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Source channel @githubtrending · Post #15495 · Feb 15

#c_lang Moonshine Voice is an open-source toolkit for fast, private on-device speech-to-text that beats Whisper's accuracy and speed (up to 5x faster, 6.65% vs. 7.44% WER) with tiny 26MB-245M models for live apps on phones, Raspberry Pi, and more. It streams results as you speak, supports English/Spanish/Mandarin/etc., and handles transcription/commands easily via simple APIs on Python/iOS/Android. You benefit by building responsive voice apps offline without accounts, keys, or cloud costs—perfect for real-time tools like translators or assistants. https://github.com/moonshine-ai/moonshine

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djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,