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egs/wsj/s5/steps/segmentation/internal/resample_targets.py
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#!/usr/bin/env python
# Copyright 2017 Vimal Manohar
# Apache 2.0
"""
This script reads a Kaldi text archive of matrices from 'targets_in_ark' (e.g.
'-' for standard input), modifies them by subsampling them, and writes the
modified archive to 'targets_out_ark'.
This form of 'subsampling' is similar to taking every n'th frame (specifically:
every n'th row), except that we average over blocks of size 'n' instead of
taking every n'th element.
Thus, this script is similar to the binary 'subsample-feats' except that
it subsamples by averaging.
"""
import argparse
import logging
import numpy as np
import sys
sys.path.insert(0, 'steps')
import libs.common as common_lib
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s [%(pathname)s:%(lineno)s - "
"%(funcName)s - %(levelname)s ] %(message)s")
handler.setFormatter(formatter)
logger.addHandler(handler)
def get_args():
parser = argparse.ArgumentParser(
description="""
This script reads a Kaldi text archive of matrices from 'targets_in_ark' (e.g.
'-' for standard input), modifies them by subsampling them, and writes the
modified archive to 'targets_out_ark'.
This form of 'subsampling' is similar to taking every n'th frame (specifically:
every n'th row), except that we average over blocks of size 'n' instead of
taking every n'th element.
Thus, this script is similar to the binary 'subsample-feats' except that
it subsamples by averaging.""")
parser.add_argument("--subsampling-factor", type=int, default=1,
help="The sampling rate is scaled by this factor")
parser.add_argument("--verbose", type=int, default=0, choices=[0,1,2],
help="Verbose level")
parser.add_argument("targets_in_ark", type=argparse.FileType('r'),
help="Input targets archive")
parser.add_argument("targets_out_ark", type=argparse.FileType('w'),
help="Output targets archive")
args = parser.parse_args()
if args.subsampling_factor < 1:
raise ValueError("Invalid --subsampling-factor value {0}".format(
args.subsampling_factor))
if args.verbose >= 2:
logger.setLevel(logging.DEBUG)
handler.setLevel(logging.DEBUG)
return args
def run(args):
num_utts = 0
for key, mat in common_lib.read_mat_ark(args.targets_in_ark):
mat = np.matrix(mat)
if args.subsampling_factor > 0:
num_indexes = ((mat.shape[0] + args.subsampling_factor - 1)
/ args.subsampling_factor)
out_mat = np.zeros([num_indexes, mat.shape[1]])
i = 0
for k in range(int(args.subsampling_factor / 2.0),
mat.shape[0], args.subsampling_factor):
st = int(k - float(args.subsampling_factor) / 2.0)
end = int(k + float(args.subsampling_factor) / 2.0)
if st < 0:
st = 0
if end > mat.shape[0]:
end = mat.shape[0]
try:
out_mat[i, :] = np.sum(mat[st:end, :], axis=0) / float(end - st)
except IndexError:
logger.error("mat.shape = {0}, st = {1}, end = {2}"
"".format(mat.shape, st, end))
raise
assert i == k / args.subsampling_factor
i += 1
common_lib.write_matrix_ascii(args.targets_out_ark, out_mat, key=key)
num_utts += 1
args.targets_in_ark.close()
args.targets_out_ark.close()
logger.info("Sub-sampled {num_utts} target matrices"
"".format(num_utts=num_utts))
def main():
args = get_args()
try:
run(args)
except Exception as e:
logger.error("Script failed; traceback = ", exc_info=True)
raise SystemExit(1)
finally:
for f in [args.targets_in_ark, args.targets_out_ark]:
if f is not None:
f.close()
if __name__ == "__main__":
main()
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