examples/nlp/token_classification/data/get_libritts_data.py

# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  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.
"""
This script downloads and unpacks LibriTTS data. And prepares it for punctuation and capitalization lexical audio model.
Data is being downloaded from www.openslr.org and then extracted via tar.
The script gathers text from every *.normalized.txt file inside of archive into single file with text and file with audio filepaths.
"""
import argparse
import glob
import os
import re
import shutil
import subprocess
import tarfile

from tqdm import tqdm

from nemo.collections.nlp.data.token_classification.token_classification_utils import create_text_and_labels
from nemo.utils import logging

URL = {
    'train_clean_100': "https://www.openslr.org/resources/60/train-clean-100.tar.gz",
    'train_clean_360': "https://www.openslr.org/resources/60/train-clean-360.tar.gz",
    'train_other_500': "https://www.openslr.org/resources/60/train-other-500.tar.gz",
    'dev_clean': "https://www.openslr.org/resources/60/dev-clean.tar.gz",
    'dev_other': "https://www.openslr.org/resources/60/dev-other.tar.gz",
    'test_clean': "https://www.openslr.org/resources/60/test-clean.tar.gz",
    'test_other': "https://www.openslr.org/resources/60/test-other.tar.gz",
}


def __extract_file(filepath, data_dir):
    try:
        tar = tarfile.open(filepath)
        tar.extractall(data_dir)
        tar.close()
    except Exception:
        print(f"Error while extracting {filepath}. Already extracted?")


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])
        return 1
    else:
        logging.info(f'{destination} found. Skipping download')
        return 0


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description='Prepare LibriTTS dataset for punctuation capitalization lexical audio model training/evaluating.'
    )
    parser.add_argument("--data_sets", default="dev_clean", type=str, help="List of subsets separated by comma")
    parser.add_argument("--data_dir", required=True, type=str, help="Path to dir where data will be stored")
    parser.add_argument(
        "--clean", "-c", action="store_true", help="If set to True will delete all files except produced .txt and .wav"
    )
    args = parser.parse_args()

    data_dir = args.data_dir

    if not os.path.exists(data_dir):
        os.makedirs(data_dir)

    for subset in args.data_sets.split(','):
        logging.info(f'Downloading {subset} subset')
        if __maybe_download_file(data_dir + f'/{subset}.tar.gz', subset):
            logging.info(f'Extracting {subset} subset')
            __extract_file(data_dir + f'/{subset}.tar.gz', data_dir)

    logging.info(f'Processing data')

    splits = set([split.split('_')[0] for split in args.data_sets.split(',')])
    for split in splits:
        os.makedirs(f'{data_dir}/audio/{split}', exist_ok=True)
        with open(f'{data_dir}/{split}.txt', 'w') as text_data, open(
            f'{data_dir}/audio_{split}.txt', 'w'
        ) as audio_data:
            for file in tqdm(glob.glob(f'{data_dir}/LibriTTS/{split}*/*/*/*.wav'), desc=f'Processing {split}'):
                with open(file[:-4] + '.normalized.txt', 'r') as source_file:
                    lines = source_file.readlines()
                    text = lines[0]
                    text = re.sub(r"[^a-zA-Z\d,?!.']", ' ', text)
                    text = re.sub(' +', ' ', text)
                shutil.copy(file.strip(), (f'{data_dir}/audio/{split}/' + file.split('/')[-1]).strip())
                text_data.write(text.strip() + "\n")
                audio_data.write((f'{data_dir}/audio/{split}/' + file.split('/')[-1]).strip() + "\n")
        create_text_and_labels(f'{data_dir}/', f'{data_dir}/{split}.txt')
        logging.info(f'Processed {split} subset')

    if args.clean:
        shutil.rmtree(f'{data_dir}/LibriTTS')
        for tar in glob.glob(f'{data_dir}/**.tar.gz'):
            os.remove(tar)