refine wrapper for swig and simplify setup

deep_speech_2/deploy.py

@@ -10,8 +10,7 @@ import multiprocessing
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import deep_speech2
-from deploy.swig_decoders import *
-from swig_scorer import Scorer
+from deploy.swig_decoders_wrapper import *
 from error_rate import wer
 import utils
 import time
@@ -164,7 +163,8 @@ def infer():
     ]
 
     # external scorer
-    ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
+    ext_scorer = Scorer(
+        alpha=args.alpha, beta=args.beta, model_path=args.language_model_path)
 
     ## decode and print
     time_begin = time.time()

deep_speech_2/deploy/README.md

 ### Installation
-The setup of the decoder for deployment depends on the source code of [kenlm](https://github.com/kpu/kenlm/), first clone it to current directory (i.e., `deep_speech_2/deploy`)
+The setup of the decoder for deployment depends on the source code of [kenlm](https://github.com/kpu/kenlm/) and [openfst](http://www.openfst.org/twiki/bin/view/FST/WebHome), first clone kenlm and download openfst to current directory (i.e., `deep_speech_2/deploy`)
 
 ```shell
 git clone https://github.com/kpu/kenlm.git
+wget http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-1.6.3.tar.gz
+tar -xzvf openfst-1.6.3.tar.gz
 ```
 
 Then run the setup
 
 ```shell
-sh setup.sh
-```
-
-After the installation succeeds, go back to the parent directory
-
-```
+python setup.py install
 cd ..
 ```
 

deep_speech_2/deploy/ctc_decoders.i → deep_speech_2/deploy/decoders.i

-%module swig_ctc_decoders
+%module swig_decoders
 %{
+#include "scorer.h"
 #include "ctc_decoders.h"
 %}
 
@@ -18,6 +19,6 @@ namespace std{
     %template(PairDoubleStringVector) std::vector<std::pair<double, std::string> >;
 }
 
-%import scorer.h
 %import decoder_utils.h
+%include "scorer.h"
 %include "ctc_decoders.h"

deep_speech_2/deploy/scorer.i

-%module swig_scorer
-%{
-#include "scorer.h"
-%}
-
-%include "std_string.i"
-
-%include "scorer.h"

deep_speech_2/deploy/scorer_setup.py

-from setuptools import setup, Extension
-import glob
-import platform
-import os
-
-
-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
-
-
-FILES = glob.glob('kenlm/util/*.cc') + glob.glob('kenlm/lm/*.cc') + glob.glob(
-    'kenlm/util/double-conversion/*.cc')
-FILES = [
-    fn for fn in FILES if not (fn.endswith('main.cc') or fn.endswith('test.cc'))
-]
-
-LIBS = ['stdc++']
-if platform.system() != 'Darwin':
-    LIBS.append('rt')
-
-ARGS = ['-O3', '-DNDEBUG', '-DKENLM_MAX_ORDER=6']
-
-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')
-
-os.system('swig -python -c++ ./scorer.i')
-
-ext_modules = [
-    Extension(
-        name='_swig_scorer',
-        sources=FILES + ['scorer_wrap.cxx', 'scorer.cpp'],
-        language='C++',
-        include_dirs=['.', './kenlm'],
-        libraries=LIBS,
-        extra_compile_args=ARGS)
-]
-
-setup(
-    name='swig_scorer',
-    version='0.1',
-    ext_modules=ext_modules,
-    include_package_data=True,
-    py_modules=['swig_scorer'], )

deep_speech_2/deploy/decoder_setup.py → deep_speech_2/deploy/setup.py

@@ -20,7 +20,7 @@ LIBS = ['stdc++']
 if platform.system() != 'Darwin':
     LIBS.append('rt')
 
-ARGS = ['-O3', '-DNDEBUG', '-DKENLM_MAX_ORDER=6']
+ARGS = ['-O3', '-DNDEBUG', '-DKENLM_MAX_ORDER=6', '-std=c++11']
 
 if compile_test('zlib.h', 'z'):
     ARGS.append('-DHAVE_ZLIB')
@@ -34,24 +34,21 @@ if compile_test('lzma.h', 'lzma'):
     ARGS.append('-DHAVE_XZLIB')
     LIBS.append('lzma')
 
-os.system('swig -python -c++ ./ctc_decoders.i')
+os.system('swig -python -c++ ./decoders.i')
 
 ctc_beam_search_decoder_module = [
     Extension(
-        name='_swig_ctc_decoders',
-        sources=FILES + [
-            'scorer.cpp', 'ctc_decoders_wrap.cxx', 'ctc_decoders.cpp',
-            'decoder_utils.cpp'
-        ],
+        name='_swig_decoders',
+        sources=FILES + glob.glob('*.cxx') + glob.glob('*.cpp'),
         language='C++',
-        include_dirs=['.', './kenlm'],
+        include_dirs=['.', './kenlm', './openfst-1.6.3/src/include'],
         libraries=LIBS,
         extra_compile_args=ARGS)
 ]
 
 setup(
-    name='swig_ctc_decoders',
+    name='swig_decoders',
     version='0.1',
     description="""CTC decoders""",
     ext_modules=ctc_beam_search_decoder_module,
-    py_modules=['swig_ctc_decoders'], )
+    py_modules=['swig_decoders'], )

deep_speech_2/deploy/setup.sh

-echo "Run decoder setup ..."
-
-python decoder_setup.py install
-rm -r ./build
-
-echo "Run scorer setup ..."
-
-python scorer_setup.py install
-rm -r ./build
-
-echo "Finish the installation of decoder and scorer."

deep_speech_2/deploy/swig_decoders.py → deep_speech_2/deploy/swig_decoders_wrapper.py

@@ -3,9 +3,25 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import swig_ctc_decoders
-#import multiprocessing
-from pathos.multiprocessing import Pool
+import swig_decoders
+import multiprocessing
+
+
+class Scorer(swig_decoders.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.
+    :type model_path: basestring
+    """
+
+    def __init__(self, alpha, beta, model_path):
+        swig_decoders.Scorer.__init__(self, alpha, beta, model_path)
 
 
 def ctc_best_path_decoder(probs_seq, vocabulary):
@@ -20,8 +36,7 @@ def ctc_best_path_decoder(probs_seq, vocabulary):
     :return: Decoding result string.
     :rtype: basestring
     """
-    return swig_ctc_decoders.ctc_best_path_decoder(probs_seq.tolist(),
-                                                   vocabulary)
+    return swig_decoders.ctc_best_path_decoder(probs_seq.tolist(), vocabulary)
 
 
 def ctc_beam_search_decoder(
@@ -54,9 +69,9 @@ def ctc_beam_search_decoder(
              results, in descending order of the probability.
     :rtype: list
     """
-    return swig_ctc_decoders.ctc_beam_search_decoder(
-        probs_seq.tolist(), beam_size, vocabulary, blank_id, cutoff_prob,
-        ext_scoring_func)
+    return swig_decoders.ctc_beam_search_decoder(probs_seq.tolist(), beam_size,
+                                                 vocabulary, blank_id,
+                                                 cutoff_prob, ext_scoring_func)
 
 
 def ctc_beam_search_decoder_batch(probs_split,
@@ -86,25 +101,4 @@ def ctc_beam_search_decoder_batch(probs_split,
     pool.close()
     pool.join()
     beam_search_results = [result.get() for result in results]
-    """
-    len_args = len(probs_split)
-    beam_search_results = pool.map(ctc_beam_search_decoder,
-                                  probs_split,
-                                  [beam_size for i in xrange(len_args)],
-                                  [vocabulary for i in xrange(len_args)],
-                                  [blank_id for i in xrange(len_args)],
-                                  [cutoff_prob for i in xrange(len_args)],
-                                  [ext_scoring_func for i in xrange(len_args)]
-                                  )
-    """
-    '''
-    processes = [mp.Process(target=ctc_beam_search_decoder,
-                args=(probs_list, beam_size, vocabulary, blank_id, cutoff_prob,
-                    ext_scoring_func) for probs_list in probs_split]
-    for p in processes:
-        p.start()
-    for p in processes:
-        p.join()
-    beam_search_results = []
-    '''
     return beam_search_results