Addressing review comments

MANIFEST.in

-include README.rst buildout.cfg develop.cfg version.txt
+include LICENSE README.rst buildout.cfg develop.cfg version.txt
 recursive-include doc conf.py *.rst
 recursive-include bob/kaldi/test/data *.wav *.txt *.npy *.ivector *.ie

README.rst

@@ -28,13 +28,10 @@ Bob_.
 Installation
 ------------
 
-Follow our `installation`_ instructions. Then, using the Python interpreter
-provided by the distribution, build this package with::
+To install the package, install firt bob, and then install the bob.kaldi package:
 
-  $ git clone BOB.KALDI_REPOSITORY
-  $ cd bob.kaldi
-  $ source activate BOB_ENV
-  $ buildout
+  $ conda install bob kaldi
+  $ pip install bob.kaldi
   
 To be able to work properly, some dependent packages are required to be installed.
 Please make sure that you have read the `Dependencies

bob/kaldi/mfcc.py

@@ -11,7 +11,7 @@ from . import io
 
 from subprocess import PIPE, Popen
 # import subprocess
-from os.path import exists
+from os.path import isfile
 import tempfile
 
 import logging
@@ -148,7 +148,7 @@ def mfcc_from_path(filename, channel=0, preemphasis_coefficient=0.97, raw_energy
   ]
 
   # import ipdb; ipdb.set_trace()
-  assert exists(filename)
+  assert isfile(filename)
   
   with open(os.devnull, "w") as fnull:
     pipe1 = Popen (cmd1, stdin=PIPE, stdout=PIPE, stderr=fnull)

doc/index.rst

@@ -12,7 +12,8 @@
    import pkg_resources
    import bob.kaldi
    import bob.io.audio
-
+   import tempfile
+   import os
    
 .. _bob.kaldi:
 
@@ -84,19 +85,20 @@ are supported, speakers can be enrolled and scored:
 
 .. doctest::
 
-  >>> sample = pkg_resources.resource_filename('bob.kaldi', 'test/data/sample16k.wav')
-  >>> mfcc = bob.kaldi.mfcc_from_path(sample)
   >>> # Train small diagonall GMM
-  >>> projector_file = 'Projector'
-  >>> dubm = bob.kaldi.ubm_train(mfcc, projector_file, num_gauss = 2, num_gselect = 2, num_iters = 2)
+  >>> projector = tempfile.TemporaryFile()
+  >>> dubm = bob.kaldi.ubm_train(feat, projector.name, num_gauss=2, num_gselect=2, num_iters=2)
   >>> # Train small full GMM
-  >>> ubm = bob.kaldi.ubm_full_train(mfcc, projector_file, num_gselect = 2, num_iters = 2)
+  >>> ubm = bob.kaldi.ubm_full_train(feat, projector.name, num_gselect=2, num_iters=2)
   >>> # Enrollement - MAP adaptation of the UBM-GMM
-  >>> spk_model = bob.kaldi.ubm_enroll(mfcc, dubm)
+  >>> spk_model = bob.kaldi.ubm_enroll(feat, dubm)
   >>> # GMM scoring
-  >>> score = bob.kaldi.gmm_score(mfcc, spk_model, dubm)
+  >>> score = bob.kaldi.gmm_score(feat, spk_model, dubm)
   >>> print ('%.3f' % score)
   0.282
+  >>> os.unlink(projector.name)
+  >>> os.unlink(projector.name + '.dubm')
+  >>> os.unlink(projector.name + '.fubm')
 
 Following guide describes how to run whole speaker recognition experiments:
 
@@ -104,13 +106,13 @@ Following guide describes how to run whole speaker recognition experiments:
 
 .. code-block:: sh
 		
-	./bin/verify.py -d 'mobio-audio-male' -p 'energy-2gauss' -e 'mfcc-kaldi' -a 'gmm-kaldi' -s exp-gmm-kaldi --groups {dev,eval} -R '/your/work/directory/' -T '/your/temp/directory' -vv
+	verify.py -d 'mobio-audio-male' -p 'energy-2gauss' -e 'mfcc-kaldi' -a 'gmm-kaldi' -s exp-gmm-kaldi --groups {dev,eval} -R '/your/work/directory/' -T '/your/temp/directory' -vv
 
 2. To run the ivector+plda speaker recognition experiment, run:
 
 .. code-block:: sh
 		
-	./bin/verify.py -d 'mobio-audio-male' -p 'energy-2gauss' -e 'mfcc-kaldi' -a 'ivector-plda-kaldi' -s exp-ivector-plda-kaldi --groups {dev,eval} -R '/your/work/directory/' -T '/your/temp/directory' -vv
+	verify.py -d 'mobio-audio-male' -p 'energy-2gauss' -e 'mfcc-kaldi' -a 'ivector-plda-kaldi' -s exp-ivector-plda-kaldi --groups {dev,eval} -R '/your/work/directory/' -T '/your/temp/directory' -vv
 
 3. Results: