A crash course on training speech recognition models using DeepSpeech.
Start here. This section will set your expectations for what you can achieve with the DeepSpeech Playbook, and the prerequisites you’ll need to start to train your own speech recognition models.
Once you know what you can achieve with the DeepSpeech Playbook, this section provides an overview of DeepSpeech itself, its component parts, and how it differs from other speech recognition engines you may have used in the past.
Before you can train a model, you will need to collect and format your corpus of data. This section provides an overview of the data format required for DeepSpeech, and walks through an example in prepping a dataset from Common Voice.
If you are training a model that uses a different alphabet to English, for example a language with diacritical marks, then you will need to modify the
Learn what the scorer does, and how you can go about building your own.
Learn about the differences between DeepSpeech’s acoustic model and language model and how they combine to provide end to end speech recognition.
This section walks you through building a Docker image, and spawning DeepSpeech in a Docker container with persistent storage. This approach avoids the complexities of dependencies such as
Once you have your training data formatted, and your training environment established, this section will show you how to train a model, and provide guidance for overcoming common pitfalls.
Once you’ve trained a model, you will need to validate that it works for the context it’s been designed for. This section walks you through this process.
Once trained and tested, your model is deployed. This section provides an overview of how you can deploy your model.
This section covers specific use cases where DeepSpeech can be applied to real world problems, such as transcription, keyword searching and voice controlled applications.
Learn how to set up Continuous Integration (CI) for your own fork of DeepSpeech. Intended for developers who are utilising DeepSpeech for their own specific use cases.
Providing an introduction to machine learning is beyond the scope of this PlayBook, howevever having an understanding of machine learning and deep learning concepts will aid your efforts in training speech recognition models with DeepSpeech.
Here, we’ve linked to several resources that you may find helpful; they’re listed in the order we recommend reading them in.
Digital Ocean’s introductory machine learning tutorial provides an overview of different types of machine learning. The diagrams in this tutorial are a great way of explaining key concepts.
Google’s machine learning crash course provides a gentle introduction to the main concepts of machine learning, including gradient descent, learning rate, training, test and validation sets and overfitting.
If machine learning is something that sparks your interest, then you may enjoy the MIT Open Learning Library’s Introduction to Machine Learning course, a 13-week college-level course covering perceptrons, neural networks, support vector machines and convolutional neural networks.
You can help to make the DeepSpeech PlayBook even better by providing via a GitHub Issue
Please try these instructions, particularly for building a Docker image and running a Docker container, on multiple distributions of Linux so that we can identify corner cases.