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