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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 json
import os
import pandas as pd
from nemo.utils import logging
def main():
parser = argparse.ArgumentParser(description="Convert kaldi data folder to manifest.json")
parser.add_argument(
"--data_dir", required=True, type=str, help="data in kaldi format",
)
parser.add_argument(
"--manifest", required=True, type=str, help="path to store the manifest file",
)
parser.add_argument(
"--with_aux_data",
default=False,
action="store_true",
help="whether to include auxiliary data in the manifest",
)
args = parser.parse_args()
kaldi_folder = args.data_dir
required_data = {
"audio_filepath": os.path.join(kaldi_folder, "wav.scp"),
"duration": os.path.join(kaldi_folder, "segments"),
"text": os.path.join(kaldi_folder, "text"),
}
aux_data = {
"speaker": os.path.join(kaldi_folder, "utt2spk"),
"gender": os.path.join(kaldi_folder, "utt2gender"),
}
output_names = list(required_data.keys())
# check if required files exist
for name, file in required_data.items():
if not os.path.exists(file):
raise ValueError(f"{os.path.basename(file)} is not in {kaldi_folder}.")
# read wav.scp
wavscp = pd.read_csv(required_data["audio_filepath"], sep=" ", header=None)
if wavscp.shape[1] > 2:
logging.warning(
f"""More than two columns in 'wav.scp': {wavscp.shape[1]}.
Maybe it contains pipes? Pipe processing can be slow at runtime."""
)
wavscp = pd.read_csv(
required_data["audio_filepath"],
sep="^([^ ]+) ",
engine="python",
header=None,
usecols=[1, 2],
names=["wav_label", "audio_filepath"],
)
else:
wavscp = wavscp.rename(columns={0: "wav_label", 1: "audio_filepath"})
# read text
text = pd.read_csv(
required_data["text"], sep="^([^ ]+) ", engine="python", header=None, usecols=[1, 2], names=["label", "text"],
)
# read segments
segments = pd.read_csv(
required_data["duration"], sep=" ", header=None, names=["label", "wav_label", "offset", "end"],
)
# add offset if needed
if len(segments.offset) > len(segments.offset[segments.offset == 0.0]):
logging.info("Adding offset field.")
output_names.insert(2, "offset")
segments["duration"] = (segments.end - segments.offset).round(decimals=3)
# merge data
wav_segments_text = pd.merge(
pd.merge(segments, wavscp, how="inner", on="wav_label"), text, how="inner", on="label",
)
if args.with_aux_data:
# check if auxiliary data is present
for name, aux_file in aux_data.items():
if os.path.exists(aux_file):
logging.info(f"Adding info from '{os.path.basename(aux_file)}'.")
wav_segments_text = pd.merge(
wav_segments_text,
pd.read_csv(aux_file, sep=" ", header=None, names=["label", name]),
how="left",
on="label",
)
output_names.append(name)
else:
logging.info(f"'{os.path.basename(aux_file)}' does not exist. Skipping ...")
# write data to .json
entries = wav_segments_text[output_names].to_dict(orient="records")
with open(args.manifest, "w", encoding="utf-8") as fout:
for m in entries:
fout.write(json.dumps(m, ensure_ascii=False) + "\n")
if __name__ == "__main__":
main()
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