Author: Qianhui Wan Version: 1.0.0 Date : 2018-01-23
Kaldi 5.3, as well as Altas and OpenFst required by Kaldi. https://github.com/kaldi-asr/kaldi
libfvad, Voice activity detection (VAD) library, based on WebRTC's VAD engine. https://github.com/dpirch/libfvad
Install Kaldi 5.3: git clone https://github.com/kaldi-asr/kaldi.git kaldi --origin upstream cd kaldi
Install Kaldi's required libraries: cd to /kaldi/tools and follow INSTALL instructions there.
Compile and finish Kaldi install: cd to /kaldi/src and follow INSTALL instructions there.
Install libfvad: git clone https://github.com/dpirch/libfvad cd libfvad ./bootstrap ./configure make make install (perhaps sudo at this command)
Install KaldiBasedSpeakerVerification
cd KaldiBasedSpeakerVerification/src *edit makefile; provide the correct locations for this project and the libraries. make (This will output 3 executables under /src: enroll, identifySpeaker and extractFeatures)
/examples contains enroll and test examples, along with example data
/examples/iv contains i-vector features extracted from enrollment.(this can be empty before enrolling speakers, must have 2 files before testing)
/examples/mat contains background model data, must have six files.
/scripts contains scripts mainly used to create background model.
/src contains code for 3 applications: creating a background model, enrolling speakers and speaker identification.
/src/enroll.cpp This program is used to extract speech features from one speaker. Usage: enroll speakerId wavefile The output should look like: Not registered speaker: speakerId. Created a new spkid or Found registered speaker: speakerId. Updated speaker model
The wavefile should be in .wav format.
This will create/update two files in /iv: train_iv.ark and train_num_utts.ark.
/src/identifySpeaker.cpp This program process a given audio clip and output person identification every ~3.2 seconds. Usage: identifySpeaker wavefile The output should look like: Family membmer detected! Speaker: 225 Family membmer detected! Speaker: 225 Stanger detected! Family membmer detected! Speaker: 227 Family membmer detected! Speaker: 227 ...
It will also output the probability score for each segments -> this could be used to adjust the decision threshold due to different audio condition.
After installing all required applications, you can run the following examples to test if your installation is right.
make sure there is three folder in /examples /example_data /iv /mat (due to the file size limit of GitHub, final.ie was zipped into several parts. To unzip, do: cat iepart* -> final.ie)
run ./test1Enroll.sh This will enroll all speech files in /example_data/enroll The output should look like:
The total active speech is 1.61 seconds. No registered speaker: 174. Create a new spkid Done. The total active speech is 15 seconds. Found registered speaker: 174. Update speaker model Done. The total active speech is 0.88 seconds. No registered speaker: 84. Create a new spkid Done. The total active speech is 3.47 seconds. Found registered speaker: 84. Update speaker model Done.
Effective speech length: 2.605s.No family member detected. (score: 4.97931) Effective speech length: 5.685s.Family member detected! Speaker: 84 (score: 33.7779) Speech data is finished! Done.
*Note: There will also be outputs of kaldi log which look like: LOG ([5.3.96~1-7ee7]:ComputeDerivedVars():ivector-extractor.cc:183) Computing derived variables for iVector extractor LOG ([5.3.96~1-7ee7]:ComputeDerivedVars():ivector-extractor.cc:204) Done.
This tells you one audio segment has been processed and can be omitted by setting kaldi verbose level.
/src/extractFeatures The program extracts 20-dim MFCC (with energy), append deltas and double deltas, and apply CMVN Usage: extractFeatures wav.scp ark,scp:feat.ark,feat.scp Input: wav.scp, a text list of speech file name and path Output: feat.ark, feat.scp -> same as kaldi.
/scripts/data_prep.sh usage: data_prep.sh path_to_speech path_to_info prepare useful text file for later process, please refer to data_prep.sh for details
/scripts/utt2spk_to_spk2utt.pl usage: utt2spk_to_spk2utt.pl utt2spk > spk2utt create the spk2utt file with given utt2spk file
/scripts/train_ubm.sh usage: train_ubm.sh path_to_feat path_to_mat output: final.dubm, final.ubm please refer to train_ubm.sh for details
/scripts/train_ivextractor.sh usage: train_ivextractor.sh path_to_feat path_to_mat output: final.ie please refer to train_ivextractor.sh for details
/scripts/train_comp_plda.sh usage: train_comp_plda.sh path_to_feat path_to_mat output: final.plda, transform.mat, mean_vec please refer to train_comp_plda.sh for details
The following folders will be created during running: /dev_data contains development dataset speech information, MFCC features and i-vectors
/mat contains all trained models: final.dubm, final.ubm, final.ie, final.plda, transform.mat, mean_vec
Note: The whole process can take several hours (e.g. 5 to 6 hours from VirtualBox-run CentOS version). Note: All scripts need to modified manually for the path (same as examples), this can be avoided if you add all paths to environmental variables.