Author: Deep Learner

Neural Network

Neural G2P to portuguese language with python

neural-g2p-portuguese Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. This project was adapted from https://github.com/hajix/G2P. Dependencies The following libraries are used:pytorchtqdmmatplotlib Install dependencies using pip: pip3 install -r requirements.txt Dataset The dataset used here was taken from site http://www.portaldalinguaportuguesa.org/, as well as some insertions made by me so that the dataset would give more coverage […]

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Text-to-Speech

Attention Based Grapheme To Phoneme with python

G2P The G2P algorithm is used to generate the most probable pronunciation for a word not contained in the lexicon dictionary. It could be used as a preprocess of text-to-speech system to generate pronunciation for OOV words. Dependencies The following libraries are used:pytorchtqdmmatplotlib Install dependencies using pip: pip3 install -r requirements.txt Dataset Currently the following languages are supported: EN: English FA: Farsi RU: Russian You could easily provide and use your own language specific pronunciatin doctionary for training G2P. More […]

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Beginner, Data Science, NLP, Python, Text, Word Embeddings

Part 8: Step by Step Guide to Master NLP – Useful Natural Language Processing Tasks

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). Up to part-7 of this series, we completed the most useful concepts in NLP. While going away in this series, let’s first discuss some of the useful tasks of NLP so that you have much clarity about what you can do by learning the NLP. After this part, we will start our discussion on […]

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Advanced, NLP

Part 11: Step by Step Guide to Master NLP – Syntactic Analysis

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous article, we discussed an entity extraction technique named i.e, Named Entity Recognition. There is also another entity extraction technique which is also a popular technique named Topic Modeling, which we will discuss in the subsequent articles of our blog series. So, In this article, we will deep dive into Syntactic Analysis, […]

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Advanced, Data Engineering, Docker, Machine Learning, Model Deployment, NLP, Profile Building

Deploying Machine learning Application on AWS Fargate

Amazon Web Services(AWS) offers reliable, scalable, and cost-effective cloud computing services. It provides Infrastructure as a service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS) also a new model known as Function as a Service(FaaS) eg. AWS Lambda which is a serverless entity. Before going further, if you don’t have an AWS account please create one to follow along with the hands-on. Amazon Elastic Container Service(ECS) Amazon ECS is a container orchestration platform developed by Amazon, it is similar to […]

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Facebook NLP Research
NLP

Not All Memories are Created Equal: Learning to Forget by Expiring

Abstract Attention mechanisms have shown promising results in sequence modeling tasks that require long term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories (Rae et al., 2020). However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous […]

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Facebook NLP Research
NLP

Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task

Abstract Pretraining and multitask learning are widely used to improve the speech to text translation performance. In this study, we are interested in training a speech to text translation model along with an auxiliary text to text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information […]

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Facebook NLP Research
NLP

Adaptive Multi-Channel Signal Enhancement Based on Multi-Source Contribution Estimation

August 23, 2021 By: Jacob Donley, Vladimir Tourbabin, Boaz Rafaely, Ravish Mehra Abstract Automated solutions to multi-channel signal enhancement for improving speech communication in noisy environments has become a popular goal among the research community. Many proposed approaches focus on adapting to speech signals based on their temporal characteristics but these methods are primarily limited to specific types of desired and undesired sound sources. This paper outlines a new method to adapt to desired and undesired signals using their spatial […]

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Maps

Riemannian Convex Potential Maps with python

rcpm This repository is by Brandon Amos, Samuel Cohen and Yaron Lipman and contains the JAX source code to reproduce the experiments in our ICML 2021 paper on Riemannian Convex Potential Maps. Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited by representational and computational tradeoffs. We propose and study a class of flows that uses convex potentials from Riemannian optimal […]

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