forked from mrq/tortoise-tts
01b783fc02
- 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? |
||
---|---|---|
.. | ||
__init__.py | ||
audio.py | ||
diffusion.py | ||
stft.py | ||
tokenizer.py | ||
typical_sampling.py |