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Add Python/NumPy bindings to ctcdecode

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Reuben Morais 2018-11-02 13:51:55 -03:00
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Decoder sources originally imported from https://github.com/parlance/ctcdecode, commit 140b45860cec6671fb0bf6dbb675073241c0f9b0
Decoder sources are under the MIT license (LICENSE.parlance).
Binding code adapted from https://github.com/PaddlePaddle/DeepSpeech/tree/develop/decoders/swig, commit 3ea19973c66a6a10320888ba47a8857bebf5abfa
Binding code are under the Apache License (LICENSE.paddlepaddle).

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MIT License MIT License
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.PHONY: bindings clean
include ../definitions.mk
NUM_PROCESSES ?= 1
all: bindings
clean:
rm -rf dist temp_build ds_ctcdecoder.egg-info
rm -f swigwrapper_wrap.cpp swigwrapper.py
bindings:
pip install --quiet $(PYTHON_PACKAGES) wheel==0.31.0 setuptools==39.1.0
AS=$(AS) CC=$(CC) CXX=$(CXX) LD=$(LD) CFLAGS="$(CFLAGS) $(CXXFLAGS)" LDFLAGS="$(LDFLAGS_NEEDED)" $(PYTHON_PATH) $(NUMPY_INCLUDE) python ./setup.py build --num_processes $(NUM_PROCESSES) $(PYTHON_PLATFORM_NAME) $(SETUP_FLAGS)
find temp_build -type f -name "*.o" -delete
AS=$(AS) CC=$(CC) CXX=$(CXX) LD=$(LD) CFLAGS="$(CFLAGS) $(CXXFLAGS)" LDFLAGS="$(LDFLAGS_NEEDED)" $(PYTHON_PATH) $(NUMPY_INCLUDE) python ./setup.py bdist_wheel --num_processes $(NUM_PROCESSES) $(PYTHON_PLATFORM_NAME) $(SETUP_FLAGS)
rm -rf temp_build

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Originally imported from https://github.com/parlance/ctcdecode, commit 140b45860cec6671fb0bf6dbb675073241c0f9b0

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from __future__ import absolute_import, division, print_function
from . import swigwrapper
class Scorer(swigwrapper.Scorer):
"""Wrapper for Scorer.
:param alpha: Parameter associated with language model. Don't use
language model when alpha = 0.
:type alpha: float
:param beta: Parameter associated with word count. Don't use word
count when beta = 0.
:type beta: float
:model_path: Path to load language model.
:trie_path: Path to trie file (optional).
:alphabet: Alphabet
:type model_path: basestring
"""
def __init__(self, alpha, beta, model_path, trie_path, alphabet):
swigwrapper.Scorer.__init__(self, alpha, beta, model_path, trie_path, alphabet.config_file())
def ctc_beam_search_decoder(probs_seq,
alphabet,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
ext_scoring_func=None):
"""Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2-D list of probability distributions over each time
step, with each element being a list of normalized
probabilities over alphabet and blank.
:type probs_seq: 2-D list
:param alphabet: alphabet list.
:alphabet: Alphabet
:param beam_size: Width for beam search.
:type beam_size: int
:param cutoff_prob: Cutoff probability in pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in alphabet will be
used in beam search, default 40.
:type cutoff_top_n: int
:param ext_scoring_func: External scoring function for
partially decoded sentence, e.g. word count
or language model.
:type external_scoring_func: callable
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list
"""
beam_results = swigwrapper.ctc_beam_search_decoder(
probs_seq, alphabet.config_file(), beam_size, cutoff_prob, cutoff_top_n,
ext_scoring_func)
beam_results = [(res.probability, alphabet.decode(res.tokens)) for res in beam_results]
return beam_results
def ctc_beam_search_decoder_batch(probs_seq,
seq_lengths,
alphabet,
beam_size,
num_processes,
cutoff_prob=1.0,
cutoff_top_n=40,
ext_scoring_func=None):
"""Wrapper for the batched CTC beam search decoder.
:param probs_seq: 3-D list with each element as an instance of 2-D list
of probabilities used by ctc_beam_search_decoder().
:type probs_seq: 3-D list
:param alphabet: alphabet list.
:alphabet: Alphabet
:param beam_size: Width for beam search.
:type beam_size: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param cutoff_prob: Cutoff probability in alphabet pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in alphabet will be
used in beam search, default 40.
:type cutoff_top_n: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param ext_scoring_func: External scoring function for
partially decoded sentence, e.g. word count
or language model.
:type external_scoring_function: callable
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list
"""
batch_beam_results = swigwrapper.ctc_beam_search_decoder_batch(
probs_seq, seq_lengths, alphabet.config_file(), beam_size, num_processes,
cutoff_prob, cutoff_top_n, ext_scoring_func)
batch_beam_results = [
[(res.probability, alphabet.decode(res.tokens)) for res in beam_results]
for beam_results in batch_beam_results
]
return batch_beam_results

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# temp_build is two deep because SWIG does not clean relative paths when
# building, so ../kenlm pollutes the source directory.
[build_ext]
build-lib=temp_build/temp_build
build-temp=temp_build/temp_build
[build_py]
build-lib=temp_build/temp_build
[bdist_wheel]
bdist-dir=temp_build/temp_build
[install_lib]
build-dir=temp_build/temp_build

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#!/usr/bin/env python
from __future__ import absolute_import, division, print_function
from distutils.command.build import build
from setuptools import setup, Extension, distutils
import argparse
import glob
import multiprocessing.pool
import os
import platform
import sys
try:
import numpy
try:
numpy_include = numpy.get_include()
except AttributeError:
numpy_include = numpy.get_numpy_include()
except ImportError:
numpy_include = ''
assert 'NUMPY_INCLUDE' in os.environ
numpy_include = os.getenv('NUMPY_INCLUDE', numpy_include)
numpy_min_ver = os.getenv('NUMPY_DEP_VERSION', '')
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_processes",
default=1,
type=int,
help="Number of cpu processes to build package. (default: %(default)d)")
args = parser.parse_known_args()
# reconstruct sys.argv to pass to setup below
sys.argv = [sys.argv[0]] + args[1]
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
project_version = read('../../VERSION').strip()
# monkey-patch for parallel compilation
# See: https://stackoverflow.com/a/13176803
def parallelCCompile(self,
sources,
output_dir=None,
macros=None,
include_dirs=None,
debug=0,
extra_preargs=None,
extra_postargs=None,
depends=None):
# those lines are copied from distutils.ccompiler.CCompiler directly
macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
output_dir, macros, include_dirs, sources, depends, extra_postargs)
cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
# parallel code
def _single_compile(obj):
try:
src, ext = build[obj]
except KeyError:
return
self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
# convert to list, imap is evaluated on-demand
thread_pool = multiprocessing.pool.ThreadPool(args[0].num_processes)
list(thread_pool.imap(_single_compile, objects))
return objects
def compile_test(header, library):
dummy_path = os.path.join(os.path.dirname(__file__), "dummy")
command = "bash -c \"g++ -include " + header \
+ " -l" + library + " -x c++ - <<<'int main() {}' -o " \
+ dummy_path + " >/dev/null 2>/dev/null && rm " \
+ dummy_path + " 2>/dev/null\""
return os.system(command) == 0
# hack compile to support parallel compiling
distutils.ccompiler.CCompiler.compile = parallelCCompile
FILES = glob.glob('../kenlm/util/*.cc') \
+ glob.glob('../kenlm/lm/*.cc') \
+ glob.glob('../kenlm/util/double-conversion/*.cc')
FILES += glob.glob('third_party/openfst-1.6.7/src/lib/*.cc')
FILES = [
fn for fn in FILES
if not (fn.endswith('main.cc') or fn.endswith('test.cc') or fn.endswith(
'unittest.cc'))
]
LIBS = ['stdc++']
if platform.system() != 'Darwin':
LIBS.append('rt')
ARGS = ['-O3', '-DNDEBUG', '-DKENLM_MAX_ORDER=6', '-std=c++11',
'-Wno-unused-local-typedef', '-Wno-sign-compare']
if compile_test('zlib.h', 'z'):
ARGS.append('-DHAVE_ZLIB')
LIBS.append('z')
if compile_test('bzlib.h', 'bz2'):
ARGS.append('-DHAVE_BZLIB')
LIBS.append('bz2')
if compile_test('lzma.h', 'lzma'):
ARGS.append('-DHAVE_XZLIB')
LIBS.append('lzma')
decoder_module = Extension(
name='ds_ctcdecoder._swigwrapper',
sources=['swigwrapper.i'] + FILES + glob.glob('*.cpp'),
swig_opts=['-c++', '-extranative'],
language='c++',
include_dirs=[
numpy_include,
'..',
'../kenlm',
'third_party/openfst-1.6.7/src/include',
'third_party/ThreadPool',
],
libraries=LIBS,
extra_compile_args=ARGS
)
class BuildExtFirst(build):
sub_commands = [('build_ext', build.has_ext_modules),
('build_py', build.has_pure_modules),
('build_clib', build.has_c_libraries),
('build_scripts', build.has_scripts)]
setup(
name='ds_ctcdecoder',
version=project_version,
description="""DS CTC decoder""",
cmdclass = {'build': BuildExtFirst},
ext_modules=[decoder_module],
package_dir = {'ds_ctcdecoder': '.'},
py_modules=['ds_ctcdecoder', 'ds_ctcdecoder.swigwrapper'],
install_requires = ['numpy%s' % numpy_min_ver],
)

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%module swigwrapper
%{
#include "ctc_beam_search_decoder.h"
#define SWIG_FILE_WITH_INIT
%}
%include "pyabc.i"
%include "std_vector.i"
%include "numpy.i"
%init %{
import_array();
%}
// Convert NumPy arrays to pointer+lengths
%apply (double* IN_ARRAY2, int DIM1, int DIM2) {(const double *probs, int time_dim, int class_dim)};
%apply (double* IN_ARRAY3, int DIM1, int DIM2, int DIM3) {(const double *probs, int batch_dim, int time_dim, int class_dim)};
%apply (int* IN_ARRAY1, int DIM1) {(const int *seq_lengths, int seq_lengths_size)};
// Convert char* to Alphabet
%rename (ctc_beam_search_decoder) mod_decoder;
%inline %{
std::vector<Output>
mod_decoder(const double *probs,
int time_dim,
int class_dim,
char* alphabet_config_path,
size_t beam_size,
double cutoff_prob,
size_t cutoff_top_n,
Scorer *ext_scorer)
{
Alphabet a(alphabet_config_path);
return ctc_beam_search_decoder(probs, time_dim, class_dim, a, beam_size,
cutoff_prob, cutoff_top_n, ext_scorer);
}
%}
%rename (ctc_beam_search_decoder_batch) mod_decoder_batch;
%inline %{
std::vector<std::vector<Output>>
mod_decoder_batch(const double *probs,
int batch_dim,
int time_dim,
int class_dim,
const int *seq_lengths,
int seq_lengths_size,
char* alphabet_config_path,
size_t beam_size,
size_t num_processes,
double cutoff_prob,
size_t cutoff_top_n,
Scorer *ext_scorer)
{
Alphabet a(alphabet_config_path);
return ctc_beam_search_decoder_batch(probs, batch_dim, time_dim, class_dim,
seq_lengths, seq_lengths_size, a, beam_size,
num_processes, cutoff_prob, cutoff_top_n,
ext_scorer);
}
%}
%include "output.h"
%include "scorer.h"
%include "ctc_beam_search_decoder.h"
%template(IntVector) std::vector<int>;
%template(OutputVector) std::vector<Output>;
%template(OutputVectorVector) std::vector<std::vector<Output>>;