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import json
from tqdm import tqdm
import logging
import pickle
from collections import Counter
import re
import fire
class Vocabulary(object):
"""Simple vocabulary wrapper."""
def __init__(self):
self.word2idx = {}
self.idx2word = {}
self.idx = 0
def add_word(self, word):
if not word in self.word2idx:
self.word2idx[word] = self.idx
self.idx2word[self.idx] = word
self.idx += 1
def __call__(self, word):
if not word in self.word2idx:
return self.word2idx["<unk>"]
return self.word2idx[word]
def __getitem__(self, word_id):
return self.idx2word[word_id]
def __len__(self):
return len(self.word2idx)
def build_vocab(input_json: str,
threshold: int,
keep_punctuation: bool,
host_address: str,
character_level: bool = False,
zh: bool = True ):
"""Build vocabulary from csv file with a given threshold to drop all counts < threshold
Args:
input_json(string): Preprossessed json file. Structure like this:
{
'audios': [
{
'audio_id': 'xxx',
'captions': [
{
'caption': 'xxx',
'cap_id': 'xxx'
}
]
},
...
]
}
threshold (int): Threshold to drop all words with counts < threshold
keep_punctuation (bool): Includes or excludes punctuation.
Returns:
vocab (Vocab): Object with the processed vocabulary
"""
data = json.load(open(input_json, "r"))["audios"]
counter = Counter()
pretokenized = "tokens" in data[0]["captions"][0]
if zh:
from nltk.parse.corenlp import CoreNLPParser
from zhon.hanzi import punctuation
if not pretokenized:
parser = CoreNLPParser(host_address)
for audio_idx in tqdm(range(len(data)), leave=False, ascii=True):
for cap_idx in range(len(data[audio_idx]["captions"])):
if pretokenized:
tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split()
else:
caption = data[audio_idx]["captions"][cap_idx]["caption"]
# Remove all punctuations
if not keep_punctuation:
caption = re.sub("[{}]".format(punctuation), "", caption)
if character_level:
tokens = list(caption)
else:
tokens = list(parser.tokenize(caption))
data[audio_idx]["captions"][cap_idx]["tokens"] = " ".join(tokens)
counter.update(tokens)
else:
if pretokenized:
for audio_idx in tqdm(range(len(data)), leave=False, ascii=True):
for cap_idx in range(len(data[audio_idx]["captions"])):
tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split()
counter.update(tokens)
else:
from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
captions = {}
for audio_idx in range(len(data)):
audio_id = data[audio_idx]["audio_id"]
captions[audio_id] = []
for cap_idx in range(len(data[audio_idx]["captions"])):
caption = data[audio_idx]["captions"][cap_idx]["caption"]
captions[audio_id].append({
"audio_id": audio_id,
"id": cap_idx,
"caption": caption
})
tokenizer = PTBTokenizer()
captions = tokenizer.tokenize(captions)
for audio_idx in tqdm(range(len(data)), leave=False, ascii=True):
audio_id = data[audio_idx]["audio_id"]
for cap_idx in range(len(data[audio_idx]["captions"])):
tokens = captions[audio_id][cap_idx]
data[audio_idx]["captions"][cap_idx]["tokens"] = tokens
counter.update(tokens.split(" "))
if not pretokenized:
json.dump({ "audios": data }, open(input_json, "w"), indent=4, ensure_ascii=not zh)
words = [word for word, cnt in counter.items() if cnt >= threshold]
# Create a vocab wrapper and add some special tokens.
vocab = Vocabulary()
vocab.add_word("<pad>")
vocab.add_word("<start>")
vocab.add_word("<end>")
vocab.add_word("<unk>")
# Add the words to the vocabulary.
for word in words:
vocab.add_word(word)
return vocab
def process(input_json: str,
output_file: str,
threshold: int = 1,
keep_punctuation: bool = False,
character_level: bool = False,
host_address: str = "http://localhost:9000",
zh: bool = False):
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
logging.basicConfig(level=logging.INFO, format=logfmt)
logging.info("Build Vocab")
vocabulary = build_vocab(
input_json=input_json, threshold=threshold, keep_punctuation=keep_punctuation,
host_address=host_address, character_level=character_level, zh=zh)
pickle.dump(vocabulary, open(output_file, "wb"))
logging.info("Total vocabulary size: {}".format(len(vocabulary)))
logging.info("Saved vocab to '{}'".format(output_file))
if __name__ == '__main__':
fire.Fire(process)
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