SSMLParser VS NumPy

I even wrote an SSML parser in JavaScript https://github.com/guest271314/SSMLParser to prove the lack of SSML support in Web Speech API is not a technical difficult, rather a failure in the specification and a lack of will to implement by browser vendors How is a complete SSML document expected to be parsed when set once at .text property of SpeechSynthesisUtterance instance?.

Navigate to https://guest271314.github.io/SSMLParser/, click "Start speech synthesis". What happens?

I implemented SSML parsing using JavaScript https://github.com/guest271314/SSMLParser just to demonstrate the requirement is possible.

I can run this page https://guest271314.github.io/SSMLParser/ without an issue.

I implemented SSML parsing in JavaScript by hand for Web Speech API per SSML specification https://github.com/guest271314/SSMLParser, where the Web Speech API nor Firefox nor Chrome or Chromium browsers (Google does implement SSML parsing as a service https://github.com/guest271314/GoogleNetworkSpeechSynthesis) have implemented SSML parsing.

I did using JavaScript https://github.com/guest271314/SSMLParser.

For parsing SSML https://github.com/guest271314/SSMLParser there is the specification, which I implemented, to demonstrate the requirement is possible; there was and still is in that matter, simply a lack of will to implement in the browser. Google would rather try to get you to sign up for their cloud products.

Thus, I wrote https://github.com/guest271314/SSMLParser and https://github.com/guest271314/native-messaging-espeak-ng.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]