This repository documents my approach to producing high-quality AI-generated voice and audio outputs, with a focus on input preparation, iteration strategy, and quality evaluation.
While this work is non-engineering, it sits at the intersection of audio engineering, product thinking, and system optimization. The goal is to consistently move outputs from “acceptable” to “production-ready” through disciplined inputs and structured iteration.
The workflows documented here are platform-agnostic, but are particularly relevant to modern AI voice systems such as those used by companies like ElevenLabs.
This repository emphasizes:
- Audio and script preparation
- Iterative generation strategies
- Quality control and evaluation
- Practical heuristics developed through hands-on experimentation
High-quality outputs begin with high-quality inputs. Before generating audio, I focus on:
- Cleaning and editing raw voice recordings (timing, tuning, sibilance, breath control)
- Removing unintended emphasis, noise, or pacing inconsistencies
- Structuring scripts into natural, speakable phrases rather than long blocks of text
- Aligning phrasing with realistic breath and cadence patterns
- Removing or adding silence to make monologues and conversations flow naturally
- Final listenthrough to ensure vocal quality remains natural and avoids feeling over-edited
This preparation significantly reduces artifacts and improves realism in downstream generations.
Rather than treating scripts as static text, I treat them as performance instructions.
Best practices include:
- Breaking scripts into short, natural phrases
- Using punctuation and line breaks to guide pacing
- Avoiding forced emphasis and over-styled delivery
- Writing for how something is spoken, not how it is read
Small structural changes often outperform large parameter adjustments.
My generation approach prioritizes controlled iteration over single “perfect” renders.
Key principles:
- Generate in short passes rather than long, all-at-once outputs
- Make micro-adjustments to speed, stability, and style
- Adjust pacing before adjusting emphasis
- Treat generation as a feedback loop, not a one-shot process
In practice, one-percentage-point changes often separate “good” from “excellent.”
I evaluate outputs using both technical and perceptual criteria, including:
- Naturalness of cadence and breathing
- Consistency of tone across phrases
- Absence of artifacts or synthetic emphasis
- Clarity, intelligibility, and listener comfort
I rely heavily on A/B comparisons and incremental refinement rather than subjective gut checks alone.
/example-scripts
- before.md # unstructured or raw script
- after.md # optimized, speakable version with annotations
/generation-notes
- iteration-notes.md # documented changes and observed effects
These examples illustrate how small, intentional changes in inputs produce measurable improvements in outputs.
The core belief behind this work is simple:
Output quality is constrained less by the model itself and more by the discipline of the person using it.
This repository reflects a practical, systems-oriented approach to AI audio generation grounded in real production experience.