Transcription and captioning — faster, easier, and open source.

Powered by OpenAI Whisper (C++ implementation)

Announcment • Development • Lightning AI • Lightning Apps Gallery

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Lightning Echo

Use Echo to generate quick captions of video and audio content. Powered by OpenAI’s Whisper, Echo benefits from near-human speech recognition to transcribe spoken words into text.

Running Echo

Configuration

All configuration is done using environment variables, which are documented below with their default values.

Name	Type	Default Value	Description
ECHO_MODE	development/production	production	Toggles monitoring and other production-specific features.
ECHO_MODEL_SIZE	See Whisper Docs	base	The Whisper model to use.
ECHO_ENABLE_MULTI_TENANCY	boolean	false	If enabled, users will not be able to see Echoes or data created by other users. If disabled, the app treats everyone as the same user so everything is visible to everyone who uses it.
ECHO_RECOGNIZER_MIN_REPLICAS	integer	1	Minimum number of speech recognizer Works to keep running at all times, even if they are idle.
ECHO_RECOGNIZER_MAX_IDLE_SECONDS_PER_WORK	integer	120	Autoscaler will shut down any spare recognizer Works that haven't processed anything after this duration.
ECHO_RECOGNIZER_MAX_PENDING_CALLS_PER_WORK	integer	10	Autoscaler will create a new recognizer Work if any existing recognizer Work has this many pending items to process.
ECHO_RECOGNIZER_AUTOSCALER_CRON_SCHEDULE	cron	*/5 * * * *	How often the autoscaler will check to see if recognizer Works need to be scaled up/down
ECHO_RECOGNIZER_CLOUD_COMPUTE	Cloud Compute	gpu	The instance type each recognizer Work will use when running in the cloud.
ECHO_FILESERVER_CLOUD_COMPUTE	Cloud Compute	cpu-small	The instance type the fileserver Work will use when running in the cloud.
ECHO_FILESERVER_AUTH_TOKEN	string	None	Pre-shared key that prevents anyone other than the Flow from deleting files from the fileserver.
ECHO_YOUTUBER_MIN_REPLICAS	integer	1	Minimum number of downloader Works to keep running at all times, even if they are idle.
ECHO_YOUTUBER_MAX_IDLE_SECONDS_PER_WORK	integer	120	Autoscaler will shut down any spare downloader Works that haven't processed anything after this duration.
ECHO_YOUTUBER_MAX_PENDING_CALLS_PER_WORK	integer	10	Autoscaler will create a new downloader Work if any existing downloader Work has this many pending items to process.
ECHO_YOUTUBER_AUTOSCALER_CRON_SCHEDULE	cron	*/5 * * * *	How often the autoscaler will check to see if downloader Works need to be scaled up/down
ECHO_YOUTUBER_CLOUD_COMPUTE	Cloud Compute	cpu	The instance type each downloader Work will use when running in the cloud.
ECHO_USER_ECHOES_LIMIT	integer	100	Maximum number of Echoes that each user can create.
ECHO_SOURCE_TYPE_FILE_ENABLED	boolean	true	Allows Echoes to be created from a local file upload (.mp3, .mp4, etc)
ECHO_SOURCE_TYPE_RECORDING_ENABLED	boolean	true	Allows Echoes to be recorded with the device microphone using the UI.
ECHO_SOURCE_TYPE_YOUTUBE_ENABLED	boolean	true	Allows Echoes to be created by providing the URL to a public YouTube video.
ECHO_GARBAGE_COLLECTION_CRON_SCHEDULE	cron	None	How often the garbage collector will check for old Echoes and delete them.
ECHO_GARBAGE_COLLECTION_MAX_AGE_SECONDS	integer	86400 (24 hours)	Echoes that are older than this will be deleted (if ECHO_GARBAGE_COLLECTION_MAX_AGE_SECONDS is set).
ECHO_VIDEO_SOURCE_MAX_DURATION_SECONDS	integer	960 (15 minutes)	Restricts the length of YouTube videos that can be processed.
ECHO_DATABASE_CLOUD_COMPUTE	Cloud Compute	cpu	The instance type the database server will use when running in the cloud.
ECHO_LOADBALANCER_AUTH_TOKEN	string	None	Secret authentication token which is used for manually scaling the different Works using the /api/scale endpoint
ECHO_ROOT_PATH	string	/	Used to serve the app under a subpath or proxy.
ECHO_SENTRY_DSN	string	None	ID of the Sentry project for monitoring
ECHO_SENTRY_SAMPLE_RATE	float	0.1	Sample rate for Sentry monitoring
ECHO_FRONTEND_BUCKET_URL	string	https://storage.googleapis.com/lightning-echo-frontend	URL to the bucket containing frontend build artifacts.

Development

Prerequisites

• Python 3.8+
• git
• make
• ffmpeg
• libmagic
• yarn (for frontend development)

1. Clone the repo:

git clone git@github.com:Lightning-AI/LAI-Echo-App.git
cd LAI-Echo-App

2. Create new virtual environment:

python -m venv venv
source ./venv/bin/activate

3. Install Python dependencies:

pip install -r requirements.txt

4. Install Whisper (C++ implementation)

cd $HOME
git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp
make base

5. Install frontend dependencies:

cd echo/ui
yarn install

6. Build frontend:

cd echo/ui
yarn build

7. Run the app locally using lightning CLI:

# Run from project root
lightning run app app.py

End-to-End Tests

End-to-end tests are written using Cypress. To run them against a local instance of the app, follow these instructions:

1. Clean the environment:

./scripts/clean.sh

2. Start app locally using the instructions above.

3. Run Cypress:

cd echo/ui
yarn run e2e

Debug using VSCode

You can use the visual debugger for Python in VSCode to set breakpoints, inspect variables, and find exceptions. Add the following to .vscode/launch.json:

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Run Lightning App (local)",
      "type": "python",
      "request": "launch",
      "program": "${workspaceFolder}/debug.py",
      "console": "integratedTerminal",
      "justMyCode": false,
      "envFile": "${workspaceFolder}/.env"
    }
  ]
}