A complete speech segmentation system using Kaldi and x-vectors for voice activity detection (VAD) and speaker diarisation.

Overview

bbc-speech-segmenter: Voice Activity Detection & Speaker Diarization

A complete speech segmentation system using Kaldi and x-vectors for voice activity detection (VAD) and speaker diarisation.

The x-vector-vad system is described in the paper; Ogura, M. & Haynes, M. (2021) X-vector-vad for Multi-genre Broadcast Speech-to-text. The paper has been submitted to 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) and is currently under review as of June 2021.

Quickstart

$ docker pull bbcrd/bbc-speech-segmenter

# Test

$ docker run -w /wrk -v `pwd`:/wrk bbcrd/bbc-speech-segmenter ./test.sh

# Segmentation help

$ docker run bbcrd/bbc-speech-segmenter ./run-segmentation.sh --help
usage: run-segmentation.sh [options] input.wav input.stm output-dir

options:
  --nj NUM                 Maximum number of CPU cores to use
  --stage STAGE            Start from this stage
  --cluster-threshold THR  Cluster stopping criteria. Default: -0.3
  --vad-threshold THR      Xvector classifier threshold. Lower the number the
                           more speech segments shall be returned at the
                           expense of accuracy. Default: 0.2
  --vad-method             Filter segments on an individual or segment basis.
                           Default: individual
  --no-vad                 Skip xvector vad stages. Default: false
  --help                   Print this message

# Run segmentation (VAD + diarisation), results are in output-dir/diarize.stm

$ docker run -v `pwd`:/data bbcrd/bbc-speech-segmenter \
  ./run-segmentation.sh /data/audio.wav /data/audio.stm /data/output-dir

$ cat output-dir/diarize.stm
audio 0 audio_S00004 3.750 10.125 <speech>
audio 0 audio_S00003 10.125 13.687 <speech>
audio 0 audio_S00004 13.688 16.313 <speech>
...

# Train x-vector classifier

$ docker run -w /wrk/recipe -v `pwd`:/wrk bbcrd/bbc-speech-segmenter \
  local/xvector_utils.py data/bbc-vad-train/reference.stm            \
  data/bbc-vad-train/xvectors.ark new_model.pkl

# Evaluate x-vector classifier

$ docker run -w /wrk/recipe -v `pwd`:/wrk bbcrd/bbc-speech-segmenter \
  local/xvector_utils.py evaluate data/bbc-vad-eval/reference.stm    \
  data/bbc-vad-eval/xvectors.ark model/xvector-classifier.pkl

Audio & STM file format

In order to run the segmentation script you need your audio in 16Khz Mono WAV format. You also need an STM file describing the segments you want to apply voice activity detection and speaker diarization to.

For more information on the STM file format see XVECTOR_UTILS.md.

# Convert audio file to 16Khz mono wav

$ ffmpeg audio.mp3 -vn -ac 1 -ar 16000 audio.wav

# Create STM file for input

$ DURATION=$(ffprobe -i audio.wav -show_entries format=duration -v quiet -of csv="p=0")
$ DURATION=$(printf "%0.2f\n" $DURATION)

$ FILENAME=$(basename audio.wav)

$ echo "${FILENAME%.*} 0 ${FILENAME%.*} 0.00 $DURATION <label> _" > audio.stm

$ cat audio.stm
audio 0 audio 0.00 60.00 <label> _

Use Docker image to run code in local checkout

# Bulid Docker image

$ docker build -t bbc-speech-segmenter .

# Spin up a Docker container in an interactive mode

$ docker run -it -v `pwd`:/wrk bbc-speech-segmenter /bin/bash

# Inside a Docker container

$ cd /wrk/

# Run test

$ ./test.sh
All checks passed

Training and evaluation

X-vector utility

xvector_utils.py can be used to train and evaluate x-vector classifier, as well as o extract and visualize x-vectors. For more detailed information, see XVECTOR_UTILS.md.

The documentation also gives details on file formats such as ARK, SCP or STM, which are required to use this tool.

Run x-vector VAD training

Two files are required for x-vector-vad training:

  • Reference STM file
  • X-vectors ARK file

For example, from inside the Docker container:

$ cd /wrk/recipe

$ python3 local/xvector_utils.py train \
  data/bbc-vad-train/reference.stm     \
  data/bbc-vad-train/xvectors.ark      \
  new_model.pkl

The model will be saved as new_model.pkl.

Run x-vector VAD evaluation

Three files are needed in order to run VAD evaluation:

  • Reference STM file
  • X-vectors ARK file
  • x-vector-vad classifier model

For example, from inside the Docker container:

$ cd /wrk/recipe

$ python3 local/xvector_utils.py evaluate \
  data/bbc-vad-eval/reference.stm        \
  data/bbc-vad-eval/xvectors.ark         \
  model/xvector-classifier.pkl

WebRTC baseline

The code for the baseline WebRTC system referenced in the paper is available in the directory recipe/baselines/denoising_DIHARD18_webrtc.

Request access to bbc-vad-train

Due to size restriction, only bbc-vad-eval is included in the repository. If you'd like access to bbc-vad-train, please contact Matt Haynes.

Authors

Owner
BBC
Open source code used on public facing services, internal services and educational resources.
BBC
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation

Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle

Yunjey Choi 864 Dec 30, 2022
hipCaffe: the HIP port of Caffe

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Cent

ROCm Software Platform 126 Dec 05, 2022
Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Art041_NN_Feed_Forward Public repository containing materials used for Feed Forward (FF) Neural Networks article. -- Illustration of a very simple Fee

SolClover 2 Dec 29, 2021
A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

CoVA: Context-aware Visual Attention for Webpage Information Extraction Abstract Webpage information extraction (WIE) is an important step to create k

Keval Morabia 41 Jan 01, 2023
"Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion"(WWW 2021)

STAR_KGC This repo contains the source code of the paper accepted by WWW'2021. "Structure-Augmented Text Representation Learning for Efficient Knowled

Bo Wang 60 Dec 26, 2022
A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery

PiSL A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery. Sun, F., Liu, Y. and Sun, H., 2021. Physics-informe

Fangzheng (Andy) Sun 8 Jul 13, 2022
A Simple Key-Value Data-store written in Python

mercury-db This is a File Based Key-Value Datastore that supports basic CRUD (Create, Read, Update, Delete) operations developed using Python. The dat

Vaidhyanathan S M 1 Jan 09, 2022
Transformers based fully on MLPs

Awesome MLP-based Transformers papers An up-to-date list of Transformers based fully on MLPs without attention! Why this repo? After transformers and

Fawaz Sammani 35 Dec 30, 2022
Tensorflow implementation of DeepLabv2

TF-deeplab This is a Tensorflow implementation of DeepLab, compatible with Tensorflow 1.2.1. Currently it supports both training and testing the ResNe

Chenxi Liu 21 Sep 27, 2022
Shuffle Attention for MobileNetV3

SA-MobileNetV3 Shuffle Attention for MobileNetV3 Train Run the following command for train model on your own dataset: python train.py --dataset mnist

Sajjad Aemmi 36 Dec 28, 2022
The official implementation of ICCV paper "Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds".

Box-Aware Tracker (BAT) Pytorch-Lightning implementation of the Box-Aware Tracker. Box-Aware Feature Enhancement for Single Object Tracking on Point C

Kangel Zenn 5 Mar 26, 2022
An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

A Gentle Introduction to Satellite Image Processing Welcome to this introductory course on Satellite Image Analysis! Satellite imagery has become a pr

Edward Oughton 32 Jan 03, 2023
TLDR: Twin Learning for Dimensionality Reduction

TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self

NAVER 105 Dec 28, 2022
Code of the lileonardo team for the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021

Emotion and Theme Recognition in Music The repository contains code for the submission of the lileonardo team to the 2021 Emotion and Theme Recognitio

Vincent Bour 8 Aug 02, 2022
KAPAO is an efficient multi-person human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

KAPAO (Keypoints and Poses as Objects) KAPAO is an efficient single-stage multi-person human pose estimation model that models keypoints and poses as

Will McNally 664 Dec 30, 2022
Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

PyLabel pip install pylabel PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. I

PyLabel Project 176 Jan 01, 2023
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"

On-the-Fly Adaptation Official Pytorch Code base for On-the-Fly Test-time Adaptation for Medical Image Segmentation Paper Introduction One major probl

Jeya Maria Jose 17 Nov 10, 2022
Final term project for Bayesian Machine Learning Lecture (XAI-623)

Mixquality_AL Final Term Project For Bayesian Machine Learning Lecture (XAI-623) Youtube Link The presentation is given in YoutubeLink Problem Formula

JeongEun Park 3 Jan 18, 2022
Official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch.

Multi-speaker DGP This repository provides official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch. O

sarulab-speech 24 Sep 07, 2022