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PaddleSpeech/deepspeech/decoders/swig/setup.py

129 lines
3.9 KiB

4 years ago
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Script to build and install decoder package."""
from setuptools import setup, Extension, distutils
import glob
import platform
import os, sys
import multiprocessing.pool
import argparse
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]
# 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('openfst-1.6.3/src/lib/*.cc')
FILES = [
Support paddle 2.x (#538) * 2.x model * model test pass * fix data * fix soundfile with flac support * one thread dataloader test pass * export feasture size add trainer and utils add setup model and dataloader update travis using Bionic dist * add venv; test under venv * fix unittest; train and valid * add train and config * add config and train script * fix ctc cuda memcopy error * fix imports * fix train valid log * fix dataset batch shuffle shift start from 1 fix rank_zero_only decreator error close tensorboard when train over add decoding config and code * test process can run * test with decoding * test and infer with decoding * fix infer * fix ctc loss lr schedule sortagrad logger * aishell egs * refactor train add aishell egs * fix dataset batch shuffle and add batch sampler log print model parameter * fix model and ctc * sequence_mask make all inputs zeros, which cause grad be zero, this is a bug of LessThanOp add grad clip by global norm add model train test notebook * ctc loss remove run prefix using ord value as text id * using unk when training compute_loss need text ids ord id using in test mode, which compute wer/cer * fix tester * add lr_deacy refactor code * fix tools * fix ci add tune fix gru model bugs add dataset and model test * fix decoding * refactor repo fix decoding * fix musan and rir dataset * refactor io, loss, conv, rnn, gradclip, model, utils * fix ci and import * refactor model add export jit model * add deploy bin and test it * rm uselss egs * add layer tools * refactor socket server new model from pretrain * remve useless * fix instability loss and grad nan or inf for librispeech training * fix sampler * fix libri train.sh * fix doc * add license on cpp * fix doc * fix libri script * fix install * clip 5 wer 7.39, clip 400 wer 7.54, 1.8 clip 400 baseline 7.49
4 years ago
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']
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++ ./decoders.i')
decoders_module = [
Extension(
name='_swig_decoders',
sources=FILES + glob.glob('*.cxx') + glob.glob('*.cpp'),
language='c++',
include_dirs=[
'.',
'kenlm',
'openfst-1.6.3/src/include',
'ThreadPool',
],
libraries=LIBS,
extra_compile_args=ARGS)
]
setup(
name='swig_decoders',
version='1.1',
description="""CTC decoders""",
ext_modules=decoders_module,
py_modules=['swig_decoders'], )