Speech Analytics, Sound Analytics in TorchAudio

Speech Analytics, Sound Analytics in TorchAudio

Learn how to do sound pre-processing in TorchAudio. The landscape of DataScience is changing everyday. In the last few years we have seen numerous number of research and advancement in NLP and Computer Vision field.

The landscape of DataScience is changing everyday. In the last few years we have seen numerous number of research and advancement in NLP and Computer Vision field. But there is a field which is still unexplored and has a lot of potential , the field is — SPEECH.

In the last tutorial we have learned about:

  1. What is a Sound wave?

2. The basic properties of sound wave

3. Feature Extraction from sound wave

4. Pre-Processing a sound wave

In this tutorial we would be looking into the practical application of it in Python. The two most popular libraries to help you in your journey are:

  1. Librosa
  2. TorchAudio

TorchAudio — It is a PyTorch domain library consisting of I/O, popular datasets and common audio transformations that can bring new speed and efficiency to your PyTorch projects. It is one of the powerful speech modulation software created by Facebook.

speech-analytics speech-recognition machine-learning-ai pytorch speech ai

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Speech Analytics Part -1, Basics of Speech Analytics

Learn about the basics properties of sound wave, how to extract features and pre-process them in this part. Speech signals are sound signals, defined as pressure variations travelling through the air. These variations in pressure can be described as waves and correspondingly they are often called sound waves. Sound wave can be described by five characteristics: Wavelength, Amplitude, Time-Period, Frequency and Velocity or Speed

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