walk_in_the_park VS ddsp-singing-vocoders

Compare walk_in_the_park vs ddsp-singing-vocoders and see what are their differences.

walk_in_the_park

By ikostrikov
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ddsp-singing-vocoders

Official implementation of SawSing (ISMIR'22) (by YatingMusic)
ismir singing-synthesis singing-voice vocoders
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walk_in_the_park ddsp-singing-vocoders
2 2
226 240
- 0.0%
0.0 10.0
over 1 year ago over 1 year ago
Python Python
MIT License GNU Affero General Public License v3.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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walk_in_the_park

Posts with mentions or reviews of walk_in_the_park. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-03.

ddsp-singing-vocoders

Posts with mentions or reviews of ddsp-singing-vocoders. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-03.

What are some alternatives?

When comparing walk_in_the_park and ddsp-singing-vocoders you can also consider the following projects:

textual_inversion

bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.

Cold-Diffusion-Models - Official implementation of Cold-Diffusion for different transformations in pytorch.

Awesome-Dataset-Distillation - Awesome Dataset Distillation Papers

PeRFception - [NeurIPS2022] Official implementation of PeRFception: Perception using Radiance Fields.

MinVIS

walk_in_the_park vs textual_inversion ddsp-singing-vocoders vs bitsandbytes walk_in_the_park vs Cold-Diffusion-Models ddsp-singing-vocoders vs Cold-Diffusion-Models walk_in_the_park vs Awesome-Dataset-Distillation ddsp-singing-vocoders vs textual_inversion walk_in_the_park vs bitsandbytes ddsp-singing-vocoders vs PeRFception walk_in_the_park vs MinVIS ddsp-singing-vocoders vs MinVIS walk_in_the_park vs PeRFception ddsp-singing-vocoders vs Awesome-Dataset-Distillation