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+# Transcription Service App
+
+[Transcription Service App](https://pvm002.virtuos.uni-osnabrueck.de/) is a web app for universities to make simple transcriptions from video or audio files in multiple languages, currently tailored towards Open AI's Whisper models.
+
+![screenshot.png](docs/assets/screenshot.png)
+
+Some of its features are:
+- Supports transcriptions with or without simultaneous translations to multiple languages.
+- Simple interface.
+- Access to two of Open AI's Whisper models (base and large-v3).
+- Supports upload from videos and audio files (up to 1gb) as well as YouTube links.
+- Users can edit and download transcription results in 4 different formats (txt, vtt, srt and json).
+- Diarization support to detect multiple speakers (up to 20).
+- Srt, vtt and json formats provide timestamp and speaker information (when available).
+- Transcribed subtitles can be activated in uploaded videos.
+
+# Usage & Configuration
+
+You first need to set up a [whisperx API server](https://github.com/virtUOS/whisperx-api) to work with this app.
+
+Some environment variables should be set. Here is an example of a .env file:
+
+```yml
+# PATH to the ffmpeg library in your system
+FFMPEG_PATH=/usr/bin/ffmpeg
+# Path where temporal files will be generated 
+TEMP_PATH=transcription-whisper-temp
+# Uncomment this up if you're using an authentication process to allow users to log out
+#LOGOUT_URL=/oauth2/sign_out
+# Url and port to the API server
+API_URL=http://131.173.130.67:11300
+```
+
+## Development
+
+The app is developed in the [streamlit](https://streamlit.io/) framework.
+
+You can install the requirements needed to run and develop the app using `pip install -r requirements.txt`.
+Then simply run a development server like this:
+
+```bash
+streamlit run app.py
+```
+
+## Authors
+
+virtUOS