CheshireCC/faster-whisper-GUI

faster_whisper GUI with PySide6

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Summary Information

Updated 51 minutes ago
Type:ApplicationCategory(s):Voice & Speech AIAI Agents & LLM Apps
Added to GitGenius on September 23rd, 2026
Created on July 18th, 2023
Open Issues & Pull Requests: 125 (+0)
GitHub issues: Enabled
Number of forks: 172
Total Stargazers: 3,005 (+0)
Total Subscribers: 19 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 21.2 hours
Mean response time: 18.7 days
90th percentile: 41.4 days
Tracked items: 104

How this project is maintained

100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 74% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 67
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 547 days
Stale 30+ days: 66
Stale 90+ days: 63

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • advice (2)
  • bug (1)
  • whisperX (1)

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Detailed Description

faster-whisper-gui is a desktop application for transcribing audio and video files using faster-whisper and whisperX models.

The tool addresses the need for an accessible interface to speech-to-text transcription. It wraps the faster-whisper and whisperX libraries, which provide optimized implementations of OpenAI's Whisper model, and exposes their parameters through a graphical interface built with PySide6. Users can transcribe audio or video to multiple output formats including SRT, TXT, SMI, VTT, and LRC. The application includes voice activity detection through Silero VAD, support for the large-v3 Whisper model, and audio enhancement via Demucs for vocal isolation before transcription. It handles batch processing of multiple files and provides model management features including downloading and converting models.

The tool suits developers and end users who need speech transcription without command-line interaction and want fine-grained control over VAD and Whisper model parameters. It is particularly useful for those working with video files or needing output in subtitle formats. The application supports multiple UI languages and themes. Anyone adopting it should be aware that model files must be obtained separately from Hugging Face or other sources, though the software includes utilities to download and convert models.

The project shows active development with implementation of multiple transcription backends and audio processing features. The codebase demonstrates ongoing refinement of the user interface and file handling systems. The tool integrates established external libraries rather than reimplementing core functionality, relying on faster-whisper for the transcription engine and Demucs for audio separation.