rosannamilner / segment-f-measure Goto Github PK
View Code? Open in Web Editor NEWScoring metric for diarisation focussing on segment correctness applying the F-measure
License: Apache License 2.0
Scoring metric for diarisation focussing on segment correctness applying the F-measure
License: Apache License 2.0
########################################## # CONTACT ########################################## Rosanna Milner, Thomas Hain @ SPandh, University of Sheffield https://mini.dcs.shef.ac.uk/resources/speaker-diarisation-evaluation/ [ICASSP 2016] "Segment-oriented evaluation of Speaker Diarisation performance" @inproceedings{Milner2016icassp, author = {Rosanna Milner and Thomas Hain}, title = {Segment-oriented evaluation of speaker diarisation performance}, booktitle = {{ICASSP}}, pages = {5460--5464}, publisher = {{IEEE}}, year = {2016} } ########################################## # USAGE ########################################## This script takes a reference RTTM file (or list of RTTM files) and a hypothesis RTTM file (or a list of RTTM files) and calculates the segment F-measure for speaker diarisation Evaluation. For using list examples and collar 0.1: $ ./segmentfmeasure_v1.0.py ./rttm/list.ref.rttm ./rttm/list.sys.rttm -c 0.1 --list For using concatenated examples (or single files) and collar 0.1: $ ./segmentfmeasure_v1.0.py ./rttm/example1+example2.ref.rttm ./rttm/example1+example2.sys.rttm -c 0.1 ########################################## # HELP (./segmentfmeasure_v1.0.py -h) ########################################## usage: segmentfmeasure_v1.0.py [-h] [-u UEM] [-g GAP] [-c COLLAR] [-d {uniform,triangular,Gaussian}] [-t THRESHOLD] [-p PADDING] [-cs COLLAR_SCALE] [-f FOLDER] [--sad] [-m] [--list] [--save] ref hyp positional arguments: ref Reference RTTM file (or list with flag --list) hyp Hypothesis RTTM file (or list with flag --list) optional arguments: -h, --help show this help message and exit -u UEM, --uem UEM UEM file (can be single file or list) -g GAP, --gap GAP Smoothing gap (seconds) -c COLLAR, --collar COLLAR Collar around reference boundaries (+/- seconds) -d {uniform,triangular,Gaussian}, --distribution {uniform,triangular,Gaussian} Distribution around reference boundaries -t THRESHOLD, --threshold THRESHOLD Threshold for segment match/no match decision (not uniform distribution) -p PADDING, --padding PADDING Padding around hypothesis boundary -cs COLLAR_SCALE, --collar-scale COLLAR_SCALE Scale to multiply collar if boundary type NONSPEECH -f FOLDER, --folder FOLDER Folder in which to save smoothed RTTM files --sad Score Speech Activity Detection only - ignore speaker labels -m, --spkr-map Display speaker mapping information --list REF and HYP are lists of RTTMs --save Save smoothed RTTM files ########################################## # OUPUT ########################################## SE: speaker error (sum of impure reference speakers and missed reference speakers SSE: speaker segment error (hypothesis segments assigned to the incorrect speaker) MAT: reference-hypothesis segment matches INS: hypothesis segment insertions (unmatched to a reference segment) DEL: reference segment deletions (unmatched to a hypothesis segment) PRC: segment precision RCL: segment recall F: segment F-measure The overall score for SE is weighted by the number of reference speakers, SSE is weigthed by the number of matched segments used for the speaker mapping stage, and the rest are weigthed by the number of reference segments. For example: $ ./segmentfmeasure_v1.3.py rttm/example1.ref.rttm ./rttm/example1.sys.rttm -u uem/example1.uem -c 0.1 -------------------------------- FILE: example1 EVAL TIME: 50.00 TO 250.00 REF SEGMENTS: 72 HYP SEGMENTS: 110 REF SEGMENTS (SMOOTHED GAP 0.25): 60 COLLAR: 0.10 -------- REF SPEAKERS: 4 HYP CLUSTERS: 4 BOUNDARY MATCHED SEGMENTS: 36 MAPPED SPEAKER-CLUSTER PAIRS: 4 UNASSIGNED SPEAKERS: 0 UNASSIGNED CLUSTERS: 0 PURE SPEAKER-CLUSTER PAIRS: 2 IMPURE SPEAKER-CLUSTER PAIRS: 2 SPEAKER ERROR (SE): 50.00 % ( (2 + 0) / 4 ) SPEAKER SEGMENT ERROR (SSE): 5.56 % ( 2 / 36 ) -------- SE: 50.0 % SSE: 5.6 % MAT: 56.7 % INS: 95.0 % DEL: 43.3 % PRC: 37.4 % RCL: 56.7 % F: 45.0 %
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.