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PaddleSpeech/utils/copy-feats.py

106 lines
3.4 KiB

#!/usr/bin/env python3
import argparse
import logging
from distutils.util import strtobool
from paddlespeech.audio.transform.transformation import Transformation
from paddlespeech.s2t.utils.cli_readers import file_reader_helper
from paddlespeech.s2t.utils.cli_utils import get_commandline_args
from paddlespeech.s2t.utils.cli_utils import is_scipy_wav_style
from paddlespeech.s2t.utils.cli_writers import file_writer_helper
def get_parser():
parser = argparse.ArgumentParser(
description="copy feature with preprocessing",
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--verbose", "-V", default=0, type=int, help="Verbose option")
parser.add_argument(
"--in-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "sound.hdf5", "sound"],
help="Specify the file format for the rspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--out-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "sound.hdf5", "sound"],
help="Specify the file format for the wspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--write-num-frames",
type=str,
help="Specify wspecifer for utt2num_frames")
parser.add_argument(
"--compress",
type=strtobool,
default=False,
help="Save in compressed format")
parser.add_argument(
"--compression-method",
type=int,
default=2,
help="Specify the method(if mat) or "
"gzip-level(if hdf5)", )
parser.add_argument(
"--preprocess-conf",
type=str,
default=None,
help="The configuration file for the pre-processing", )
parser.add_argument(
"rspecifier",
type=str,
help="Read specifier for feats. e.g. ark:some.ark")
parser.add_argument(
"wspecifier", type=str, help="Write specifier. e.g. ark:some.ark")
return parser
def main():
parser = get_parser()
args = parser.parse_args()
# logging info
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
if args.verbose > 0:
logging.basicConfig(level=logging.INFO, format=logfmt)
else:
logging.basicConfig(level=logging.WARN, format=logfmt)
logging.info(get_commandline_args())
if args.preprocess_conf is not None:
preprocessing = Transformation(args.preprocess_conf)
logging.info("Apply preprocessing: {}".format(preprocessing))
else:
preprocessing = None
with file_writer_helper(
args.wspecifier,
filetype=args.out_filetype,
write_num_frames=args.write_num_frames,
compress=args.compress,
compression_method=args.compression_method, ) as writer:
for utt, mat in file_reader_helper(args.rspecifier, args.in_filetype):
if is_scipy_wav_style(mat):
# If data is sound file, then got as Tuple[int, ndarray]
rate, mat = mat
if preprocessing is not None:
mat = preprocessing(mat, uttid_list=utt)
# shape = (Time, Channel)
if args.out_filetype in ["sound.hdf5", "sound"]:
# Write Tuple[int, numpy.ndarray] (scipy style)
writer[utt] = (rate, mat)
else:
writer[utt] = mat
if __name__ == "__main__":
main()