From 37bd3c00fee486696a110f2a2ee04aed9581a6cf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=2E=20Fran=C3=A7ois?= <fran.marelli@gmail.com>
Date: Mon, 6 May 2019 17:23:35 +0200
Subject: [PATCH] Public release
---
README.md | 8 ++++-
docs/README.rst | 56 +++--------------------------
docs/sphinx/Makefile | 3 ++
docs/sphinx/conf.py | 2 +-
neural_filters/__init__.py | 20 ++++++++---
neural_filters/neural_filter.py | 14 +-------
neural_filters/neural_filter_2CC.py | 15 ++------
neural_filters/neural_filter_2CD.py | 14 +-------
neural_filters/neural_filter_2R.py | 14 +-------
setup.py | 9 ++---
10 files changed, 41 insertions(+), 114 deletions(-)
diff --git a/README.md b/README.md
index 9d97dc7..df9d4a5 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,7 @@
-neural_filters is a PyTorch toolbox implementing linear IIR filters as recurrent neural units
+This package implements Neural Filters related to the following paper:
+
+ "François Marelli, Bastian Schnell, Hervé Bourlard, Thierry Dutoit, and Philip N. Garner. An end-to-end network to synthesize intonation using a generalized command response model. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, May 2019"
+
+Neural Filters are linear IIR filters integrated in the PyTorch framework, which can be interfaced with neural networks and trained by gradient descent.
+
+(c) Idiap Research Institute, Francois Marelli <francois.marelli@idiap.ch>
diff --git a/docs/README.rst b/docs/README.rst
index 68f9931..cdbc4d6 100644
--- a/docs/README.rst
+++ b/docs/README.rst
@@ -8,24 +8,12 @@ NeuralFilterCell
This module implements a basic trainable all-pole first order filter
using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful, but
-WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
class neural_filters.neural_filter.NeuralFilter(hidden_size)
A trainable first-order all-pole filter \frac{1}{1 - P z^{-1}}
@@ -51,24 +39,12 @@ NeuralFilter2R
This module implements a trainable all-pole second order filter with
real poles using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful, but
-WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
class neural_filters.neural_filter_2R.NeuralFilter2R(hidden_size)
A trainable second-order all-(real)pole filter \frac{1}{1 - P_{1}
@@ -95,24 +71,12 @@ NeuralFilter2CD
This module implements a trainable critically damped all-pole second
order filter with real poles using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful, but
-WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
class neural_filters.neural_filter_2CD.NeuralFilter2CD(hidden_size)
A trainable second-order critically damped all-pole filter
@@ -139,24 +103,12 @@ NeuralFilter2CC
This module implements a trainable all-pole second order filter with
complex conjugate poles using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful, but
-WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
class neural_filters.neural_filter_2CC.NeuralFilter2CC(hidden_size)
A trainable second-order all-pole filter \frac{1}{1 - 2 P
diff --git a/docs/sphinx/Makefile b/docs/sphinx/Makefile
index 9f9de0e..c930ea9 100644
--- a/docs/sphinx/Makefile
+++ b/docs/sphinx/Makefile
@@ -14,6 +14,9 @@ ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
# the i18n builder cannot share the environment and doctrees with the others
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+all:
+ make doc
+
.PHONY: help
help:
@echo "Please use \`make <target>' where <target> is one of"
diff --git a/docs/sphinx/conf.py b/docs/sphinx/conf.py
index e85360a..8ef4b00 100644
--- a/docs/sphinx/conf.py
+++ b/docs/sphinx/conf.py
@@ -53,7 +53,7 @@ master_doc = 'index'
# General information about the project.
project = u'Neural Filters'
-copyright = u'2018, Idiap Research Institute'
+copyright = u'2019, Idiap Research Institute'
author = u'Francois Marelli'
# The version info for the project you're documenting, acts as replacement for
diff --git a/neural_filters/__init__.py b/neural_filters/__init__.py
index b410f32..269067a 100644
--- a/neural_filters/__init__.py
+++ b/neural_filters/__init__.py
@@ -1,9 +1,21 @@
-import numpy
+"""
+neural_filters
+**************
+
+Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+
+Written by Francois Marelli <Francois.Marelli@idiap.ch>
+
+This file is part of neural_filters.
+
+"""
+
+import numpy as np
EPSILON = 1e-6
INIT_MODULUS = 0.95
MIN_ANGLE = 0
-MAX_ANGLE = numpy.pi / 2
+MAX_ANGLE = np.pi / 2
def asig(x):
@@ -13,7 +25,7 @@ def asig(x):
x[x == 1] = 1 - EPSILON
x[x == 0] = EPSILON
- return -numpy.log((1 / x) - 1)
+ return -np.log((1 / x) - 1)
def atanh(x):
@@ -22,7 +34,7 @@ def atanh(x):
x[abs(x) == 1] *= (1 - EPSILON)
- return numpy.arctanh(x)
+ return np.arctanh(x)
from .neural_filter import *
diff --git a/neural_filters/neural_filter.py b/neural_filters/neural_filter.py
index aa558d4..a7550c2 100644
--- a/neural_filters/neural_filter.py
+++ b/neural_filters/neural_filter.py
@@ -5,24 +5,12 @@ NeuralFilterCell
This module implements a basic trainable all-pole first order filter using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
"""
import numpy as np
diff --git a/neural_filters/neural_filter_2CC.py b/neural_filters/neural_filter_2CC.py
index 57d0caa..6e2e294 100644
--- a/neural_filters/neural_filter_2CC.py
+++ b/neural_filters/neural_filter_2CC.py
@@ -5,30 +5,19 @@ NeuralFilter2CC
This module implements a trainable all-pole second order filter with complex conjugate poles using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
"""
import numpy as np
import torch
from torch.nn import Parameter
from torch.nn._functions.rnn import Recurrent, VariableRecurrent
+from torch.nn import LSTMCell
from torch.nn.utils.rnn import PackedSequence
from . import MIN_ANGLE, MAX_ANGLE, INIT_MODULUS, asig, atanh
diff --git a/neural_filters/neural_filter_2CD.py b/neural_filters/neural_filter_2CD.py
index 8d7d03f..f73cfd9 100644
--- a/neural_filters/neural_filter_2CD.py
+++ b/neural_filters/neural_filter_2CD.py
@@ -5,24 +5,12 @@ NeuralFilter2CD
This module implements a trainable critically damped all-pole second order filter with real poles using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
"""
import numpy as np
diff --git a/neural_filters/neural_filter_2R.py b/neural_filters/neural_filter_2R.py
index 9cc2eaa..70d02bb 100644
--- a/neural_filters/neural_filter_2R.py
+++ b/neural_filters/neural_filter_2R.py
@@ -5,24 +5,12 @@ NeuralFilter2R
This module implements a trainable all-pole second order filter with real poles using pyTorch
-Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
+Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
-neural_filters is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License version 3 as
-published by the Free Software Foundation.
-
-neural_filters is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
-
"""
import numpy as np
diff --git a/setup.py b/setup.py
index 8d1de4b..d37047e 100644
--- a/setup.py
+++ b/setup.py
@@ -1,8 +1,8 @@
-from setuptools import setup, find_packages
+from setuptools import setup
setup(
name='neural-filters',
- version='1.2.1',
+ version='0.1',
description='Linear filters for neural networks in pyTorch',
author='François Marelli (Idiap research institute)',
author_email='francois.marelli@idiap.ch',
@@ -10,12 +10,13 @@ setup(
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
- 'License :: OSI Approved :: GNU GPL v3',
+ 'License :: OSI Approved :: MIT',
'Programming Language :: Python :: 3.6',
+ 'Programming Language :: Python :: 3.7',
],
packages=['neural_filters'],
install_requires=[
- 'torch>=0.4.0',
+ 'torch>=0.4.0,<1.0',
'numpy',
],
zip_safe=True,
--
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