The project is associated with the recently-launched ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) to provide participants with baseline systems for speech recognition and speaker diarization in conference scenario.

Overview

M2MeT challenge baseline -- AliMeeting

This project provides the baseline system recipes for the ICASSP 2020 Multi-channel Multi-party Meeting Transcription Challenge (M2MeT). The challenge mainly consists of two tracks, named Automatic Speech Recognition (ASR) and Speaker Diarization. For each track, detailed descriptions can be found in its corresponding directory. The goal of this project is to simplify the training and evaluation procedures and make it flexible for participants to reproduce the baseline experiments and develop novelty methods.

Setup

git clone https://github.com/yufan-aslp/AliMeeting.git

Introduction

General steps

  1. Prepare the training data for speaker diarization and ASR model, respectively
  2. Follow the running steps of the speaker diarization experiment and obtain the rttm file. The rttm file includes the voice activity detection (VAD) and speaker diarization results, which will be used to compute the final Diarization Error Rate (DER) scores.
  3. For ASR track, we can train the single-speaker or multi-speaker ASR models. The evaluation metric of ASR systems is Character Error Rate (CER).

Citation

If you use the challenge dataset or our baseline systems, please consider citing the following:

@article{yu2021m2met,
title={M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge},
author={Yu, Fan and Zhang, Shiliang and Fu, Yihui and Xie, Lei and Zheng, Siqi and Du, Zhihao and Huang, Weilong and Guo, Pengcheng and Yan, Zhijie and Ma, Bin and others},
journal={arXiv preprint arXiv:2110.07393},
year={2021}
}

Our paper is available at https://arxiv.org/abs/2110.07393

The data download method will be sent to registered challenge participants via email.

Organizing Committee

Contributors

Code license

Apache 2.0

Owner
yufan
yufan
商品推荐系统

商品top50推荐系统 问题建模 本项目的数据集给出了15万左右的用户以及12万左右的商品, 以及对应的经过脱敏处理的用户特征和经过预处理的商品特征,旨在为用户推荐50个其可能购买的商品。 推荐系统架构方案 本项目采用传统的召回+排序的方案。

107 Dec 29, 2022
Reimplement of SimSwap training code

SimSwap-train Reimplement of SimSwap training code Instructions 1.Environment Preparation (1)Refer to the README document of SIMSWAP to configure the

seeprettyface.com 111 Dec 31, 2022
Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method

C++/ROS Source Codes for "Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method" published in IEEE Trans. Intelligent Transportation Systems

Bai Li 88 Dec 23, 2022
Mitsuba 2: A Retargetable Forward and Inverse Renderer

Mitsuba Renderer 2 Documentation Mitsuba 2 is a research-oriented rendering system written in portable C++17. It consists of a small set of core libra

Mitsuba Physically Based Renderer 2k Jan 07, 2023
deep learning model that learns to code with drawing in the Processing language

sketchnet sketchnet - processing code generator can we teach a computer to draw pictures with code. We use Processing and java/jruby code paired with

41 Dec 12, 2022
ElegantRL is featured with lightweight, efficient and stable, for researchers and practitioners.

Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. 🔥

AI4Finance 2.5k Jan 08, 2023
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdh

Ayan Kumar Bhunia 22 Aug 27, 2022
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

Thomas Winters 36 Nov 15, 2022
Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation

Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation By: Zayd Hammoudeh and Daniel Lowd Paper: Arxiv Preprint Coming soo

Zayd Hammoudeh 2 Oct 08, 2022
A library for performing coverage guided fuzzing of neural networks

TensorFuzz: Coverage Guided Fuzzing for Neural Networks This repository contains a library for performing coverage guided fuzzing of neural networks,

Brain Research 195 Dec 28, 2022
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

SANDS This is an annonymous repository containing code and data necessary to reproduce the results published in "Semi-supervised Stance Detection of T

2 Sep 22, 2022
Manifold-Mixup implementation for fastai V2

Manifold Mixup Unofficial implementation of ManifoldMixup (Proceedings of ICML 19) for fast.ai (V2) based on Shivam Saboo's pytorch implementation of

Nestor Demeure 16 Jul 25, 2022
NLG evaluation via Statistical Measures of Similarity: BaryScore, DepthScore, InfoLM

NLG evaluation via Statistical Measures of Similarity: BaryScore, DepthScore, InfoLM Automatic Evaluation Metric described in the papers BaryScore (EM

Pierre Colombo 28 Dec 28, 2022
A deep learning network built with TensorFlow and Keras to classify gender and estimate age.

Convolutional Neural Network (CNN). This repository contains a source code of a deep learning network built with TensorFlow and Keras to classify gend

Pawel Dziemiach 1 Dec 18, 2021
Dynamic Realtime Animation Control

Our project is targeted at making an application that dynamically detects the user’s expressions and gestures and projects it onto an animation software which then renders a 2D/3D animation realtime

Harsh Avinash 10 Aug 01, 2022
Code for the CVPR2022 paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity"

Introduction This is an official release of the paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity" (arxiv link). Abstrac

Leo 21 Nov 23, 2022
PyTorch implementations of the paper: "Learning Independent Instance Maps for Crowd Localization"

IIM - Crowd Localization This repo is the official implementation of paper: Learning Independent Instance Maps for Crowd Localization. The code is dev

tao han 91 Nov 10, 2022
Improving the robustness and performance of biomedical NLP models through adversarial training

RobustBioNLP Improving the robustness and performance of biomedical NLP models through adversarial training In this repository you can find suppliment

Milad Moradi 3 Sep 20, 2022
[ICCV 2021] Learning A Single Network for Scale-Arbitrary Super-Resolution

ArbSR Pytorch implementation of "Learning A Single Network for Scale-Arbitrary Super-Resolution", ICCV 2021 [Project] [arXiv] Highlights A plug-in mod

Longguang Wang 229 Dec 30, 2022
Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training"

Saliency Guided Training Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training" by Aya Abdelsalam Ismail, Hector Cor

8 Sep 22, 2022