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# Copyright (c) 2022, 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 json
import os
import random
import subprocess
from pathlib import Path
import numpy as np
from nemo_text_processing.text_normalization.normalize import Normalizer
from opencc import OpenCC
def get_args():
parser = argparse.ArgumentParser(
description='Prepare SF_bilingual dataset and create manifests with predefined split'
)
parser.add_argument(
"--data-root",
type=Path,
help="where the dataset will reside",
default="./DataChinese/sf_bilingual_speech_zh_en_vv1/SF_bilingual/",
)
parser.add_argument(
"--manifests-path", type=Path, help="where the resulting manifests files will reside", default="./"
)
parser.add_argument("--val-size", default=0.01, type=float, help="eval set split")
parser.add_argument("--test-size", default=0.01, type=float, help="test set split")
parser.add_argument(
"--seed-for-ds-split",
default=100,
type=float,
help="Seed for deterministic split of train/dev/test, NVIDIA's default is 100",
)
args = parser.parse_args()
return args
def __process_transcript(file_path: str):
# Create zh-TW to zh-simplify converter
cc = OpenCC('t2s')
# Create normalizer
text_normalizer = Normalizer(
lang="zh", input_case="cased", overwrite_cache=True, cache_dir=str(file_path / "cache_dir"),
)
text_normalizer_call_kwargs = {"punct_pre_process": True, "punct_post_process": True}
normalizer_call = lambda x: text_normalizer.normalize(x, **text_normalizer_call_kwargs)
entries = []
i = 0
with open(file_path / "text_SF.txt", encoding="utf-8") as fin:
for line in fin:
content = line.split()
wav_name, text = content[0], "".join(content[1:])
wav_name = wav_name.replace(u'\ufeff', '')
# WAR: change DL to SF, e.g. real wave file com_SF_ce2727.wav, wav name in text_SF
# com_DL_ce2727. It would be fixed through the dataset in the future.
wav_name = wav_name.replace('DL', 'SF')
wav_file = file_path / "wavs" / (wav_name + ".wav")
assert os.path.exists(wav_file), f"{wav_file} not found!"
duration = subprocess.check_output(f"soxi -D {wav_file}", shell=True)
simplified_text = cc.convert(text)
normalized_text = normalizer_call(simplified_text)
entry = {
'audio_filepath': os.path.abspath(wav_file),
'duration': float(duration),
'text': text,
'normalized_text': normalized_text,
}
i += 1
entries.append(entry)
return entries
def __process_data(dataset_path, val_size, test_size, seed_for_ds_split, manifests_dir):
entries = __process_transcript(dataset_path)
random.Random(seed_for_ds_split).shuffle(entries)
train_size = 1.0 - val_size - test_size
train_entries, validate_entries, test_entries = np.split(
entries, [int(len(entries) * train_size), int(len(entries) * (train_size + val_size))]
)
assert len(train_entries) > 0, "Not enough data for train, val and test"
def save(p, data):
with open(p, 'w') as f:
for d in data:
f.write(json.dumps(d) + '\n')
save(manifests_dir / "train_manifest.json", train_entries)
save(manifests_dir / "val_manifest.json", validate_entries)
save(manifests_dir / "test_manifest.json", test_entries)
def main():
args = get_args()
dataset_root = args.data_root
dataset_root.mkdir(parents=True, exist_ok=True)
__process_data(
dataset_root, args.val_size, args.test_size, args.seed_for_ds_split, args.manifests_path,
)
if __name__ == "__main__":
main()
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