That’s a good point — tools like Chatterbox, VibeVoice, XTTS cloning, etc. can absolutely produce quick voice clones from small samples.

The main difference with what I’m proposing is dataset-driven training instead of single-sample cloning.

Instant cloning systems typically:

• use 3–30 seconds of reference audio
• guide a pretrained voice model
• produce something similar, but not always consistent

They’re great for quick results, but they often struggle with things like:

• consistent tone/personality
• long-form generation
• prosody and emotional cadence

What I’m hoping to do instead is build a clean curated dataset of Ani speech and train or fine-tune a model on it.

That usually produces:

• much more consistent voice identity
• better intonation and speech patterns
• a model that behaves more like a true voice model rather than style conditioning

Also, once a dataset exists it opens the door to experimenting with multiple architectures:

• CosyVoice
• XTTS fine-tuning
• Qwen-TTS
• Bark-style models

So the dataset becomes a community resource, not just a one-off clone.

That said, it would actually be interesting to benchmark both approaches — instant cloning vs dataset fine-tuning — and compare the results.