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Cool article, I do like the extensibility provided via the unix philosophy. Another thing you can do nowadays is use off-the-shelf deep neural networks to do tt

by pizza 5y ago

Cool article, I do like the extensibility provided via the unix philosophy. Another thing you can do nowadays is use off-the-shelf deep neural networks to do tts eg https://github.com/NVIDIA/tacotron2 https://github.com/NVIDIA/tacotron2 https://github.com/mozilla/TTS https://github.com/mozilla/TTS https://github.com/CorentinJ/Real-Time-Voice-Cloning https://github.com/CorentinJ/Real-Time-Voice-Cloning https://github.com/coqui-ai/TTS https://github.com/coqui-ai/TTS They're not all easy to setup however

ducktective 5y ago
Why so many ML-based projects don't release a binary or package?
- Blikkentrekker 5y ago
  Because they wouldn't know the specific a.b.i. of your system which on many system changes in a rolling way as well. They would have to release a great many different ones or alternatively bundle all libraries with it.
  - moron4hire 5y ago
    That's why all major software releases exclusive as source code /s
    - Blikkentrekker 5y ago
      They have the time and money to research this issue on every system, and they typically do bundle libraries. I once saw a comparison with LibreOffice that showed that the the package Debian itself provided was 20% of the size of the package LibreOffice provided targeting Debian, — which would not receive the same benefits of security bugfixes to libraries, but of course also not the same problems that often arise on Debian when they arrogantly patch libraries they barely understand and create their own unique security problems.
- echelon 5y ago
  An overwhelming number of reasons. Because you need data, trained models, etc. Because data scientists aren't typically product people or software engineers with UX in mind. Because ML packages are brittle and tied to specific hardware configurations. Because the ML world is evolving rapidly. It's quick, dirty, and messy. View these as stepping stones for research and product development. (I created https://vo.codes https://vo.codes using a lot of these, fwiw, in an attempt to make it easy.)

by pizza 5y ago

ducktective 5y ago

Why so many ML-based projects don't release a binary or package?

Blikkentrekker 5y ago
Because they wouldn't know the specific a.b.i. of your system which on many system changes in a rolling way as well. They would have to release a great many different ones or alternatively bundle all libraries with it.
- moron4hire 5y ago
  That's why all major software releases exclusive as source code /s
  - Blikkentrekker 5y ago
    They have the time and money to research this issue on every system, and they typically do bundle libraries. I once saw a comparison with LibreOffice that showed that the the package Debian itself provided was 20% of the size of the package LibreOffice provided targeting Debian, — which would not receive the same benefits of security bugfixes to libraries, but of course also not the same problems that often arise on Debian when they arrogantly patch libraries they barely understand and create their own unique security problems.
echelon 5y ago
An overwhelming number of reasons. Because you need data, trained models, etc. Because data scientists aren't typically product people or software engineers with UX in mind. Because ML packages are brittle and tied to specific hardware configurations. Because the ML world is evolving rapidly. It's quick, dirty, and messy. View these as stepping stones for research and product development. (I created https://vo.codes https://vo.codes using a lot of these, fwiw, in an attempt to make it easy.)