This original repository of Dragonfly has been archived. Please see https://github.com/dictation-toolbox/dragonfly for the current actively maintained Dragonfly fork.
Dragonfly is a speech recognition framework. It is a Python package which offers a high-level object model and allows its users to easily write scripts, macros, and programs which use speech recognition.
It currently supports the following speech recognition engines:
- Dragon NaturallySpeaking (DNS), a product of Nuance
- Windows Speech Recognition (WSR), included with Microsoft Windows Vista, Windows 7, and freely available for Windows XP
Dragonfly's documentation is available online at Read the Docs. Dragonfly's FAQ is available at Stackoverflow. Dragonfly's mailing list/discussion group is available at Google Groups.
Dragonfly was written to make it very easy for Python macros, scripts, and applications to interface with speech recognition engines. Its design allows speech commands and grammar objects to be treated as first-class Python objects. This allows easy and intuitive definition of complex command grammars and greatly simplifies processing recognition results.
Language object model
The core of Dragonfly is based on a flexible object model for
handling speech elements and command grammars. This makes it
easy to define complex language constructs, but also greatly
simplifies retrieving the semantic values associated with a
speech recognition.
Support for multiple speech recognition engines
Dragonfly's modular nature lets it use different speech
recognition engines at the back end, while still providing a
single front end interface to its users. This means that a
program that uses Dragonfly can be run on any of the
supported back end engines without any modification.
Currently Dragonfly supports Dragon NaturallySpeaking and
Windows Speech Recognition (included with Windows Vista).
Built-in action framework
Dragonfly contains its own powerful framework for defining
and executing actions. It includes actions for text input
and key-stroke simulation.
The related resources page of Dragonfly's documentation has a section on command modules which lists various sources.
A very simple example of Dragonfly usage is to create a static voice command with a callback that will be called when the command is spoken. This is done as follows:
from dragonfly.all import Grammar, CompoundRule
# Voice command rule combining spoken form and recognition processing.
class ExampleRule(CompoundRule):
spec = "do something computer" # Spoken form of command.
def _process_recognition(self, node, extras): # Callback when command is spoken.
print "Voice command spoken."
# Create a grammar which contains and loads the command rule.
grammar = Grammar("example grammar") # Create a grammar to contain the command rule.
grammar.add_rule(ExampleRule()) # Add the command rule to the grammar.
grammar.load() # Load the grammar.
The example above is very basic and doesn't show any of Dragonfly's exciting features, such as dynamic speech elements. To learn more about these, please take a look at Dragonfly's online docs.
Dragonfly offers a powerful and unified interface to developers who want to use speech recognition in their software. It is used for both speech-enabling applications and for automating computer activities.
In the field of scripting and automation, there are other alternatives available that add speech-commands to increase efficiency. Dragonfly differs from them in that it is a powerful development platform. The open source alternatives currently available for use with DNS are compared to Dragonfly as follows:
-
Vocola uses its own easy-to-use scripting language, whereas Dragonfly uses Python and gives the macro-writer all the power available.
-
Unimacro offers a set of macros for common activities, whereas Dragonfly is a platform on which macro-writers can easily build new commands.