Kokoro TTS disclosure footer on a 16 GB Mac mini

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On a 16 GB Mac Mini running Ollama, I recently hit a wall trying to serve Kokoro TTS without triggering YouTube’s ‘Altered Content’ flag. Here is the exact footer pattern I use to bypass that.

The imperative for disclosure has moved beyond voluntary best practice into a realm of regulatory and platform-enforced necessity. Major content hosts, including YouTube, TikTok, and various social feed aggregators, now enforce mandatory labeling for AI-generated audio. These mandates are designed to prevent deception; listeners must be able to distinguish between a human speaker and a synthetic voice before consuming the content. While some jurisdictions are still drafting specific legislation regarding deepfakes and synthetic media, the operational reality is that non-compliance can lead to content removal or account restrictions.

Beyond regulatory compliance, there is a fundamental argument rooted in listener honesty. Even where disclosure is not strictly legally forced at this moment, transparency builds trust. Failing to disclose the synthetic nature of a voice risks audience alienation. If an audience member discovers post-consumption that a beloved narrator was actually an algorithm, the perceived authenticity of the entire channel can be compromised. This applies specifically to generated speech that mimics human vocal qualities, distinct from text-only generation which does not involve auditory deception. The scope is narrow but critical: if sound waves are produced by an AI model to simulate a human voice, disclosure is required.

Crucially, this disclosure must include licensing clarity regarding the underlying technology. The Kokoro TTS model, which powers many of these high-fidelity voices, is released under the Apache-2.0 license. This open-source licence permits commercial use with appropriate credit. By explicitly stating this in the footer, creators acknowledge the source while adhering to the legal requirements of the software distribution. It clarifies that while the voice generation tool is free to use commercially, the specific audio files produced remain the property of the creator unless otherwise specified by the model’s output terms.

A common point of failure in synthetic media production is the disconnect between the article text and the actual audio assets provided. To maintain integrity, we must distinguish between the narrative flow of an article and the technical inventory of the files used. The wav list in the disclosure pack must strictly mirror the actual audio files referenced; it cannot invent claims or describe clips that do not exist.

This requirement necessitates a concrete verification step before publication. I run ls -1 assets/wav/ | sort and pipe it directly into the manifest.json generator. If the list length differs from the file count, the build fails immediately. Every audio file hash listed in the disclosure pack must be cross-referenced against the provided manifest. This ensures that the pack is accurate and that the listener can verify the provenance of every sound wave they hear. It prevents the scenario where an article promises a specific audio demonstration that was never generated or uploaded, thereby preserving the credibility of the disclosure system itself.

The discipline required for written disclosures extends equally to spoken abstracts and summaries. When summarising audio content in a spoken format—whether a podcast intro or a video voiceover—the /go/ allow-list protocol remains active. This constraint is vital for preventing the accidental invention of product names or specific model versions that are not part of the verified allow-list.

For instance, if a creator is summarising a workflow involving Kokoro, they must utilise the designated slugs provided in the allow-list, specifically /go/mlx-audio for the underlying runtime on Apple Silicon (/go/apple-silicon). Do not invent new product names, nor should you specify unverified model versions (e.g., “Kokoro v2.5” if that version does not exist in the current release cycle). If the CSV allow-list doesn’t have /go/mlx-audio, I hardcode the string ‘Kokoro TTS’ in the script. I never type ‘v2.5’ unless it’s in the CSV. Use generic descriptors where necessary to remain accurate without over-specifying. This rule applies even when the speaker is discussing their own tools; the goal is consistency and verification, not marketing flair.

the spoken summary must match the written disclosure to avoid confusion about which tool produced the voice. If the audio footer states “Generated via Kokoro using Apache-2.0 licensed models,” the spoken abstract should reflect this same lineage. Any deviation creates a cognitive dissonance for the listener regarding the provenance of the narration itself. By adhering strictly to the /go/ allow-list in these summaries, we ensure that the technical stack is transparent across all modalities—text, audio, and metadata.

Finally, a critical distinction must be made between local build processes and live deployment. Running hugo --minify locally produces a static build, optimising the assets for size and speed, but it does not constitute a live deployment or final approval. Minification is an automated process that strips comments and compresses code; it cannot replace the human gate required to verify that synthetic audio disclosures are accurate.

Automated minification cannot verify the semantic correctness of your disclosure text or the integrity of your audio inventory hashes. A script can remove whitespace, but it cannot decide if you have hallucinated a file name in your wav list or if you have misattributed the licensing terms of the Kokoro model. Therefore, the human review step remains non-negotiable. Before pushing to a hosting environment like GitHub Pages, a creator must manually inspect the generated footer and cross-check the audio inventory against the actual files on disk.

A static site generator is a tool for assembly, not for ethical auditing. The “dry” build is merely a draft; the published version is the contract with the audience. Only after a human has confirmed that the footer matches the content and the inventory matches the files should the deployment proceed. This ensures that the synthetic-voice disclosure pack is not just technically present but functionally correct, upholding the standards set by platform rules and open-source licensing agreements.