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# Copyright (c) 2021, NVIDIA CORPORATION. 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.
import argparse
import logging
import os
import random
import re
import subprocess
from nemo.collections.nlp.data.token_classification.token_classification_utils import create_text_and_labels
from nemo.utils import logging
URL = {'tatoeba': 'https://downloads.tatoeba.org/exports/sentences.csv'}
def __maybe_download_file(destination: str, source: str):
"""
Downloads source to destination if not exists.
If exists, skips download
Args:
destination: local filepath
source: url of resource
"""
source = URL[source]
if not os.path.exists(destination):
logging.info(f'Downloading {source} to {destination}')
subprocess.run(['wget', '-O', destination, source])
else:
logging.info(f'{destination} found. Skipping download')
def __process_english_sentences(
in_file: str, out_file: str, percent_to_cut: float = 0, num_to_combine: int = 1, num_samples: int = -1
):
"""
Extract English sentences from the Tatoeba dataset.
Expected in_file format
that
contain letters and punctuation marks (,.?).
Chop and combine sentences.
Args:
in_file: local filepath to the tatoeba dataset.
Format: id [TAB] region_name [TAB] sentence,
for example: "1276\teng\tLet's try something.\n"
out_file: local filepath to the clean dataset
percent_to_cut: Percent of sentences to cut in the middle
to get examples of incomplete sentences.
This could be useful since ASR output not always
represents a complete sentence
num_to_combine: Number of sentences to combine into
a single example
num_samples: Number of samples in the final dataset
"""
if not os.path.exists(in_file):
raise FileNotFoundError(f'{in_file} not found.')
in_file = open(in_file, 'r')
out_file = open(out_file, 'w')
lines_to_combine = []
samples_count = 0
for line in in_file:
line = line.split('\t')
# use only English sentences
if line[1] == 'eng':
line = line[2].strip()
if re.match("^[A-Z][A-Za-z.,'?\s]+$", line): # nopep8
# chop some sentences in the middle
if percent_to_cut > 0:
line = line.split()
if random.random() < percent_to_cut:
line = line[: len(line) // 2]
line = ' '.join(line)
# combine multiple sentences into a single example
# to make it harder for the model to learn eos punctuation
if len(lines_to_combine) >= num_to_combine:
if samples_count == num_samples:
return
out_file.write(' '.join(lines_to_combine) + '\n')
lines_to_combine = []
samples_count += 1
lines_to_combine.append(line)
if len(lines_to_combine) > 0 and (samples_count < num_samples or num_samples < 0):
out_file.write(' '.join(lines_to_combine) + '\n')
def __split_into_train_dev(in_file: str, train_file: str, dev_file: str, percent_dev: float):
"""
Create train and dev split of the dataset.
Args:
in_file: local filepath to the dataset
train_file: local filepath to the train dataset
dev_file: local filepath to the dev dataset
percent_dev: Percent of the sentences in the dev set
"""
if not os.path.exists(in_file):
raise FileNotFoundError(f'{in_file} not found.')
lines = open(in_file, 'r').readlines()
train_file = open(train_file, 'w')
dev_file = open(dev_file, 'w')
dev_size = int(len(lines) * percent_dev)
train_file.write(' '.join(lines[:-dev_size]))
dev_file.write(' '.join(lines[-dev_size:]))
def __delete_file(file_to_del: str):
"""
Deletes the file
Args:
file_to_del: local filepath to the file to delete
"""
if os.path.exists(file_to_del):
os.remove(file_to_del)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Prepare tatoeba dataset')
parser.add_argument("--data_dir", required=True, type=str)
parser.add_argument("--dataset", default='tatoeba', type=str)
parser.add_argument("--num_samples", default=-1, type=int, help='-1 to use the whole dataset')
parser.add_argument("--percent_to_cut", default=0, type=float, help='Percent of sentences to cut in the middle')
parser.add_argument(
"--num_lines_to_combine", default=1, type=int, help='Number of lines to combine into single example'
)
parser.add_argument("--percent_dev", default=0.2, type=float, help='Size of the dev set, float')
parser.add_argument("--clean_dir", action='store_true')
args = parser.parse_args()
if not os.path.exists(args.data_dir):
os.makedirs(args.data_dir)
if args.dataset != 'tatoeba':
raise ValueError("Unsupported dataset.")
logging.info(f'Downloading tatoeba dataset')
tatoeba_dataset = os.path.join(args.data_dir, 'sentences.csv')
__maybe_download_file(tatoeba_dataset, args.dataset)
logging.info(f'Processing English sentences...')
clean_eng_sentences = os.path.join(args.data_dir, 'clean_eng_sentences.txt')
__process_english_sentences(
tatoeba_dataset, clean_eng_sentences, args.percent_to_cut, args.num_lines_to_combine, args.num_samples
)
train_file = os.path.join(args.data_dir, 'train.txt')
dev_file = os.path.join(args.data_dir, 'dev.txt')
logging.info(
f'Splitting the {args.dataset} dataset into train and dev sets' + ' and creating labels and text files'
)
__split_into_train_dev(clean_eng_sentences, train_file, dev_file, args.percent_dev)
logging.info(f'Creating text and label files for training')
create_text_and_labels(args.data_dir, os.path.join(args.data_dir, 'train.txt'))
create_text_and_labels(args.data_dir, os.path.join(args.data_dir, 'dev.txt'))
if args.clean_dir:
logging.info(f'Cleaning up {args.data_dir}')
__delete_file(clean_eng_sentences)
__delete_file(tatoeba_dataset)
__delete_file(train_file)
__delete_file(dev_file)
logging.info(f'Processing of the {args.dataset} is complete')
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