Inferring on your own SpeechBrain models
In this tutorial, we will learn the three different ways of inferring on a trained model. This is particularly useful to debug your pipeline or to deploy a model in a production context.
Open in Google Colab 
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Pre-trained Models and Fine-Tuning with 
Training DNN models is often very time-consuming and expensive.
For this reason, whenever it is possible, using off-the-shelf pretrained
models can be convenient in various scenarios.
We provide a simple and straightforward way to download and instantiate a
state-of-the-art pretrained-model from HuggingFace
and use it either for direct inference or
or fine-tuning/knowledge distillation or whatever new fancy technique you can come up with!
Data Loading for Big Datasets and Shared Filesystems
Do you have a huge dataset stored in a shared file system? This tutorial will show you how to load large datasets from the shared file system and use them for training a neural network with SpeechBrain. In particular, we describe a solution based on the WebDataset library, that is easy to integrate within the SpeechBrain toolkit.
Open in Google ColabText Tokenizer
Machine Learning tasks that process text may contain thousands of vocabulary words which leads to models dealing with huge embeddings as input/output (e.g. for one-hot-vectors and ndim=vocabulary_size). This causes an important consumption of memory, complexe computations, and more importantly, sub-optimal learning due to extremely sparse and cumbersome one-hot vectors. In this tutorial, we provide all the basics needed to correctly use the SpeechBrain Tokenizer relying on SentencePiece (BPE and unigram).
Open in Google Colab