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Transition Optimization

Suno AI users often encounter challenges in achieving smooth transitions within and at the end of their generated songs. These issues can manifest as abrupt changes in musical flow, unexpected spoken words, or premature endings. Several techniques and strategies have been developed to optimize these transitions.

Using Meta Tags for Smoother Transitions​

One approach to improve song flow involves using meta tags within the prompt. By creating a structured template, users can guide the AI in generating more coherent musical sections.

Template Creation: A template can be created to outline the desired song structure. For example:

[Bridge with Ostinato]
[Transition Section]
[Heavy Female Screaming Section]
[Bridge Section]
[Chorus with Drop]

These templates, up to 3000 characters in custom mode, allow for diverse generations. Uploading an instrumental based on this template can seed a new song with enhanced output quality.

Tagging Techniques: To ensure specific elements are included, concise, lowercase tags are recommended. For example, instead of complex phrases, use:

[sax]
[saxophone]
[solo]

or

[chorus]
[drop]

Using ChatGPT can help refine tags for better effectiveness.

Addressing Weird Song Endings​

Suno-generated songs sometimes end with random spoken words or strange sounds. These issues are likely due to the AI's training data and can be exacerbated by ambiguous prompts.

Prompt Specificity: Being specific and clear in prompts can minimize unexpected outputs. For example, instead of:

hyperpop, glitchy, clear female soprano, smooth vocals, British

use:

hyperpop, glitchy, clear British female soprano with smooth vocals

Ending Tags: Specific tags can control the song ending. Examples include:

[End]
[FINISH]
[Song Ends]
[End of Song]

Manual Editing: If strange endings persist, download the song and use a Digital Audio Workstation (DAW) to edit out unwanted parts. Trimming and fading out the end can often resolve the problem.

Achieving Natural Song Endings​

To ensure a natural ending, annotations in the lyrics box can be used. These annotations signal to the AI to conclude the song smoothly.

Ending Annotations:

  • [end] directs the AI to stop the music clearly.
  • [fade out] is intended to gradually lower the volume until the song fades out.
  • [outro] [Instrumental Fade out] [End] can create a more elaborate ending sequence.

Experimenting with these annotations is key, as AI interpretations can vary. If necessary, manual editing with audio software can add a fade-out effect.

Troubleshooting Song Extension Issues​

Extending songs in Suno can sometimes lead to premature cut-offs. Several strategies can be used to address these issues.

Adjusting Extend Points: Changing the extend point by a second or two can prevent the song from being cut off. If this doesn't work, try extending by slightly different intervals.

Prompt Modification: Modifying prompts can help align generations better. If an update has occurred, changing or removing specific words in the prompt might be necessary.

Model Versioning: Switching between different models, such as using model 3.0 instead of 3.5, can sometimes resolve extension issues. Switching back and forth between versions can also be helpful.

Transition Prompts: Adding transition words or phrases, such as [Transition], can help the AI continue the song naturally.

Prompt Simplification: Complex prompts can sometimes confuse the AI. Simplifying prompts or avoiding overly specific criteria can lead to better results and fewer cut-off issues.