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- Adds a new script and API endpoints for doing this - Reworks autoregressive and diffusion models so that the conditioning is computed separately (which will actually provide a mild performance boost) - Updates README This is untested. Need to do the following manual tests (and someday write unit tests for this behemoth before it becomes a problem..) 1) Does get_conditioning_latents.py work? 2) Can I feed those latents back into the model by creating a new voice? 3) Can I still mix and match voices (both with conditioning latents and normal voices) with read.py? |
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| .. | ||
| __init__.py | ||
| audio.py | ||
| diffusion.py | ||
| samples_generator.py | ||
| stft.py | ||
| tokenizer.py | ||
| typical_sampling.py | ||