Labeling Audio Data

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...

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...

Real-time voice classification with Random Forest

In an era marked by the integration of advanced technologies into our daily lives, real-time voice classification systems have emerged as pivotal tools across various domains. The Python script in this section, showcasing the implementation of a real-time voice classification system using the Random Forest classifier from scikit-learn, is a testament to the versatility and significance of such applications.

The primary objective of this script is to harness the power of machine learning to differentiate between positive audio samples, indicative of human speech (voice), and negative samples, representing background noise or non-vocal elements. By employing the Random Forest classifier, a robust and widely used algorithm from the scikit-learn library, the script endeavors to create an efficient model capable of accurately classifying real-time audio input.

The real-world applications of this voice classification system are extensive...

Transcribing audio using the OpenAI Whisper model

In this section, we are going to see how to transcribe audio file to text using the OpenAI Whisper model and then label the audio transcription using the OpenAI large language model (LLM).

Whisper is an open source ASR model developed by OpenAI. It is trained on nearly 700,000 hours of multilingual speech data and is capable of transcribing audio to text in almost 100 different languages. According to OpenAI, Whisper “approaches human level robustness and accuracy on English speech recognition.”

In a recent benchmark study, Whisper was compared to other open source ASR models, such as wav2vec 2.0 and Kaldi. The study found that Whisper performed better than wav2vec 2.0 in terms of accuracy and speed across five different use cases, including conversational AI, phone calls, meetings, videos, and earnings calls.

Whisper is also known for its affordability, accuracy, and features. It is best suited for audio-to-text...

Hands-on – labeling audio data using a CNN

In this section, we will see how to train the CNN network on audio data and use it to label the audio data.

The following code demonstrates the process of labeling audio data using a CNN. The code outlines how to employ a CNN to label audio data, specifically training the model on a dataset of cat and dog audio samples. The goal is to classify new, unseen audio data as either a cat or a dog. Let’s take the cat and dog sample audio data and train the CNN model. Then, we will send new unseen data to the model to predict whether it is a cat or a dog:

1. Load and pre-process the data: The audio data for cats and dogs is loaded from the specified folder structure using the load_and_preprocess_data function. The load_and_preprocess_data function processes the audio data, converting it into mel spectrograms and resizing them for model compatibility.
2. Split data into training and testing sets: The loaded and pre-processed...

Exploring audio data augmentation

Let’s see how to manipulate audio data by adding noise, using NumPy.

Adding noise to audio data during training helps the model become more robust in real-world scenarios, where there might be background noise or interference. By exposing a model to a variety of noisy conditions, it learns to generalize better.

Augmenting audio data with noise prevents a model from memorizing specific patterns in the training data. This encourages the model to focus on more general features, which can lead to better generalization on unseen data:

import numpy as np
def add_noise(data, noise_factor):
    noise = np.random.randn(len(data))
    augmented_data = data + noise_factor * noise
    # Cast back to same data type
    augmented_data = augmented_data.astype(type(data[0]))
    return augmented_data

This code defines a function named add_noise that...

Introducing Azure Cognitive Services – the speech service

Azure Cognitive Services offers a comprehensive set of speech-related services that empower developers to integrate powerful speech capabilities into their applications. Some key speech services available in Azure AI include the following:

• Speech-to-text (speech recognition): This converts spoken language into written text, enabling applications to transcribe audio content such as voice commands, interviews, or conversations.
• Speech Translation: This translates spoken language into another language in real time, facilitating multilingual communication. This service is valuable for applications requiring language translation for global audiences.

These Azure Cognitive Services speech capabilities cater to a diverse range of applications, from accessibility features and voice-enabled applications to multilingual communication and personalized user experiences. Developers can leverage these services to...

Summary

In this chapter, we explored three key sections that delve into the comprehensive process of handling audio data. The journey began with the upload of audio data, leveraging the Whisper model for transcription, and subsequently labeling the transcriptions using OpenAI. Following this, we ventured into the creation of spectrograms and employed CNNs to label these visual representations, unraveling the intricate details of sound through advanced neural network architectures. The chapter then delved into audio labeling with augmented data, thereby enhancing the dataset for improved model training. Finally, we saw the Azure Speech service for speech to text and speech translation. This multifaceted approach equips you with a holistic understanding of audio data processing, from transcription to visual representation analysis and augmented labeling, fostering a comprehensive skill set in audio data labeling techniques.

In the next and final chapter, we will explore different...