Podcasts are a great example of how this technology can be useful. Podcasts have gained growing popularity, which has led to an increasing demand for tools capable of automatically transcribing and segmenting podcast episodes, thus saving a significant amount of time on manual work.
Before diving into the Jupyter notebook, let me briefly introduce three libraries that form the backbone of this pipeline.
The notebook has a Setup section that installs packages and defines helper functions. We will go through all sections and look at each cell step by step.
To begin, it's important to install dependencies, and it must be done in a specific order due to conflicts between Pyannote and PyTorch Lightning.
!pip install pyannote.audio==2.1.1 denoiser==0.1.5
!pip install omegaconf==2.3.0 pytorch-lightning==1.8.4If you want to try out this demo on your own computer, you will need to install ffmpeg package since we will process video and audio files.
This section contains the most important part of the demo. Let's examine the code more closely. We'll start by importing the required libraries and loading the pretrained models.
To sum up, this tutorial provides a comprehensive guide to building a speech recognition and speaker diarization pipeline utilizing three different models. With the recent release of the Whisper API, developers can now easily integrate the model into their apps.
Learn how to transcribe any video in one of 99 languages, identify speakers, and translate text into any of these languages.
Audio Processing
Learn how to transcribe any video in one of 99 languages, identify speakers, and translate text.
$ cat speech-recognition-guide.md