Purpose
The aim was to create a simple transcription setup that could be used without depending on a paid online platform.
It keeps the whole process local, makes file handling easier, and gives more control over privacy, storage, and delivery.
SYSTEM AUTOMATION
Video Transcription Workflow
I built a local Python transcription workflow for times when uploading client recordings to a third-party website is not ideal.
- Processes recordings locally on the computer
- Names transcripts automatically and saves them to the chosen folder
- Uses open-source tools with no subscription fees, upload limits, or paid credits
What was built
- Video and audio file input
- Local Faster-Whisper transcription
- Timestamped transcript output
- Automatic file naming
- Chosen output folder
- Organised input and output folders
- README and setup files
- Sample transcript output
Workflow
The user runs the Python script and selects a video or audio file.
The recording is processed locally with Faster-Whisper.
The finished transcript is automatically named and saved in the chosen output folder, ready for review, editing, or delivery.
Tools
Python, Faster-Whisper, command line, local file handling.
Outcome
A free, local transcription workflow that avoids subscriptions, upload limits, and paid credits.
It gives the user more control over private recordings, file storage, naming, and where each transcript is saved.
Walkthrough
01
Choose the file
Select the video or audio recording to be transcribed.
02
Start the script
Run the Python command and enter the file path.
03
Process the recording
Faster-Whisper handles the transcription locally.
04
Create the transcript
The workflow produces timestamped text from the recording.
05
Save automatically
The transcript is named and saved in the chosen output folder.
06
Review the result
Open the transcript, clean the text if needed, and prepare it for delivery.
