mirror of
https://github.com/k2-fsa/icefall.git
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fixed broken softlinks (#1381)
* removed broken softlinks * fixed dependencies * fixed file permission
This commit is contained in:
zr_jin
GitHub
parent
1b2e99d374
commit
6d275ddf9f
@ -1 +0,0 @@
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../../../librispeech/ASR/shared/convert-k2-to-openfst.py
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102
icefall/shared/convert-k2-to-openfst.py
Executable file
102
icefall/shared/convert-k2-to-openfst.py
Executable file
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#!/usr/bin/env python3
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# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This script takes as input an FST in k2 format and convert it
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to an FST in OpenFST format.
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The generated FST is saved into a binary file and its type is
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StdVectorFst.
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Usage examples:
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(1) Convert an acceptor
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./convert-k2-to-openfst.py in.pt binary.fst
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(2) Convert a transducer
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./convert-k2-to-openfst.py --olabels aux_labels in.pt binary.fst
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"""
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import argparse
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import logging
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from pathlib import Path
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import k2
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import kaldifst.utils
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import torch
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--olabels",
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type=str,
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default=None,
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help="""If not empty, the input FST is assumed to be a transducer
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and we use its attribute specified by "olabels" as the output labels.
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""",
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)
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parser.add_argument(
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"input_filename",
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type=str,
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help="Path to the input FST in k2 format",
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)
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parser.add_argument(
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"output_filename",
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type=str,
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help="Path to the output FST in OpenFst format",
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)
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return parser.parse_args()
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def main():
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args = get_args()
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logging.info(f"{vars(args)}")
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input_filename = args.input_filename
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output_filename = args.output_filename
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olabels = args.olabels
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if Path(output_filename).is_file():
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logging.info(f"{output_filename} already exists - skipping")
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return
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assert Path(input_filename).is_file(), f"{input_filename} does not exist"
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logging.info(f"Loading {input_filename}")
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k2_fst = k2.Fsa.from_dict(torch.load(input_filename))
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if olabels:
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assert hasattr(k2_fst, olabels), f"No such attribute: {olabels}"
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p = Path(output_filename).parent
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if not p.is_dir():
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logging.info(f"Creating {p}")
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p.mkdir(parents=True)
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logging.info("Converting (May take some time if the input FST is large)")
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fst = kaldifst.utils.k2_to_openfst(k2_fst, olabels=olabels)
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logging.info(f"Saving to {output_filename}")
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fst.write(output_filename)
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if __name__ == "__main__":
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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@ -1 +0,0 @@
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../../../librispeech/ASR/shared/ngram_entropy_pruning.py
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630
icefall/shared/ngram_entropy_pruning.py
Executable file
630
icefall/shared/ngram_entropy_pruning.py
Executable file
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#
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# Copyright 2021 Johns Hopkins University (Author: Ruizhe Huang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Usage:
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./ngram_entropy_pruning.py \
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-threshold 1e-8 \
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-lm download/lm/4gram.arpa \
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-write-lm download/lm/4gram_pruned_1e8.arpa
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This file is from Kaldi `egs/wsj/s5/utils/lang/ngram_entropy_pruning.py`.
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This is an implementation of ``Entropy-based Pruning of Backoff Language Models''
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in the same way as SRILM.
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"""
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import argparse
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import gzip
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import logging
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import math
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import re
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from collections import OrderedDict, defaultdict
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from enum import Enum, unique
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from io import StringIO
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parser = argparse.ArgumentParser(
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description="""
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Prune an n-gram language model based on the relative entropy
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between the original and the pruned model, based on Andreas Stolcke's paper.
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An n-gram entry is removed, if the removal causes (training set) perplexity
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of the model to increase by less than threshold relative.
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The command takes an arpa file and a pruning threshold as input,
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and outputs a pruned arpa file.
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"""
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)
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parser.add_argument("-threshold", type=float, default=1e-6, help="Order of n-gram")
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parser.add_argument("-lm", type=str, default=None, help="Path to the input arpa file")
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parser.add_argument(
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"-write-lm", type=str, default=None, help="Path to output arpa file after pruning"
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)
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parser.add_argument(
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"-minorder",
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type=int,
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default=1,
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help="The minorder parameter limits pruning to ngrams of that length and above.",
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)
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parser.add_argument(
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"-encoding", type=str, default="utf-8", help="Encoding of the arpa file"
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)
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parser.add_argument(
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"-verbose",
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type=int,
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default=2,
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choices=[0, 1, 2, 3, 4, 5],
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help="Verbose level, where 0 is most noisy; 5 is most silent",
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)
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args = parser.parse_args()
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default_encoding = args.encoding
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logging.basicConfig(
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format="%(asctime)s — %(levelname)s — %(funcName)s:%(lineno)d — %(message)s",
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level=args.verbose * 10,
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)
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class Context(dict):
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"""
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This class stores data for a context h.
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It behaves like a python dict object, except that it has several
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additional attributes.
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"""
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def __init__(self):
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super().__init__()
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self.log_bo = None
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class Arpa:
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"""
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This is a class that implement the data structure of an APRA LM.
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It (as well as some other classes) is modified based on the library
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by Stefan Fischer:
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https://github.com/sfischer13/python-arpa
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"""
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UNK = "<unk>"
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SOS = "<s>"
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EOS = "</s>"
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FLOAT_NDIGITS = 7
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base = 10
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@staticmethod
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def _check_input(my_input):
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if not my_input:
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raise ValueError
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elif isinstance(my_input, tuple):
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return my_input
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elif isinstance(my_input, list):
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return tuple(my_input)
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elif isinstance(my_input, str):
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return tuple(my_input.strip().split(" "))
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else:
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raise ValueError
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@staticmethod
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def _check_word(input_word):
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if not isinstance(input_word, str):
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raise ValueError
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if " " in input_word:
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raise ValueError
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def _replace_unks(self, words):
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return tuple((w if w in self else self._unk) for w in words)
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def __init__(self, path=None, encoding=None, unk=None):
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self._counts = OrderedDict()
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self._ngrams = (
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OrderedDict()
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) # Use self._ngrams[len(h)][h][w] for saving the entry of (h,w)
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self._vocabulary = set()
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if unk is None:
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self._unk = self.UNK
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if path is not None:
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self.loadf(path, encoding)
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def __contains__(self, ngram):
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h = ngram[:-1] # h is a tuple
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w = ngram[-1] # w is a string/word
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return h in self._ngrams[len(h)] and w in self._ngrams[len(h)][h]
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def contains_word(self, word):
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self._check_word(word)
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return word in self._vocabulary
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def add_count(self, order, count):
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self._counts[order] = count
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self._ngrams[order - 1] = defaultdict(Context)
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def update_counts(self):
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for order in range(1, self.order() + 1):
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count = sum([len(wlist) for _, wlist in self._ngrams[order - 1].items()])
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if count > 0:
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self._counts[order] = count
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def add_entry(self, ngram, p, bo=None, order=None):
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# Note: ngram is a tuple of strings, e.g. ("w1", "w2", "w3")
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h = ngram[:-1] # h is a tuple
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w = ngram[-1] # w is a string/word
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# Note that p and bo here are in fact in the log domain (self.base = 10)
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h_context = self._ngrams[len(h)][h]
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h_context[w] = p
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if bo is not None:
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self._ngrams[len(ngram)][ngram].log_bo = bo
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for word in ngram:
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self._vocabulary.add(word)
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def counts(self):
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return sorted(self._counts.items())
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def order(self):
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return max(self._counts.keys(), default=None)
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def vocabulary(self, sort=True):
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if sort:
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return sorted(self._vocabulary)
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else:
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return self._vocabulary
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def _entries(self, order):
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return (
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self._entry(h, w)
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for h, wlist in self._ngrams[order - 1].items()
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for w in wlist
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)
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def _entry(self, h, w):
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# return the entry for the ngram (h, w)
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ngram = h + (w,)
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log_p = self._ngrams[len(h)][h][w]
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log_bo = self._log_bo(ngram)
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if log_bo is not None:
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return (
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round(log_p, self.FLOAT_NDIGITS),
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ngram,
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round(log_bo, self.FLOAT_NDIGITS),
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)
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else:
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return round(log_p, self.FLOAT_NDIGITS), ngram
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def _log_bo(self, ngram):
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if len(ngram) in self._ngrams and ngram in self._ngrams[len(ngram)]:
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return self._ngrams[len(ngram)][ngram].log_bo
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else:
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return None
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def _log_p(self, ngram):
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h = ngram[:-1] # h is a tuple
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w = ngram[-1] # w is a string/word
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if h in self._ngrams[len(h)] and w in self._ngrams[len(h)][h]:
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return self._ngrams[len(h)][h][w]
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else:
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return None
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def log_p_raw(self, ngram):
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log_p = self._log_p(ngram)
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if log_p is not None:
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return log_p
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else:
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if len(ngram) == 1:
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raise KeyError
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else:
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log_bo = self._log_bo(ngram[:-1])
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if log_bo is None:
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log_bo = 0
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return log_bo + self.log_p_raw(ngram[1:])
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def log_joint_prob(self, sequence):
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# Compute the joint prob of the sequence based on the chain rule
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# Note that sequence should be a tuple of strings
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#
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# Reference:
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# https://github.com/BitSpeech/SRILM/blob/d571a4424fb0cf08b29fbfccfddd092ea969eae3/lm/src/LM.cc#L527
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log_joint_p = 0
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seq = sequence
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while len(seq) > 0:
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log_joint_p += self.log_p_raw(seq)
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seq = seq[:-1]
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# If we're computing the marginal probability of the unigram
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# <s> context we have to look up </s> instead since the former
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# has prob = 0.
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if len(seq) == 1 and seq[0] == self.SOS:
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seq = (self.EOS,)
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return log_joint_p
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def set_new_context(self, h):
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old_context = self._ngrams[len(h)][h]
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self._ngrams[len(h)][h] = Context()
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return old_context
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def log_p(self, ngram):
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words = self._check_input(ngram)
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if self._unk:
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words = self._replace_unks(words)
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return self.log_p_raw(words)
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def log_s(self, sentence, sos=SOS, eos=EOS):
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words = self._check_input(sentence)
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if self._unk:
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words = self._replace_unks(words)
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if sos:
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words = (sos,) + words
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if eos:
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words = words + (eos,)
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result = sum(self.log_p_raw(words[:i]) for i in range(1, len(words) + 1))
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if sos:
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result = result - self.log_p_raw(words[:1])
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return result
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def p(self, ngram):
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return self.base ** self.log_p(ngram)
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def s(self, sentence):
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return self.base ** self.log_s(sentence)
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def write(self, fp):
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fp.write("\n\\data\\\n")
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for order, count in self.counts():
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fp.write("ngram {}={}\n".format(order, count))
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fp.write("\n")
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for order, _ in self.counts():
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fp.write("\\{}-grams:\n".format(order))
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for e in self._entries(order):
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prob = e[0]
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ngram = " ".join(e[1])
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if len(e) == 2:
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fp.write("{}\t{}\n".format(prob, ngram))
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elif len(e) == 3:
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backoff = e[2]
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fp.write("{}\t{}\t{}\n".format(prob, ngram, backoff))
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else:
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raise ValueError
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fp.write("\n")
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fp.write("\\end\\\n")
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class ArpaParser:
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"""
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This is a class that implement a parser of an arpa file
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"""
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@unique
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class State(Enum):
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DATA = 1
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COUNT = 2
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HEADER = 3
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ENTRY = 4
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re_count = re.compile(r"^ngram (\d+)=(\d+)$")
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re_header = re.compile(r"^\\(\d+)-grams:$")
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re_entry = re.compile(
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"^(-?\\d+(\\.\\d+)?([eE]-?\\d+)?)"
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"\t"
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"(\\S+( \\S+)*)"
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"(\t((-?\\d+(\\.\\d+)?)([eE]-?\\d+)?))?$"
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)
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def _parse(self, fp):
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self._result = []
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self._state = self.State.DATA
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self._tmp_model = None
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self._tmp_order = None
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for line in fp:
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line = line.strip()
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if self._state == self.State.DATA:
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self._data(line)
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elif self._state == self.State.COUNT:
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self._count(line)
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elif self._state == self.State.HEADER:
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self._header(line)
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elif self._state == self.State.ENTRY:
|
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self._entry(line)
|
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if self._state != self.State.DATA:
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raise Exception(line)
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return self._result
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def _data(self, line):
|
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if line == "\\data\\":
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self._state = self.State.COUNT
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self._tmp_model = Arpa()
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else:
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pass # skip comment line
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|
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def _count(self, line):
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match = self.re_count.match(line)
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if match:
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order = match.group(1)
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count = match.group(2)
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self._tmp_model.add_count(int(order), int(count))
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elif not line:
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self._state = self.State.HEADER # there are no counts
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else:
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raise Exception(line)
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def _header(self, line):
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match = self.re_header.match(line)
|
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if match:
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self._state = self.State.ENTRY
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self._tmp_order = int(match.group(1))
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elif line == "\\end\\":
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self._result.append(self._tmp_model)
|
||||
self._state = self.State.DATA
|
||||
self._tmp_model = None
|
||||
self._tmp_order = None
|
||||
elif not line:
|
||||
pass # skip empty line
|
||||
else:
|
||||
raise Exception(line)
|
||||
|
||||
def _entry(self, line):
|
||||
match = self.re_entry.match(line)
|
||||
if match:
|
||||
p = self._float_or_int(match.group(1))
|
||||
ngram = tuple(match.group(4).split(" "))
|
||||
bo_match = match.group(7)
|
||||
bo = self._float_or_int(bo_match) if bo_match else None
|
||||
self._tmp_model.add_entry(ngram, p, bo, self._tmp_order)
|
||||
elif not line:
|
||||
self._state = self.State.HEADER # last entry
|
||||
else:
|
||||
raise Exception(line)
|
||||
|
||||
@staticmethod
|
||||
def _float_or_int(s):
|
||||
f = float(s)
|
||||
i = int(f)
|
||||
if str(i) == s: # don't drop trailing ".0"
|
||||
return i
|
||||
else:
|
||||
return f
|
||||
|
||||
def load(self, fp):
|
||||
"""Deserialize fp (a file-like object) to a Python object."""
|
||||
return self._parse(fp)
|
||||
|
||||
def loadf(self, path, encoding=None):
|
||||
"""Deserialize path (.arpa, .gz) to a Python object."""
|
||||
path = str(path)
|
||||
if path.endswith(".gz"):
|
||||
with gzip.open(path, mode="rt", encoding=encoding) as f:
|
||||
return self.load(f)
|
||||
else:
|
||||
with open(path, mode="rt", encoding=encoding) as f:
|
||||
return self.load(f)
|
||||
|
||||
def loads(self, s):
|
||||
"""Deserialize s (a str) to a Python object."""
|
||||
with StringIO(s) as f:
|
||||
return self.load(f)
|
||||
|
||||
def dump(self, obj, fp):
|
||||
"""Serialize obj to fp (a file-like object) in ARPA format."""
|
||||
obj.write(fp)
|
||||
|
||||
def dumpf(self, obj, path, encoding=None):
|
||||
"""Serialize obj to path in ARPA format (.arpa, .gz)."""
|
||||
path = str(path)
|
||||
if path.endswith(".gz"):
|
||||
with gzip.open(path, mode="wt", encoding=encoding) as f:
|
||||
return self.dump(obj, f)
|
||||
else:
|
||||
with open(path, mode="wt", encoding=encoding) as f:
|
||||
self.dump(obj, f)
|
||||
|
||||
def dumps(self, obj):
|
||||
"""Serialize obj to an ARPA formatted str."""
|
||||
with StringIO() as f:
|
||||
self.dump(obj, f)
|
||||
return f.getvalue()
|
||||
|
||||
|
||||
def add_log_p(prev_log_sum, log_p, base):
|
||||
return math.log(base**log_p + base**prev_log_sum, base)
|
||||
|
||||
|
||||
def compute_numerator_denominator(lm, h):
|
||||
log_sum_seen_h = -math.inf
|
||||
log_sum_seen_h_lower = -math.inf
|
||||
base = lm.base
|
||||
for w, log_p in lm._ngrams[len(h)][h].items():
|
||||
log_sum_seen_h = add_log_p(log_sum_seen_h, log_p, base)
|
||||
|
||||
ngram = h + (w,)
|
||||
log_p_lower = lm.log_p_raw(ngram[1:])
|
||||
log_sum_seen_h_lower = add_log_p(log_sum_seen_h_lower, log_p_lower, base)
|
||||
|
||||
numerator = 1.0 - base**log_sum_seen_h
|
||||
denominator = 1.0 - base**log_sum_seen_h_lower
|
||||
return numerator, denominator
|
||||
|
||||
|
||||
def prune(lm, threshold, minorder):
|
||||
# Reference:
|
||||
# https://github.com/BitSpeech/SRILM/blob/d571a4424fb0cf08b29fbfccfddd092ea969eae3/lm/src/NgramLM.cc#L2330
|
||||
|
||||
for i in range(
|
||||
lm.order(), max(minorder - 1, 1), -1
|
||||
): # i is the order of the ngram (h, w)
|
||||
logging.info("processing %d-grams ..." % i)
|
||||
count_pruned_ngrams = 0
|
||||
|
||||
h_dict = lm._ngrams[i - 1]
|
||||
for h in list(h_dict.keys()):
|
||||
# old backoff weight, BOW(h)
|
||||
log_bow = lm._log_bo(h)
|
||||
if log_bow is None:
|
||||
log_bow = 0
|
||||
|
||||
# Compute numerator and denominator of the backoff weight,
|
||||
# so that we can quickly compute the BOW adjustment due to
|
||||
# leaving out one prob.
|
||||
numerator, denominator = compute_numerator_denominator(lm, h)
|
||||
|
||||
# assert abs(math.log(numerator, lm.base) - math.log(denominator, lm.base) - h_dict[h].log_bo) < 1e-5
|
||||
|
||||
# Compute the marginal probability of the context, P(h)
|
||||
h_log_p = lm.log_joint_prob(h)
|
||||
|
||||
all_pruned = True
|
||||
pruned_w_set = set()
|
||||
|
||||
for w, log_p in h_dict[h].items():
|
||||
ngram = h + (w,)
|
||||
|
||||
# lower-order estimate for ngramProb, P(w|h')
|
||||
backoff_prob = lm.log_p_raw(ngram[1:])
|
||||
|
||||
# Compute BOW after removing ngram, BOW'(h)
|
||||
new_log_bow = math.log(
|
||||
numerator + lm.base**log_p, lm.base
|
||||
) - math.log(denominator + lm.base**backoff_prob, lm.base)
|
||||
|
||||
# Compute change in entropy due to removal of ngram
|
||||
delta_prob = backoff_prob + new_log_bow - log_p
|
||||
delta_entropy = -(lm.base**h_log_p) * (
|
||||
(lm.base**log_p) * delta_prob
|
||||
+ numerator * (new_log_bow - log_bow)
|
||||
)
|
||||
|
||||
# compute relative change in model (training set) perplexity
|
||||
perp_change = lm.base**delta_entropy - 1.0
|
||||
|
||||
pruned = threshold > 0 and perp_change < threshold
|
||||
|
||||
# Make sure we don't prune ngrams whose backoff nodes are needed
|
||||
if (
|
||||
pruned
|
||||
and len(ngram) in lm._ngrams
|
||||
and len(lm._ngrams[len(ngram)][ngram]) > 0
|
||||
):
|
||||
pruned = False
|
||||
|
||||
logging.debug(
|
||||
"CONTEXT "
|
||||
+ str(h)
|
||||
+ " WORD "
|
||||
+ w
|
||||
+ " CONTEXTPROB %f " % h_log_p
|
||||
+ " OLDPROB %f " % log_p
|
||||
+ " NEWPROB %f " % (backoff_prob + new_log_bow)
|
||||
+ " DELTA-H %f " % delta_entropy
|
||||
+ " DELTA-LOGP %f " % delta_prob
|
||||
+ " PPL-CHANGE %f " % perp_change
|
||||
+ " PRUNED "
|
||||
+ str(pruned)
|
||||
)
|
||||
|
||||
if pruned:
|
||||
pruned_w_set.add(w)
|
||||
count_pruned_ngrams += 1
|
||||
else:
|
||||
all_pruned = False
|
||||
|
||||
# If we removed all ngrams for this context we can
|
||||
# remove the context itself, but only if the present
|
||||
# context is not a prefix to a longer one.
|
||||
if all_pruned and len(pruned_w_set) == len(h_dict[h]):
|
||||
del h_dict[
|
||||
h
|
||||
] # this context h is no longer needed, as its ngram prob is stored at its own context h'
|
||||
elif len(pruned_w_set) > 0:
|
||||
# The pruning for this context h is actually done here
|
||||
old_context = lm.set_new_context(h)
|
||||
|
||||
for w, p_w in old_context.items():
|
||||
if w not in pruned_w_set:
|
||||
lm.add_entry(
|
||||
h + (w,), p_w
|
||||
) # the entry hw is stored at the context h
|
||||
|
||||
# We need to recompute the back-off weight, but
|
||||
# this can only be done after completing the pruning
|
||||
# of the lower-order ngrams.
|
||||
# Reference:
|
||||
# https://github.com/BitSpeech/SRILM/blob/d571a4424fb0cf08b29fbfccfddd092ea969eae3/flm/src/FNgramLM.cc#L2124
|
||||
|
||||
logging.info("pruned %d %d-grams" % (count_pruned_ngrams, i))
|
||||
|
||||
# recompute backoff weights
|
||||
for i in range(
|
||||
max(minorder - 1, 1) + 1, lm.order() + 1
|
||||
): # be careful of this order: from low- to high-order
|
||||
for h in lm._ngrams[i - 1]:
|
||||
numerator, denominator = compute_numerator_denominator(lm, h)
|
||||
new_log_bow = math.log(numerator, lm.base) - math.log(denominator, lm.base)
|
||||
lm._ngrams[len(h)][h].log_bo = new_log_bow
|
||||
|
||||
# update counts
|
||||
lm.update_counts()
|
||||
|
||||
return
|
||||
|
||||
|
||||
def check_h_is_valid(lm, h):
|
||||
sum_under_h = sum(
|
||||
[lm.base ** lm.log_p_raw(h + (w,)) for w in lm.vocabulary(sort=False)]
|
||||
)
|
||||
if abs(sum_under_h - 1.0) > 1e-6:
|
||||
logging.info("warning: %s %f" % (str(h), sum_under_h))
|
||||
return False
|
||||
else:
|
||||
return True
|
||||
|
||||
|
||||
def validate_lm(lm):
|
||||
# sanity check if the conditional probability sums to one under each context h
|
||||
for i in range(lm.order(), 0, -1): # i is the order of the ngram (h, w)
|
||||
logging.info("validating %d-grams ..." % i)
|
||||
h_dict = lm._ngrams[i - 1]
|
||||
for h in h_dict.keys():
|
||||
check_h_is_valid(lm, h)
|
||||
|
||||
|
||||
def compare_two_apras(path1, path2):
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# load an arpa file
|
||||
logging.info("Loading the arpa file from %s" % args.lm)
|
||||
parser = ArpaParser()
|
||||
models = parser.loadf(args.lm, encoding=default_encoding)
|
||||
lm = models[0] # ARPA files may contain several models.
|
||||
logging.info("Stats before pruning:")
|
||||
for i, cnt in lm.counts():
|
||||
logging.info("ngram %d=%d" % (i, cnt))
|
||||
|
||||
# prune it, the language model will be modified in-place
|
||||
logging.info("Start pruning the model with threshold=%.3E..." % args.threshold)
|
||||
prune(lm, args.threshold, args.minorder)
|
||||
|
||||
# validate_lm(lm)
|
||||
|
||||
# write the arpa language model to a file
|
||||
logging.info("Stats after pruning:")
|
||||
for i, cnt in lm.counts():
|
||||
logging.info("ngram %d=%d" % (i, cnt))
|
||||
logging.info("Saving the pruned arpa file to %s" % args.write_lm)
|
||||
parser.dumpf(lm, args.write_lm, encoding=default_encoding)
|
||||
logging.info("Done.")
|
||||
@ -1 +0,0 @@
|
||||
../../../librispeech/ASR/shared/parse_options.sh
|
||||
97
icefall/shared/parse_options.sh
Executable file
97
icefall/shared/parse_options.sh
Executable file
@ -0,0 +1,97 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
|
||||
# Arnab Ghoshal, Karel Vesely
|
||||
|
||||
# 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
|
||||
#
|
||||
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||||
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
|
||||
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
|
||||
# MERCHANTABLITY OR NON-INFRINGEMENT.
|
||||
# See the Apache 2 License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
# Parse command-line options.
|
||||
# To be sourced by another script (as in ". parse_options.sh").
|
||||
# Option format is: --option-name arg
|
||||
# and shell variable "option_name" gets set to value "arg."
|
||||
# The exception is --help, which takes no arguments, but prints the
|
||||
# $help_message variable (if defined).
|
||||
|
||||
|
||||
###
|
||||
### The --config file options have lower priority to command line
|
||||
### options, so we need to import them first...
|
||||
###
|
||||
|
||||
# Now import all the configs specified by command-line, in left-to-right order
|
||||
for ((argpos=1; argpos<$#; argpos++)); do
|
||||
if [ "${!argpos}" == "--config" ]; then
|
||||
argpos_plus1=$((argpos+1))
|
||||
config=${!argpos_plus1}
|
||||
[ ! -r $config ] && echo "$0: missing config '$config'" && exit 1
|
||||
. $config # source the config file.
|
||||
fi
|
||||
done
|
||||
|
||||
|
||||
###
|
||||
### Now we process the command line options
|
||||
###
|
||||
while true; do
|
||||
[ -z "${1:-}" ] && break; # break if there are no arguments
|
||||
case "$1" in
|
||||
# If the enclosing script is called with --help option, print the help
|
||||
# message and exit. Scripts should put help messages in $help_message
|
||||
--help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2;
|
||||
else printf "$help_message\n" 1>&2 ; fi;
|
||||
exit 0 ;;
|
||||
--*=*) echo "$0: options to scripts must be of the form --name value, got '$1'"
|
||||
exit 1 ;;
|
||||
# If the first command-line argument begins with "--" (e.g. --foo-bar),
|
||||
# then work out the variable name as $name, which will equal "foo_bar".
|
||||
--*) name=`echo "$1" | sed s/^--// | sed s/-/_/g`;
|
||||
# Next we test whether the variable in question is undefned-- if so it's
|
||||
# an invalid option and we die. Note: $0 evaluates to the name of the
|
||||
# enclosing script.
|
||||
# The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar
|
||||
# is undefined. We then have to wrap this test inside "eval" because
|
||||
# foo_bar is itself inside a variable ($name).
|
||||
eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
|
||||
|
||||
oldval="`eval echo \\$$name`";
|
||||
# Work out whether we seem to be expecting a Boolean argument.
|
||||
if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then
|
||||
was_bool=true;
|
||||
else
|
||||
was_bool=false;
|
||||
fi
|
||||
|
||||
# Set the variable to the right value-- the escaped quotes make it work if
|
||||
# the option had spaces, like --cmd "queue.pl -sync y"
|
||||
eval $name=\"$2\";
|
||||
|
||||
# Check that Boolean-valued arguments are really Boolean.
|
||||
if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then
|
||||
echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2
|
||||
exit 1;
|
||||
fi
|
||||
shift 2;
|
||||
;;
|
||||
*) break;
|
||||
esac
|
||||
done
|
||||
|
||||
|
||||
# Check for an empty argument to the --cmd option, which can easily occur as a
|
||||
# result of scripting errors.
|
||||
[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1;
|
||||
|
||||
|
||||
true; # so this script returns exit code 0.
|
||||
Loading…
x
Reference in New Issue
Block a user