Building a Voice-Controlled Application with Python and Snips

Voice-controlled applications are becoming increasingly popular, as they allow users to interact with devices and software using only their voice. This can be especially useful for hands-free use or for people with disabilities. In this guide, we will explore how to build a voice-controlled application using Python and Snips, an open-source voice assistant platform.

Introduction to Voice Assistants and Snips

Voice assistants are digital assistants that use natural language processing (NLP) and speech recognition to understand and respond to voice commands. Some popular voice assistants include Amazon’s Alexa, Apple’s Siri and Google Assistant. These voice assistants have paved the way for voice-controlled applications in various domains such as smart homes, healthcare and entertainment.

Snips is an open-source voice assistant platform that allows developers to create their own voice-controlled applications. It offers a wide range of features, from speech recognition and natural language understanding to intent recognition and slot filling, all powered by machine learning. Snips run locally on your device, ensuring data privacy and reducing latency.

Setting Up Your Development Environment

Before diving into building your voice-controlled application, you need to set up your development environment. Here are the steps to follow:

  • Install Python: Make sure you have Python installed on your computer. You can download the latest version of Python from the official website. During installation, make sure to add Python to your system’s PATH.

  • Install Snips NLU: Snips NLU (Natural Language Understanding) is a Python library that allows you to easily train and use the Snips NLU engine. Install it using pip:

pip install snips-nlu

    Install Snips NLU English: Since we will be building an English-speaking voice assistant, we also need to install the English language resources for Snips NLU:

    pip install snips-nlu-english

    Creating a Snips Assistant

    To create a Snips assistant, you first need to define the intents and slots for your voice-controlled application. Intents represent the actions that the assistant can perform, while slots are the variables associated with those actions.

    For example, if you are building a weather application, you might have an intent called “GetWeather” with slots for the location and date. To define intents and slots, create a JSON file called assistant_dataset.json with the following structure:

    {
  "intents": [
    {
      "name": "GetWeather",
      "utterances": [
        "What's the weather like in {location}?",
        "Tell me the weather in {location} on {date}."
      ],
      "slots": [
        {
          "name": "location",
          "entity": "snips/city"
        },
        {
          "name": "date",
          "entity": "snips/datetime"
        }
      ]
    }
  ]
}

    In this example, we have created an intent called “GetWeather” with two utterances (example phrases) and two slots, “location” and “date”.

    Training Your Snips Assistant

    Once you have defined your intents and slots, you can train your Snips assistant using the Snips NLU Python library. Create a Python script called train_assistant.py and add the following code:

    import json
from snips_nlu import SnipsNLUEngine
from snips_nlu.default_configs import CONFIG_EN

with open("assistant_dataset.json", "r") as f:
    dataset = json.load(f)

nlu_engine = SnipsNLUEngine(config=CONFIG_EN)
nlu_engine.fit(dataset)

nlu_engine.persist("trained_assistant")

    This script trains the Snips assistant using the dataset from assistant_dataset.json and saves the trained model to a folder called trained_assistant.

      Integrating Snips with Python

      Now that you have trained your Snips assistant, you can integrate it with your Python application. First, create a new Python file called app.py and import the necessary libraries:

      import json
from snips_nlu import SnipsNLUEngine
Next, load the trained assistant model you saved earlier:

nlu_engine = SnipsNLUEngine.from_path("trained_assistant")

      You can now use the nlu_engine object to process user input and extract intents and slots. For example, to process a user’s voice command and print the extracted intent and slots, you can do the following:

      user_input = "What's the weather like in New York?"
result = nlu_engine.parse(user_input)

intent = result["intent"]["intentName"]
slots = {slot["slotName"]: slot["value"]["value"] for slot in result["slots"]}

print("Intent:", intent)
print("Slots:", slots)
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      Implementing Voice Commands

      With Snips integrated into your Python application, you can now implement the voice commands for your intents. To do this, create a function for each intent and use the extracted intent and slots to perform the desired action.

      For example, you can create the following function to handle the GetWeather intent:

      def get_weather(location, date=None):
    # Retrieve the weather data for the specified location and date
    # (you can use a weather API for this)
    weather_data = get_weather_data(location, date)

    # Return the weather information as a string
    return f"The weather in {location} is {weather_data['condition']} with a temperature of {weather_data['temperature']} degrees."

      Then, in your app’s main loop, call the appropriate function based on the extracted intent:

      if intent == "GetWeather":
    location = slots["location"]
    date = slots.get("date")
    response = get_weather(location, date)
    print(response)
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      Testing Your Voice-Controlled Application

      Before deploying your voice-controlled application, it’s essential to test it thoroughly to ensure that it responds correctly to various voice commands. You can do this by providing sample commands to your application and checking if it produces the expected output.

      You can also use the Snips NLU Python library’s built-in evaluation tools to measure the performance of your trained assistant. For example, you can use the snips-nlu-eval command-line tool to compute metrics such as precision, recall and F1-score.

      Deploying Your Application

      Once you are satisfied with your voice-controlled application’s performance, you can deploy it to your target device or platform. If you are targeting a specific platform like a smart speaker or a mobile app, you may need to integrate Snips with the platform’s SDK.

      For example, if you want to deploy your application to a Raspberry Pi, you can install the Snips platform on the Raspberry Pi and use the Snips MQTT API to communicate between your Python application and the Snips platform.

      Conclusion

      In this guide, we explored how to create a voice-controlled application using Python and Snips, an open-source voice assistant platform. By following the steps outlined in this article, you can build your own voice-controlled applications that respond to user commands, making your applications more accessible and user-friendly.

      Whether you’re building a smart home system, a voice-controlled entertainment app, or an accessibility tool for people with disabilities, the combination of Python and Snips offers a powerful and flexible platform for creating custom voice-controlled applications.