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PaddleSpeech/utils/feat-to-shape.py

83 lines
2.8 KiB

#!/usr/bin/env python3
import argparse
import logging
import sys
from paddlespeech.s2t.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
def get_parser():
parser = argparse.ArgumentParser(
description="convert feature to its shape",
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--verbose", "-V", default=0, type=int, help="Verbose option")
parser.add_argument(
"--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(
"--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(
"out",
nargs="?",
type=argparse.FileType("w"),
default=sys.stdout,
help="The output filename. "
"If omitted, then output to sys.stdout", )
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
# There are no necessary for matrix without preprocessing,
# so change to file_reader_helper to return shape.
# This make sense only with filetype="hdf5".
for utt, mat in file_reader_helper(
args.rspecifier, args.filetype, return_shape=preprocessing is None):
if preprocessing is not None:
if is_scipy_wav_style(mat):
# If data is sound file, then got as Tuple[int, ndarray]
rate, mat = mat
mat = preprocessing(mat, uttid_list=utt)
shape_str = ",".join(map(str, mat.shape))
else:
if len(mat) == 2 and isinstance(mat[1], tuple):
# If data is sound file, Tuple[int, Tuple[int, ...]]
rate, mat = mat
shape_str = ",".join(map(str, mat))
args.out.write("{} {}\n".format(utt, shape_str))
if __name__ == "__main__":
main()