When you want a Whisper-based local workflow
Voice2Sub brings Whisper-based recognition, review and export into one desktop workflow, so the result can become a transcript or subtitle file without a separate technical setup guide.
Download Voice2SubWhisper AI workflow
Whisper AI Transcription for Local Media
Use Whisper-based AI recognition for local video and audio files. Choose the spoken language, then create transcript or subtitle outputs.
Focused on the Whisper-based recognition layer inside Voice2Sub.
Whisper AI Transcription
Best for
- Users looking for Whisper AI
- Local audio and video files
- Subtitle workflows from Whisper text
- Transcript review before export
- Creators who do not want a technical setup
Why use it inside Voice2Sub
- Use Whisper AI in a file-based desktop workflow.
- Keep review and export close to the recognition step.
- Turn timestamped text into SRT, VTT, TXT, LRC or CSV.
- Review recognition output before publishing or handing it off.
- With a multilingual model, choose from up to 99 recognition languages before generation.
Whisper workflow
From media file to reviewed Whisper result
A practical route for users who want Whisper AI without building their own tooling.
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01
Open a supported file
Choose audio or video from your computer.
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02
Run recognition in the app
Voice2Sub uses its Whisper AI-based workflow to create timestamped text.
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03
Review the result
Review generated files before publishing.
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04
Save transcript or subtitles
Export TXT, SRT, VTT, LRC or CSV depending on the job.
Outputs
Whisper text can become transcripts or subtitles
The same recognized text can support a readable transcript, SRT/VTT subtitles, timed lyrics or a CSV review file.
Model-focused
For teams that prefer Whisper-based recognition
Use this workflow when you want Whisper-based recognition with a complete desktop app around review and export.
- Whisper AI
- Transcript files
- Export formats
Review matters
Whisper output is not the final proof
Treat generated text as a strong draft. Check it before subtitles, notes or client files leave your desk.
- Check names
- Generate timestamped subtitle files
- Export after review
Engine and models
A native whisper.cpp workflow with model choice
Voice2Sub packages a native whisper.cpp-based engine and offers Tiny through Large model families, including quantized and turbo variants. Model size, translation support and hardware requirements differ, so choose the model for the actual machine and output mode.
Use cases
Use Whisper recognition in everyday media work
Good when the model matters, but the final deliverable still needs editing and export.
- Transcribe interviews with Whisper
- Prepare SRT/VTT from recognized text
- Review course recordings
- Create text from podcasts
- Handle local media files in one app
Whisper AI transcription FAQ
Does Voice2Sub use Whisper AI?
Voice2Sub uses a Whisper AI-based recognition workflow inside the desktop app to create transcript and subtitle files from supported audio and video.
Is this a technical Whisper setup guide?
No. Voice2Sub packages Whisper-based recognition with model selection, review and export inside the desktop app, so normal use does not require a separate command-line setup.
Can Whisper output become SRT or VTT?
Yes. After review, you can export SRT or VTT, plus TXT, LRC and CSV.
Do I still need to review the text?
Yes. Names, accents, background noise and technical terms can still cause mistakes.
Use Whisper AI inside a practical desktop workflow
Download Voice2Sub to generate, review and export Whisper-based transcripts or subtitles from local files.