Nextcloud-Archive/text2speech_kokoro
1
0
Fork 0
mirror of https://github.com/nextcloud/text2speech_kokoro.git synced 2025-10-26 15:07:30 +01:00
Code Issues Projects Releases Packages Wiki Activity
No description
37 commits 27 branches 3 tags 180 KiB
Find a file
Exact
Lukas Schaefer 90b52bd231
 Merge pull request #32 from nextcloud/1.2.0 
Prepare 1.2.0
2025-10-20 11:42:49 -04:00
.github Add codeowners 2025-07-23 16:09:30 -04:00
.run Update dependencies and add nextcloud 33 support 2025-10-17 11:33:02 -04:00
appinfo Prepare 1.2.0 2025-10-20 11:34:52 -04:00
ex_app/lib Update dependencies and add nextcloud 33 support 2025-10-17 11:33:02 -04:00
.gitattributes Initial commit 2025-04-04 15:24:01 -04:00
.gitignore Initial commit 2025-04-04 15:24:01 -04:00
.nextcloudignore Initial commit 2025-04-04 15:24:01 -04:00
.pre-commit-config.yaml Initial commit 2025-04-04 15:24:01 -04:00
CHANGELOG.md Prepare 1.2.0 2025-10-20 11:34:52 -04:00
Dockerfile First commit 2025-04-05 17:20:54 -04:00
healthcheck.sh Initial commit 2025-04-04 15:24:01 -04:00
krankerl.toml Initial commit 2025-04-04 15:24:01 -04:00
LICENSE Update license to agpl 2025-05-07 15:51:58 -04:00
Makefile Prepare 1.2.0 2025-10-20 11:34:52 -04:00
pyproject.toml Update dependencies and add nextcloud 33 support 2025-10-17 11:33:02 -04:00
README.md fix: typo in readme 2025-06-20 08:23:11 -04:00
requirements.txt Update dependencies and add nextcloud 33 support 2025-10-17 11:33:02 -04:00
start.sh First commit 2025-04-05 17:20:54 -04:00

README.md

Nextcloud Kokoro Text2Speech App

An exapp that converts text to speech using Kokoro. This is a very lightweight model that supports languages such as English, Japanese, Mandarin, Spanish, Hindi, Italian, and Brazilian Portuguese.

Kokoro Text2Speech is a Nextcloud app that provides an AI capable of converting text to speech using various voices and languages.

This app is built on top of the Kokoro AI model.

Ethical AI Rating

Rating: 🟡

Positive:

  • The software for training and inferencing of this model is open source
  • The trained model is freely available, and thus can be ran on-premises

Negative:

  • The training data isn't freely available, making it not possible to check or correct for bias or optimise the performance and CO2 usage.

Running outside of Docker

Make sure to use python 3.12.