Top 5 Speech Recognition Open-Source Projects and Libraries With Most Stars on Github
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields.wikipedia
In today’s article, we are going to review the top five options for the best open-source Speech Recognition projects which has no less than 5000 stars on Github and can assist in your next project.
Data-scientist-with-python
Data-scientist-with-r
Machine-learning-scientist-with-r
Machine-learning-scientist-with-python
Machine-learning-for-everyone
Data-science-for-everyone
Data-engineer-with-python
Data-analyst-with-python
Big-data-fundamentals-via-pyspark
Coming back to the topic -
1. DeepSpeech — 15, 340 stars
DeepSpeech is an open-source speech-to-text engine which can run in real-time using a model trained by machine learning techniques based on Baidu’s Deep Speech research paper and is implemented using Tensorflow.
DeepSpeech can run in real-time on devices ranging from a Rasberry Pi 4 to any power GPU Servers and it supports various platforms for its development such as Linux, Android, Windows and macOS.
Its API supports:
- C
- .NET
- Java
- Javascript
- Python
To start using a pre-trained model or train your own model with DeepSpeech, you can follow the links below:
Github
Official Documentation
2. Leon — 7, 100 stars
Leon is an open-source personal assistant who can live on your server and is able to perform tasks when you ask him to. You can talk to him and he can talk to you, you can text him and he can text you back and the best part is Leon can communicate with you by being offline to protect your privacy.
Leon is open-source and uses AI concepts. It is built mainly using Node.js and Python and supported operating systems include: Linux, macOS and Windows.
You can find what he is able to do by browsing the: packages list and read more about it by clicking on this Link:
Github
Official Documentation
3. Wav2letter — 5, 400 stars
Wav2letter++ is Facebook AI Research’s end-to-end Automatic Speech Recognition Toolkit written entirely in C++, supporting a wide range of models and learning techniques. It is often compared to DeepSpeech due to the many similarities between the two.
Wav2letter++ also embarks a very efficient modular beam-search decoder, for both structured learning (CTC, ASG) and seq2seq approaches. Their Github repository includes recipes to reproduce the following research papers as well as pre-trained models.
To start building Recipes, clone the project from:
Github
4. Annyang — 5, 890 stars
Annyang is an Open-Source JavaScript Speech Recognition library that lets users control your site with your voice commands. It supports more than 75 languages, has no dependencies and is free to use and modify.
You can easily add a GUI(Graphical User Interface) for the user to interact with Speech Recognition using Speech KITT. Speech KITT is fully customizable and comes with many different themes, and instructions on how to create your own designs.
Github
Play with some live speech recognition demos
5. SpeechRecognition — 5, 120 stars
SpeechRecognition is a free and open-source module for performing speech recognition in Python, with support for several engines and APIs in both online and offline mode.
It has many usage examples:
- Recognize speech input from the microphone
- Transcribe an audio file
- Save audio data to an audio file
- Show extended recognition results
- Calibrate the recognizer energy threshold for ambient noise levels
- Listening to a microphone in the background
- Various other useful recognizer features
Github
Official Documentation
Summary
In this post, you discovered some of the open-source Speech Recognition Projects and Libraries which can help you in your Machine Learning and Artificial Intelligence tasks.
Do you have any questions? Ask your questions in the comments below or connect with me via my social media accounts and I will do my best to answer.
If you enjoyed reading this article, I am sure that we share similar interests and are/will be in similar industries. So let’s connect via LinkedIn and Github. Please do not hesitate to send a contact request!