/TEDIUM_AHG

A Speech recognition baseline model base on Kaldi toolkit and Tedlium dataset

Primary LanguageShellMIT LicenseMIT

TED-LIUM AHG Speech Recognition Baseline

Introduction

A Kaldi baseline model adapted to personal/non-clustering machines.

Version Released

  • s5_r1

    1. Revised from original egs/tedlium/s5_r1 script
    2. Using updated Kaldi library

  • s5_r2

    1. A tutorial example for undergraduate students
    2. Based on AISHELL-1 framework
    3. Using TED-LIUM 1 corpus

TODO

  1. To enable GPU exclusive mode via command "sudo nvidia-smi -c 3"
  2. Type "screen" in command prompt to enter background process
  3. cd s5_r2
  4. Configure the variable exactDataDir in path.sh (The desired dataset location)
  5. bash run.sh

Learning Objectives

These are several hints for you to prepare your in-lab meeting presentation.

  • Basic

    1. Which type of features are used?
    2. Overall model building flow (To explain each step)
    3. Arguments of each line of code (file I/O)
    4. How to decode other wave files
    5. How to know the overall performance

  • Intermediate

    1. To visualize features (MFCC files, ivectors)
    2. To decode graph, decision trees
    3. Knowing the structure of neural networks