Commit 3d73b5f2 authored by Pavel KORSHUNOV's avatar Pavel KORSHUNOV

fixing sphinx

parent f0c4f3d1
Pipeline #9281 passed with stages
in 10 minutes and 13 seconds
; vim: set fileencoding=utf-8 :
; Pavel Korshunov <Pavel.Korshunov@idiap.ch>
; Thu 23 Jun 13:43:22 2016
[buildout]
parts = scripts
eggs = bob.paper.interspeech_2016
bob.db.base
bob.pad.base
bob.bio.base
bob.bio.gmm
bob.pad.voice
bob.bio.spear
gridtk
extensions = bob.buildout
mr.developer
auto-checkout = *
develop = src/bob.spoof.speech
src/bob.db.base
src/bob.pad.base
src/bob.bio.base
src/bob.bio.gmm
src/bob.bio.spear
src/bob.pad.voice
.
; options for bob.buildout
debug = true
verbose = true
newest = false
[sources]
bob.db.base = git git@gitlab.idiap.ch:bob/bob.db.base.git
bob.bio.base = git git@gitlab.idiap.ch:bob/bob.bio.base.git
bob.pad.base = git git@gitlab.idiap.ch:bob/bob.pad.base.git
bob.bio.spear = git git@gitlab.idiap.ch:bob/bob.bio.spear.git
bob.bio.gmm = git git@gitlab.idiap.ch:bob/bob.bio.gmm.git
[scripts]
recipe = bob.buildout:scripts
dependent-scripts = true
......@@ -43,19 +43,20 @@ Once the databases are downloaded, please specify the paths to these databases b
Now everything is ready to run the experiments. Here is a generic command for training GMM-based PAD system::
$ ./bin/train_gmm.py -d DB_NAME -p Preprocessor -e Feature_Extractor -a gmm-tomi -s Folder_Name -groups world --skip-enroller-training -vv
$ ./bin/train_gmm.py -d DB_NAME -p Preprocessor -e Feature_Extractor -a gmm-tomi -s Folder_Name --groups world --skip-enroller-training -vv
Here is the generic command to tune the system on developing set and evaluate on the test set::
$ ./bin/spoof.py -d DB_NAME -p Preprocessor -e Feature_Extractor -a gmm --projector-file Projector_spoof.hdf5 -s Folder_Name -groups dev eval --skip-projector-training -vv
$ ./bin/spoof.py -d DB_NAME -p Preprocessor -e Feature_Extractor -a gmm --projector-file Projector_spoof.hdf5 -s Folder_Name --groups dev eval --skip-projector-training -vv
For example, to train and evaluate a GMM-based PAD system using MFCC-based features for Licit protocol of the ASVspoof database, the following commands need to be run::
$ ./bin/train_gmm.py -d asvspoof-licit -p mod-4hz -e mfcc20 -a gmm-tomi -s temp -groups world
$ ./bin/train_gmm.py -d asvspoof-licit -p mod-4hz -e mfcc20 -a gmm-tomi -s temp --groups world
--skip-enroller-training -vv --parallel 6
$ ./bin/train_gmm.py -d asvspoof-spoof -p mod-4hz -e mfcc20 -a gmm-tomi -s temp -groups world
$ ./bin/train_gmm.py -d asvspoof-spoof -p mod-4hz -e mfcc20 -a gmm-tomi -s temp --groups world
--skip-enroller-training -vv --parallel 6
$ ./bin/spoof.py -d asvspoof -p mod-4hz -e mfcc20 -a gmm --projector-file Projector_spoof.hdf5 -s temp -groups dev eval --skip-projector-training -vv
$ ./bin/spoof.py -d asvspoof -p mod-4hz -e mfcc20 -a gmm --projector-file Projector_spoof.hdf5 -s temp
--groups dev eval --skip-projector-training -vv
Then, using the obtained scores, error rates can be computed and DET curves plotted using the following script::
......@@ -95,5 +96,5 @@ Please make sure that you have read the `Dependencies <https://github.com/idiap/
.. _bob: https://www.idiap.ch/software/bob
.. _AVspoof: https://www.idiap.ch/dataset/avspoof
.. ASVspoof_: http://datashare.is.ed.ac.uk/handle/10283/853
.. _ASVspoof: http://datashare.is.ed.ac.uk/handle/10283/853
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