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