How can I implement real-time sentiment analysis on live audio streams using Python?

I'm currently working on a project where I need to perform real-time sentiment analysis on live audio streams using Python. The goal is to analyze the sentiment expressed in the spoken words and provide insights in real-time. I've done some research and found resources on text-based sentiment analysis, but I'm unsure about how to adapt these techniques to audio streams.

Context and Efforts:

Research: I've researched various libraries and tools for sentiment analysis, such as Natural Language Processing (NLP) libraries like NLTK and spaCy. However, most resources I found focus on text data rather than audio.

Audio Processing: I'm familiar with libraries like pyaudio and soundfile in Python for audio recording and processing. I've successfully captured live audio streams using these libraries.

Text-to-Speech Conversion: I've experimented with converting the spoken words from the audio streams into text using libraries like SpeechRecognition to prepare the data for sentiment analysis.

Challenges:

Sentiment Analysis: My main challenge is adapting the sentiment analysis techniques to audio data. I'm not sure if traditional text-based sentiment analysis models can be directly applied to audio, or if there are specific approaches for this scenario.

Real-Time Processing: I'm also concerned about the real-time aspect of the analysis. How can I ensure that the sentiment analysis is performed quickly enough to provide insights in real-time without introducing significant delays?

Question:

I'm seeking guidance on the best approach to implement real-time sentiment analysis on live audio streams using Python. Are there any specialized libraries or techniques for audio-based sentiment analysis that I should be aware of? How can I effectively process the audio data and perform sentiment analysis in real-time? Any insights, code examples, or recommended resources would be greatly appreciated.

The solution I found here by following these steps:

• Adding middleware to make text from video
• Running the sentiment analysis for each complete sentence (Using Whisper as @doneforaiur suggested)

So, the latency here depends on the length of the words of the sentence. It's a trade-off for getting the analysis for the full context. Making chunks doesn't reflect the whole sentence thus the complete context.

There is a possible way to extend this by adding multiple sentence feeds to the analysis system to get deeper context. I'm thinking about it. Sometimes, we describe a scenario with multiple sentences.

Thanks, @doneforaiur for setting me in the right direction.