diff --git a/README.rst b/README.rst index 2572f073b6a99d60d264fbc92ff32632de743d0d..aa7433dd180465a0eb8f64f6f48b67ab47e5f6b3 100644 --- a/README.rst +++ b/README.rst @@ -21,25 +21,22 @@ =========================== This package provides pythonic bindings for Kaldi_ functionality so it can be -seemlessly integrated with Python-based workflows. It is a part fo the signal-processing and machine learning toolbox -Bob_. +seamlessly integrated with Python-based workflows. It is a part fo the signal- +processing and machine learning toolbox Bob_. Installation ------------ -To install the package, install firt bob, and then install the bob.kaldi package: +This package depends on both Bob_ and Kaldi_. To install Bob_ follow our +installation_ instructions. Kaldi_ is also bundled in our conda channnels which +means you can install Kaldi_ using conda easily too. After you have installed +Bob_, please follow these instructions to install Kaldi_ too. - $ conda install bob kaldi + # BOB_ENVIRONMENT is the name of your conda enviroment. + $ source activate BOB_ENVIRONMENT + $ conda install 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 -<https://github.com/idiap/bob/wiki/Dependencies>`_ for your operating system. - -This package also requires that Kaldi_ is properly installed alongside the -Python interpreter you're using, under the directory ``<PREFIX>/lib/kaldi``, -along with all necessary scripts and compiled binaries. Documentation @@ -48,7 +45,7 @@ Documentation For further documentation on this package, please read the `Stable Version <http://pythonhosted.org/bob.kaldi/index.html>`_ or the `Latest Version <https://www.idiap.ch/software/bob/docs/latest/bioidiap/bob.kaldi/master/index.html>`_ -of the documentation. For a list of tutorials on this or the other packages ob +of the documentation. For a list of tutorials on this or the other packages of Bob_, or information on submitting issues, asking questions and starting discussions, please visit its website. diff --git a/doc/index.rst b/doc/index.rst index 8003dc4f963e8060dda8c1510b21512d4c530dba..7ca83e4f3c59dc18940ce663c223d02c884b0372 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -14,7 +14,7 @@ import bob.io.audio import tempfile import os - + .. _bob.kaldi: @@ -48,14 +48,14 @@ MFCC Extraction --------------- Two functions are implemented to extract MFCC features -`bob.kaldi.mfcc` and `bob.kaldi.mfcc_from_path`. The former function -accepts the speech samples as `numpy.ndarray`, whereas the latter the -filename as `str`, returning the features as `numpy.ndarray`: +:py:any:`bob.kaldi.mfcc` and :py:any:`bob.kaldi.mfcc_from_path`. The former function +accepts the speech samples as :py:any:`numpy.ndarray`, whereas the latter the +filename as :py:any:`str`, returning the features as :py:any:`numpy.ndarray`: 1. `bob.kaldi.mfcc` - + .. doctest:: - + >>> sample = pkg_resources.resource_filename('bob.kaldi', 'test/data/sample16k.wav') >>> data = bob.io.audio.reader(sample) >>> feat = bob.kaldi.mfcc(data.load()[0], data.rate, normalization=False) @@ -63,20 +63,20 @@ filename as `str`, returning the features as `numpy.ndarray`: (317, 39) 2. `bob.kaldi.mfcc_from_path` - + .. doctest:: - + >>> sample = pkg_resources.resource_filename('bob.kaldi', 'test/data/sample16k.wav') >>> feat = bob.kaldi.mfcc_from_path(sample) >>> print (feat.shape) (317, 39) - + ==================== Speaker recognition ==================== - - + + UBM training and evaluation --------------------------- @@ -105,13 +105,13 @@ Following guide describes how to run whole speaker recognition experiments: 1. To run the UBM-GMM with MAP adaptation speaker recognition experiment, run: .. code-block:: sh - + 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 - + 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: