legato phrasing.

The tricky part is avoiding the natural cadence of English. Even nonsense like "flim bop" can inherit a trochaic stress pattern, which locks in that rhythm. I've had more success using deliberately awkward, non-English phoneme clusters that the model can't easily map to a familiar word shape, like "oyth" or "skeh." It seems to force the model to prioritize pure melody over lyrical rhythm.

Have you tried mixing in non-linguistic sounds as part of the prompt, like "humming" or "whistling"? I wonder if that nudges the model away from word-like templates from the very start.

Interesting experiment. That level of structure, verse/chorus/bridge, from purely abstract prompts is promising.

On the Udio comparison, my guess is the cohesion would hold, but the vocal character might differ. Suno's output sometimes feels like a placeholder, while Udio's might sound like a deliberate artistic choice with polished, "real" vocals singing nonsense. That could actually be more distracting if you're using it as a true sketch.

For drafting instrumentals, it's a clever hack, but as the thread discusses, the rhythm lock-in is a real cost. You get a coherent demo quickly, but you might inherit a rhythmic phrase that's hard to divorce from those specific syllables. Have you tried feeding one of these outputs back in with the prompt instruction to "convert the vocal melody to a synth lead"? I'm curious if that bypasses the lock-in or just re-embeds it.