AI-assisted removal of filler words from video recordings

• filler-word-removal

  1 3 5.2 Python
  

In this post, I’ll explore one use case and implementation for AI-assisted post-processing that can make video presenters’ lives a little easier. We’ll go through a small demo which lets you remove disfluencies, also known as filler words, from any MP4 file. These can include words like “um”, “uh”, and similar. I will cover:

• whisper-timestamped

  2 1,564 8.1 Python

  Multilingual Automatic Speech Recognition with word-level timestamps and confidence

  

whisper-timestamped, which is a layer on top of the Whisper set of models enabling us to get accurate word timestamps and include filler words in transcription output. This transcriber downloads the selected Whisper model to the machine running the demo and no third-party API keys are required.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• CPython

  1,320 59,954 10.0 Python

  The Python programming language

  

To run the demo locally, be sure to have Python 3.11 and FFmpeg installed.

• FFmpeg

  486 42,788 10.0 C

  Mirror of https://git.ffmpeg.org/ffmpeg.git

  

To run the demo locally, be sure to have Python 3.11 and FFmpeg installed.

Suggest a related project