This is a tool to separate speakers in a list of audio files.
It is quite easy to use. You have a two-step-approach:
- Enroll Speakers
- Recognize Speakers
You have two usage models as well: enroll and predict
python msr.py -m mymodel.model -c enroll -e data
python msr.py -m mymodel.model -c predict -p test -l mylogfile.csv
If you want to enroll speakers, you need to create a directory containing the training data (wav-files). If we call the directory data
and we want to identify two different speakers, the structure is like this:
data
+--> speaker_1
| +---> file1.wav
| +---> file2.wav
| ...
+--> speaker_2
| +---> file1.wav
| ...
Then you can start the tool to enroll them using:
python msr.py -m <model_name> -c enroll -e data
That's all
For prediction, equally, you should save all your wav-files you want to analyze into a directory (e.g. 'test') and then you can start prediction.
python msr.py -m <model_name> -c predict -p test [-l mylogfile.csv]
You must have python 2.7 and various libraries installed (numpy, scipy). Additionally, you need to install pyssp
.
Lastly, please install scikits.talkbox
Optional: fast-gmm and bob (see below)
Install the scikits.talkbox from the contrib
-directory using the installation instructions there...
--WORK IN PROGRESS--
You can speed things a little if you compile the library gmm
in msrmodel/gmm
. Normally, the only thing you have to do is go in there and type:
make
Everything else is already setup. But it may be buggy :-)
--END WORK IN PROGRESS--
You can speed-up things even more if you install bob
-packages:
pip install bob.extension bob.blitz bob.core bob.sp bob.ap
That's all folks, hope you have fun...
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