whisper-auto-transcribe VS video-splitter

Compare whisper-auto-transcribe vs video-splitter and see what are their differences.

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whisper-auto-transcribe video-splitter
8 2
195 440
- -
6.1 0.0
about 1 year ago 6 months ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

whisper-auto-transcribe

Posts with mentions or reviews of whisper-auto-transcribe. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-04.

video-splitter

Posts with mentions or reviews of video-splitter. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-16.

What are some alternatives?

When comparing whisper-auto-transcribe and video-splitter you can also consider the following projects:

subgen - Autogenerate subtitles using OpenAI Whisper Model via Jellyfin, Plex, Emby, Tautulli, or Bazarr

silero-vad - Silero VAD: pre-trained enterprise-grade Voice Activity Detector

Transcriptify - One script that uses OpenAI to transcribe audio into text.

subsync - Subtitle Speech Synchronizer

FunASR - A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.

whisper - Robust Speech Recognition via Large-Scale Weak Supervision

subsai - 🎞️ Subtitles generation tool (Web-UI + CLI + Python package) powered by OpenAI's Whisper and its variants 🎞️

whisper-timestamped - Multilingual Automatic Speech Recognition with word-level timestamps and confidence