This article will also talk about solutions with speech recognition functions. We've included the most well-known products, but if you want to go deeper into voice technologies, check out the projects with 5k stars on GitHub, including DeepSpeech, Leon, Wav2letter, and Annyang. We also recommend looking at Voice APIs and similar platforms for flexible customization of the entire communication chain.
1. TensorFlow
One of the most popular deep learning platforms developed by Google. It has a wide range of tools and libraries for building AI bots. TensorFlow has a flexible architecture and is filled with multiple builds for every stage of AI development, whether it's model deployment or data preparation, making it a great choice for building complex applications.
Pros:
- high level of abstraction
- Interactivity - can create, modify and customize the model on the fly
- Flexibility - works in different directions: neural networks, Deep learning, etc.
- cross-platform - support for popular operating systems and cloud platforms
- large community, so answers to popular questions can be found without much difficulty
Cons:
- steep learning curve
- difficult for beginners, requires technical skills and general understanding of industry specifics
2. PyTorch
A popular deep learning library with a simple and intuitive interface. Provides a wide range of features for creating and training AI models. It supports smooth scaling and development on major cloud platforms, and has a strong ecosystem of tools and libraries.
Pros:
- Contains many modular parts that you can easily combine, and develop your own layer types if you want to
- open source, distributed for free
Cons:
- users usually need to write their own tutorial code
- no commercial support
3. Dialogflow
Cloud platform for creating AI programs with NLP support. Gives the developer the opportunity to create a chatbot that receives and processes commands in natural language. The service includes powerful tools for working with dialog systems, and it can also integrate with various platforms.
Pros:
- wide range of capabilities
- flexibility in processing text queries
- $600 to new customers for a free trial of Dialogflow
Cons:
- Does not support simultaneous work with other frameworks, project dependency on Dialogflow
- limit on the number of text queries per minute in the free version
Cost:
after the end of the trial period, the price of a text query is $0.007.
4. Rasa
A toolkit for making any AI bot, providing features for developing different types of bots, including text and voice bots. Rasa has a flexible architecture and supports multiple languages and platforms. You can find a ton of useful tools in their GitHub that you can use to build assistants, including using the Microsoft Bot Framework, Telegram, Mattermost, and MTS Exolve.
If you connect Rasa to the Voice or SMS APIs, for example, it can create assistants capable of having multi-level conversations with lots of branching. Integration possibilities can be explored in the documentation of Rasa and our SMS API.
Pros:
- Possibility to implement any bot functions
- Ability to create a contextual assistant for popular messengers (Telegram, Slack, Google Hangouts, etc.).
- free of charge
Cons:
- on the server side, chatbots made with Rasa take a lot of resources
- it does not have a direct connection to chat systems that are already configured and ready to work.
5. Microsoft Bot Framework
The tool for creating and deploying AI bots from the famous IT giant has powerful speech recognition and NLP functions, and also provides integration with other Microsoft platforms such as Skype, Microsoft Teams and Slack. The framework comes with multilingual support for easy communication with users from all over the world. It works well with the Azure service platform, an integrated development environment to speed up and simplify the development of any new bot.
Among other things, the company offers an emulator, QnA creation tools, analytics, and speech recognition services.
Pros:
- A set of ready-made models for Microsoft's internal products is built in
- SDKs for different computer languages are implemented
- possibility of machine learning of speech to text
Cons:
- No possibility to install the NLU kernel locally
Cost:
- work with standard channels: free of charge
- work with premium channels: after monthly provided 10 thousand free messages - $0.50 per 1 thousand.
6. Pandorabots
Pandorabots is one of the oldest open source chatbot platforms still in use today. The project went into production back in 2008, and today it is quite popular on GitHub. AIML, an artificial intelligence markup language, is used here to script conversations with the chatbot. The framework has premium paid libraries and modules such as Mitsuku as well as free and open source libraries such as ALICE, Rosie and Base Bot.
There are Speech-to-text and Text-to-speech tools, cross-language capabilities, and a RESTful API to integrate with any application, so there's no problem building advanced options into sales and support via APIs.
Pros:
- Allows you to create an AI modeling language in any natural language
- intuitive interface
Cons:
- chatbot can't start a discussion on its own
Cost:
- free plan with limited features
- Developer plan (from $19 for 10 thousand messages per month)
- Professional ($199 for 100 thousand messages per month)
7. Botpress
BotPress is an open-source service for creating AI software written in TypeScript. It provides a set of tools for developing complex chatbots with NLP processing and integration with various communication channels. The integration has a modular architecture and supports many extensions.
Pros:
- Allows you to create automating communications and workflows in companies
- secure data storage
Cons:
- No role assignment, white label widgets and other advanced features
8. Chatfuel
A modern platform used to create more than 350 thousand different bots based on Messenger. One of the most popular open source systems. As of today, Netflix, Visa, Levi's, Nissan, and Lego use the service.
Pros:
- convenient interface of communication with the user through an internal chatbot
- well-built functions of customer service with a dynamic reporting interface
Cons:
- initial setup takes a considerable amount of time
- few options among compatible channels
Cost:
- Free plan with limited features
- PRO ($15 per month)
9. Wit.ai
An AI bot development platform specializing in natural language processing, it provides APIs and tools to create AI bots that can understand and answer user questions. Wit.ai has a simple interface and can be integrated with various communication channels. This free and open source chatbot builder supports 132 languages with voice control features.
The developers offered a nice example with temperature detection in their documentation, you can try this logic for your scripts, it comes with HTTP API as well.
Pros:
- The platform uses machine learning to analyze every human interaction and is constantly improving natural language processing
- Ability to create a voice interface for mobile apps
Cons:
- small community
10. Botkit
An open-source platform managed by Microsoft with an integrated set of content management, analytics and operational tools, with an advanced learning mode to help answer common customer queries.
Pros:
- An active community and 11k stars on GitHub
Cons:
- No native NLP support and uses Microsoft's LUIS service for the NLU (Natural Language Understanding) part of the framework
Conclusion
Choosing a framework for AI bot development depends on your needs, experience and preferences. Each of the above options has its own features and benefits, so it's important to do additional research and test their functionality before you begin development.
Regardless of the framework you choose, developing an AI bot is a fun and exciting process of creating innovative and useful solutions. We hope this article will help you find the right tool for you and inspire you to create an AI bot. If you have your own tools that you apply when developing such bots, please tell us about them.
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