Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

Overview

Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

This repository is derived from the NMTGMinor project at https://github.com/quanpn90/NMTGMinor
The SVCCA calculation is derived from https://github.com/nlp-dke/svcca

Powered by Mediaan.com


Speech Translation (ST) is the task of translating speech audio in a source language into text in a target language. This repository implements and experiments on different approaches for ST:

  • Cascaded ST, including 2 steps: Automatic Speech Recognition (ASR) and Machine Translation (MT)
  • Direct ST: models trained only on ST data
  • (Main contribution) End-to-end ST limiting the use of ST data: multi-modal models leveraging ASR and MT training data for ST task

The Transformer architecture is used as the baseline for the implementation.

High-level instruction to use the repo:

  • Run covost_data_preparation.py to download and preprocess the data.
  • Run the shell script of interst, change the variables in the script if needed.
    • run_translation_pipeline.sh for single-task models (ASR, MT, ST)
    • cascaded_ST_evaluation.sh evaluates cascaded ST using pretrained ASR and MT models
    • run_translation_multi_modalities_pipeline.sh for multi-task, multi-modality models (including zero-shot)
    • run_zeroshot_with_artificial_data.sh for zero-shot models using data augmentation
    • run_bidirectional_zeroshot.sh for zero-shot models using additional opposite training data
    • run_fine_tunning.sh, run_fine_tunning_fromASR.sh for fine-tuning models with ST data, resulting in few-shot models
    • modality_similarity_svcca.sh, modality_similarity_classifier.sh measure text-audio similarity in representation

See notebooks/Repo_Instruction.ipynb for more details.

Owner
Tu Anh Dinh
Tu Anh Dinh
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

80 Dec 27, 2022
offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

PeterPu 76 Dec 08, 2022
Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).

Microsoft365_devicePhish Abusing Microsoft 365 OAuth Authorization Flow for Phishing Attack This is a simple proof-of-concept script that allows an at

Alex 236 Dec 21, 2022
Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

Ali Akram 4 Dec 28, 2021
CarND-LaneLines-P1 - Lane Finding Project for Self-Driving Car ND

Finding Lane Lines on the Road Overview When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are a

Udacity 769 Dec 27, 2022
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2:ImageNet无限制对抗攻击 介绍 : 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。

25 Dec 08, 2022
Large-Scale Unsupervised Object Discovery

Large-Scale Unsupervised Object Discovery Huy V. Vo, Elena Sizikova, Cordelia Schmid, Patrick Pérez, Jean Ponce [PDF] We propose a novel ranking-based

17 Sep 19, 2022
A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Pedro Savarese 35 Jul 29, 2022
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

DLR-RM 4.7k Jan 01, 2023
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

TensorFlow Examples This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and so

Aymeric Damien 42.5k Jan 08, 2023
Face recognition project by matching the features extracted using SIFT.

MV_FaceDetectionWithSIFT Face recognition project by matching the features extracted using SIFT. By : Aria Radmehr Professor : Ali Amiri Dependencies

Aria Radmehr 4 May 31, 2022
PyTorch implementation for our AAAI 2022 Paper "Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning"

deepGCFX PyTorch implementation for our AAAI 2022 Paper "Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning" Pr

Thilini Cooray 4 Aug 11, 2022
A library for graph deep learning research

Documentation | Paper [JMLR] | Tutorials | Benchmarks | Examples DIG: Dive into Graphs is a turnkey library for graph deep learning research. Why DIG?

DIVE Lab, Texas A&M University 1.3k Jan 01, 2023
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022
Code for ICCV2021 paper PARE: Part Attention Regressor for 3D Human Body Estimation

PARE: Part Attention Regressor for 3D Human Body Estimation [ICCV 2021] PARE: Part Attention Regressor for 3D Human Body Estimation, Muhammed Kocabas,

Muhammed Kocabas 277 Jan 03, 2023
Point cloud processing tool library.

Point Cloud ToolBox This point cloud processing tool library can be used to process point clouds, 3d meshes, and voxels. Environment python 3.7.5 Dep

ZhangXinyun 40 Dec 09, 2022
This repository contains the data and code for the paper "Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors" ([email protected])

GP-VAE This repository provides datasets and code for preprocessing, training and testing models for the paper: Diverse Text Generation via Variationa

Wanyu Du 18 Dec 29, 2022
Image Completion with Deep Learning in TensorFlow

Image Completion with Deep Learning in TensorFlow See my blog post for more details and usage instructions. This repository implements Raymond Yeh and

Brandon Amos 1.3k Dec 23, 2022