The 10 preset voices live on a hypersphere of radius ≈ 7.1 in the
12 800-D style-token space (verified empirically: pairwise cosines
0.86-0.97, SVD shows 7 axes cover 99 % of variance). Linear or
spherical interpolation between presets stays in the trained
distribution and produces new intelligible voices.
API:
voice = pipe.create_voice({'F2': 0.7, 'M1': 0.3}) # slerp by default
voice = pipe.create_voice({'F2': 0.5, 'M1': 0.5}, interp='lerp')
wav = pipe.generate('Bonjour', voice=voice, lang='fr')
The voice argument of pipe.generate() now accepts either a preset
name (str) or a custom voice descriptor (dict from create_voice).
Whisper validation on 6 custom blends (FR test phrase):
F2 70 / M1 30 → 100 % (lightly androgyne F voice)
F2 50 / M1 50 → 91 % (true androgyne)
avg of 5 F voices → 100 % (mean feminine timbre)
avg of 5 M voices → 91 % (mean masculine timbre)
warm fem (F4+F5) → 91 %
bright masc (M1+M5) → 100 %
All blends remain intelligible — the trained voice manifold is convex
enough that interpolations don't fall out of the model's distribution.
Example script in examples/custom_voice_demo.py.
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