A WebSocket server which allows clients to observe the text enunciated by a screen reader and to simulate user input
aria-at-automation · aria-at-automation-harness · aria-at-automation-driver · aria-at-automation-results-viewer
Download the source code by executing the following command:
git clone https://github.com/w3c/aria-at-automation-driver.git
Install the Node.js dependencies (from inside the project directory):
npm install
Run the install command in a terminal (from inside the project directory):
./bin/at-driver install
If prompted for system administration permission, grant permission.
Start the server by executing the following command in a terminal (from inside the project directory):
./bin/at-driver serve
The process will write a message to the standard error stream when the
WebSocket server is listening for connections. The --help
flag will cause
the command to output advanced usage instructions (e.g. at-driver --help
).
Configure any screen reader to use the synthesizer named "Microsoft Speech API version 5" and the text-to-speech voice named "Bocoup Automation Voice."
Use any WebSocket client to connect to the server. The protocol is described below. (The server will print protocol messages to its standard error stream for diagnostic purposes only. Neither the format nor the availability of this output is guaranteed, making it inappropriate for external use.)
type
and data
"lifecycle"
, "speech"
, or "error"
"lifecycle"
- signifies that the message data is an expected lifecycle of
the automation voice (e.g. initialization and destruction)"speech"
- signifies that the message data is text which a screen reader
has requested the operating system annunciate"error"
- signifies that an exceptional circumstances has occurredThis project implements AT Driver, a protocol published in 2024 as a W3C Draft Community Group Report. That document exhaustively describes the JSON-encoded WebSocket protocol. Please file a bug report against this project if you observe any discrepencies with AT Driver.
This tool is comprised of two main components: a text-to-speech voice and a WebSocket server.
The text-to-speech voice is written in C++ and integrates with the Microsoft Speech API (SAPI). Because it interfaces with the Windows operating system (that is: "below" the screen reader in the metaphorical software stack), it can observe speech from many screen readers without coupling to any particular screen reader.
The voice has two responsibilities. First, it emits the observed speech data and related events to a Windows named pipe. This allows the second component to present a robust public interface for programmatic consumption of the data. (The named pipe is an implementation detail. Neither its content nor its presence is guaranteed, making it inappropriate for external use.)
Second, the voice annunciates speech data. It does this by forwarding speech data to the system's default text-to-speech voice. This ensures that a system configured to use the voice remains accessible to screen reader users.
The WebSocket server is written in Node.js and allows an arbitrary number of clients to observe events on a standard interface. It has been designed as an approximation of an interface that may be exposed directly by screen readers in the future.
For details on contributing to this project, please refer to the file named
CONTRIBUTING.md
.
Licensed under the terms of the MIT Expat License; the complete text is available in the LICENSE file.
Copyright for portions of AT Driver are held by Microsoft as part of the "Sample Text-to-Speech Engine and MakeVoice" project. All other copyright for AT Driver are held by Bocoup.
A collection of projects for automating assistive technology tests from w3c/aria-at and beyond
aria-at-automation-harness
A command-line utility for executing test plans from w3c/aria-at without human intervention using the aria-at-automation-driver
aria-at-automation-driver
A WebSocket server which allows clients to observe the text enunciated by a screen reader and to simulate user input
aria-at-automation-results-viewer
A tool which translates the JSON-formatted data produced by the aria-at-automation-harness into a human-readable form