Skip to main content

Offline audio transcription and subtitle editor powered by Whisper

Project description

Local Whisper Studio

Offline speech-to-text with waveform editing and subtitle export—no API calls, no monthly bills.

What is this?

Local Whisper Studio is a desktop-first transcription and subtitle editor that brings OpenAI's Whisper directly to your machine. Drag in audio or video files, get instant transcripts with automatic speaker diarization, sync edits to the waveform in real-time, and export to SRT, VTT, or TXT formats. Everything runs offline—no API costs, no internet dependency, no vendor lock-in.

Features

  • Offline Transcription – Powered by OpenAI Whisper; runs entirely on your hardware
  • Speaker Diarization – Automatically identify and label different speakers
  • Waveform Editor – Sync transcript edits to audio timeline with visual feedback
  • Multi-format Export – SRT, VTT, and plain text subtitle formats
  • Video & Audio Support – Handle MP4, MOV, MP3, WAV, and more
  • Zero API Costs – No recurring bills or cloud dependencies
  • Web UI & CLI – Choose your workflow: browser interface or command-line scripting

Quick Start

Installation

Requires Python 3.9+

# Clone the repository
git clone <repo-url>
cd local-whisper-studio

# Install dependencies
pip install -e .

# Download Whisper model (first run only)
whisper-studio download-model base

Usage

Web Interface:

whisper-studio serve
# Opens http://localhost:5000 in your browser

CLI:

# Transcribe an audio file
whisper-studio transcribe audio.mp3 --output transcript.srt --format srt

# With diarization
whisper-studio transcribe video.mp4 --diarize --output subtitled.srt

Python API:

from whisper_studio import TranscriptionEngine

engine = TranscriptionEngine(model="base")
result = engine.transcribe("audio.mp3")

for segment in result.segments:
    print(f"{segment.speaker}: {segment.text}")

Tech Stack

  • Whisper – Speech recognition model (OpenAI)
  • Pyannote – Speaker diarization
  • FastAPI – Web server
  • Vanilla JS + Wavesurfer.js – Waveform editor UI
  • FFmpeg – Audio/video processing

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

local_whisper_studio-0.1.0.tar.gz (13.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

local_whisper_studio-0.1.0-py3-none-any.whl (14.9 kB view details)

Uploaded Python 3

File details

Details for the file local_whisper_studio-0.1.0.tar.gz.

File metadata

  • Download URL: local_whisper_studio-0.1.0.tar.gz
  • Upload date:
  • Size: 13.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for local_whisper_studio-0.1.0.tar.gz
Algorithm Hash digest
SHA256 f639e4866ac482a361813a7565e2b6b57b1555854c1456c2196302e026da50c9
MD5 c3b6370425f00148572109c1cdbfab8a
BLAKE2b-256 03426ab559ddd54cb6a06b06587ae5ce4efc7b64c7ff34dbbbb38b111d13e06f

See more details on using hashes here.

File details

Details for the file local_whisper_studio-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for local_whisper_studio-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 06bfee55043bc016072632eb72b8ec6f00a6479034dec289bd4fccfe23df9a5b
MD5 6e411e5c869c05e2e34fc0d4463935d2
BLAKE2b-256 0a2f71b5f67bd60e0cf3420ff9914c5e7394db28af31efb70fe5c71f0494c179

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page