In this chapter, we will embark on this transformative journey through the realms of real-time audio capture, cutting-edge transcription with the Whisper model, and audio classification using a convolutional neural network (CNN), with a focus on spectrograms. Additionally, we’ll explore innovative audio augmentation techniques. This chapter not only equips you with the tools and techniques essential for comprehensive audio data labeling but also unveils the boundless possibilities that lie at the intersection of AI and audio processing, redefining the landscape of audio data labeling.
Welcome to a journey through the intricate world of audio data labeling! In this chapter, we embark on an exploration of cutting-edge techniques and technologies that empower us to unravel the richness of audio content. Our adventure unfolds through a diverse set of topics, each designed to enhance your understanding of audio processing and labeling.
Our journey begins with the dynamic realm of real-time audio capture using microphones. We delve into the art of voice classification, using the random forest classifier to discern and categorize distinct voices in the captured audio.
Then, we introduce the groundbreaking Whisper model, a powerful tool for transcribing uploaded audio data. Witness the seamless integration of the Whisper model with OpenAI for accurate transcriptions, followed by a meticulous labeling process. As we unfold the capabilities of the Whisper model, we draw insightful comparisons with other open source models dedicated to audio data analysis.
Our journey takes a visual turn as we explore the creation of spectrograms, visually capturing the intricate details of sound. The transformative CNNs come into play, elevating audio classification through visual representations. Learn the art of labeling spectrograms, unraveling a new dimension in audio processing.
Prepare to expand your horizons as we venture into the realm of augmented data for audio labeling. Discover the transformative impact of noise augmentation, time-stretching, and pitch-shifting on audio data. Uncover the techniques to enhance the robustness of your labeled audio datasets.
Our exploration culminates in the innovative domain of Azure Cognitive Services. Immerse yourself in the capabilities of Azure to transform speech to text and achieve speech translation. Witness the seamless integration of Azure Cognitive Services, revolutionizing the landscape of audio processing.
We are going to cover the following topics:
- Capturing real-time voice using a microphone and classifying voices using the random forest classifier
- Uploading audio data and transcribing an audio file using OpenAI’s Whisper model and then labeling the transcription.
- A comparison of the Whisper model with other open source models for audio data analysis
- Creating a spectrogram for audio data and then labeling the spectrogram, using CNN for audio classification
- Augmenting audio data such as noise augmentation, time-stretching, and pitch-shifting
- Azure Cognitive Services for speech-to-text and speech translation
Technical requirements
We are going to install the following Python libraries.
openai-whisper is the Python library provided by OpenAI, offering access to the powerful Whisper Automatic Speech Recognition (ASR) model. It allows you to transcribe audio data with state-of-the-art accuracy:
%pip install openai-whisper
librosa is a Python package for music and audio analysis. It provides tools for various tasks, such as loading audio files, extracting features, and performing transformations, making it a valuable library for audio data processing:
%pip install librosa
pytube is a lightweight, dependency-free Python library for downloading YouTube videos. It simplifies the process of fetching video content from YouTube, making it suitable for various applications involving YouTube data:
%pip install pytube
transformers is a popular Python library developed by Hugging Face. It provides pre-trained models and various utilities for natural language processing (NLP) tasks. This includes transformer-based models such as BERT and GPT:
%pip install transformers
joblib is a Python library for lightweight pipelining in Python. It is particularly useful for parallelizing and caching computations, making it efficient for tasks involving parallel processing and job scheduling:
%pip install joblib