voice-gender VS spaCy

Hey y'all. I discovered the aforementioned app (What is Your Voice Gender? ) a while back and thought it was super cool. Recently I've been ramping up my voice work and went to use this app again, but any file I try to input results in an error (see a photo here) that makes it unusable. Recalling this was a personal project for the creator, I went searching for the project code in Github and found it here. I realized that despite the web app showing an error, it was kinda easy to go into RStudio myself and get the predictions without an error. I saw another user in the comments of a recent post experiencing this same issue, so I figured I'd just write out the steps to my DIY fix that worked, in case anybody else is having issues and would like to try this fix:

Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.

spaCy https://spacy.io/

In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.

Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.

The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?

[0] https://spacy.io/

[1] https://prodi.gy/

spaCy

I chose spacy. Although it's not state of the art, it's very well established and stable.