| ory/hydra |
14,797 |
|
0 |
9 |
over 2 years ago |
1 |
May 08, 2019 |
98 |
apache-2.0 |
Go |
| OpenID Certified™ OpenID Connect and OAuth Provider written in Go - cloud native, security-first, open source API security for your infrastructure. SDKs for any language. Works with Hardware Security Modules. Compatible with MITREid. |
| vanhauser-thc/thc-hydra |
8,480 |
|
0 |
0 |
over 2 years ago |
0 |
|
38 |
agpl-3.0 |
C |
| hydra |
| api-platform/api-platform |
8,190 |
|
0 |
0 |
over 2 years ago |
37 |
February 12, 2018 |
606 |
mit |
TypeScript |
| Create REST and GraphQL APIs, scaffold Jamstack webapps, stream changes in real-time. |
| ory/keto |
4,428 |
|
0 |
5 |
over 2 years ago |
51 |
March 09, 2023 |
59 |
apache-2.0 |
Go |
| Open Source (Go) implementation of "Zanzibar: Google's Consistent, Global Authorization System". Ships gRPC, REST APIs, newSQL, and an easy and granular permission language. Supports ACL, RBAC, and other access models. |
| ashleve/lightning-hydra-template |
3,322 |
|
0 |
0 |
over 2 years ago |
0 |
|
59 |
|
Python |
| PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡ |
| ory/oathkeeper |
3,082 |
|
0 |
2 |
over 2 years ago |
181 |
July 18, 2023 |
63 |
apache-2.0 |
Go |
| A cloud native Identity & Access Proxy / API (IAP) and Access Control Decision API that authenticates, authorizes, and mutates incoming HTTP(s) requests. Inspired by the BeyondCorp / Zero Trust white paper. Written in Go. |
| ory/fosite |
2,200 |
|
50 |
102 |
over 2 years ago |
280 |
December 07, 2022 |
44 |
apache-2.0 |
Go |
| Extensible security first OAuth 2.0 and OpenID Connect SDK for Go. |
| abo-abo/hydra |
1,627 |
|
0 |
0 |
almost 4 years ago |
0 |
|
81 |
|
Emacs Lisp |
| make Emacs bindings that stick around |
| jacktasia/dumb-jump |
1,503 |
|
0 |
0 |
over 2 years ago |
14 |
October 18, 2021 |
82 |
gpl-3.0 |
Emacs Lisp |
| an Emacs "jump to definition" package for 50+ languages |
| facebookresearch/svoice |
1,078 |
|
0 |
0 |
over 2 years ago |
0 |
|
27 |
other |
Python |
| We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers. |
