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

Question

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.

Answer 1

Using a smaller model of Whisper (for real-time performance) and feeding the speech-to-text output through a sentiment analysis pipeline with HuggingFace like so:

import whisper
from transformers import pipeline

model = whisper.load_model("small")
stt_result = model.transcribe("audio.mp3")

sentiment_pipeline = pipeline("sentiment-analysis")
data = [stt_result]
sentiment_pipeline(data)

would achieve your desired results as such:

[{'label': 'POSITIVE', 'score': 0.995}]

Altough you should keep in mind that in this example, Whisper doesn't accept a stream of data but a file. You should orchestrate the stream like this:

1. Save incoming audio data.
2. Transcribe last 5 seconds (the more the better) of your saved audio file.
3. If there's an overlap, select most recent segment's transcription.
4. Repeat steps 1-3.

As a last note; this GitHub project that transcribes (and deals with the orchestration) an audio stream. Adding the mentioned sentiment analysis pipeline would be much easier in your case.