Blame view
egs/wsj/s5/steps/segmentation/internal/prepare_sad_graph.py
6.35 KB
8dcb6dfcb
first commit
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
#!/usr/bin/env python
# Copyright 2016 Vimal Manohar
# Apache 2.0
"""Prepares a graph with a simple HMM topology for segmentation
with minimum and maximum speech duration constraints and minimum silence
duration constraint. The graph is written to the 'output_graph', which
can be file or "-" for stdout.
"""
from __future__ import print_function
import argparse
import logging
import math
import os
import sys
import traceback
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 [%(filename)s:%(lineno)s - "
"%(funcName)s - %(levelname)s ] %(message)s")
handler.setFormatter(formatter)
logger.addHandler(handler)
def get_args():
parser = argparse.ArgumentParser(
description="""This script prepares a graph with a simple HMM topology
for segmentation with minimum and maximum speech duration constraints
and minimum silence duration constraint. The graph is written to the
'output_graph', which can be file or "-" for stdout. for segmentation
with minimum and maximum speech duration constraints and minimum silence
duration constraint.""",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--transition-scale", type=float, default=1.0,
help="""Scale on transition probabilities relative to
LM weights""")
parser.add_argument("--loopscale", type=float, default=0.1,
help="""Scale on self-loop log-probabilities relative
to LM weights""")
parser.add_argument("--min-silence-duration", type=float, default=0.03,
help="""Minimum duration for silence""")
parser.add_argument("--min-speech-duration", type=float, default=0.3,
help="""Minimum duration for speech""")
parser.add_argument("--max-speech-duration", type=float, default=10.0,
help="""Maximum duration for speech""")
parser.add_argument("--frame-shift", type=float, default=0.03,
help="""Frame shift in seconds""")
parser.add_argument("--edge-silence-probability", type=float,
default=0.5,
help="Probability of silence at the edges.")
parser.add_argument("--transition-probability", type=float, default=0.1,
help="Transition probability for silence to speech "
"or vice-versa")
parser.add_argument("output_graph", type=str,
help="Output graph")
args = parser.parse_args()
args.min_states_silence = int(args.min_silence_duration / args.frame_shift
+ 0.5)
args.min_states_speech = int(args.min_speech_duration / args.frame_shift
+ 0.5)
args.max_states_speech = int(args.max_speech_duration / args.frame_shift
+ 0.5)
return args
def print_states(args, file_handle):
# Initial transition to silence
print ("0 1 silence silence {0}".format(-math.log(args.edge_silence_probability)),
file=file_handle)
silence_start_state = 1
# Silence min duration transitions
# 1->2, 2->3 and so on until
# (1 + min_states_silence - 2) -> (1 + min_states_silence - 1) ...
for state in range(silence_start_state,
silence_start_state + args.min_states_silence - 1):
print ("{state} {next_state} silence silence {cost}".format(
state=state, next_state=state + 1, cost=0.0),
file=file_handle)
silence_last_state = silence_start_state + args.min_states_silence - 1
# Silence self-loop
print ("{state} {state} silence silence {cost}".format(
state=silence_last_state, cost=0.0),
file=file_handle)
speech_start_state = silence_last_state + 1
# Initial transition to speech
print ("0 {state} speech speech {cost}".format(
state=speech_start_state,
cost=-math.log(1.0 - args.edge_silence_probability)),
file=file_handle)
# Silence to speech transition
print ("{sil_state} {speech_state} speech speech {cost}".format(
sil_state=silence_last_state,
speech_state=speech_start_state,
cost=-math.log(args.transition_probability)),
file=file_handle)
# Speech min duration
for state in range(speech_start_state,
speech_start_state + args.min_states_speech - 1):
print ("{state} {next_state} speech speech {cost}".format(
state=state, next_state=state + 1, cost=0.0),
file=file_handle)
# Speech max duration
for state in range(speech_start_state + args.min_states_speech - 1,
speech_start_state + args.max_states_speech - 1):
print ("{state} {next_state} speech speech {cost}".format(
state=state, next_state=state + 1, cost=0.0),
file=file_handle)
print ("{state} {sil_state} silence silence {cost}".format(
state=state, sil_state=silence_start_state,
cost=-math.log(args.transition_probability)),
file=file_handle)
speech_last_state = speech_start_state + args.max_states_speech - 1
# Transition to silence after max duration of speech
print ("{state} {sil_state} silence silence {cost}".format(
state=speech_last_state, sil_state=silence_start_state,
cost=0.0),
file=file_handle)
for state in range(1, speech_start_state):
print ("{state} {cost}".format(
state=state, cost=-math.log(args.edge_silence_probability)),
file=file_handle)
for state in range(speech_start_state, speech_last_state + 1):
print ("{state} {cost}".format(
state=state,
cost=-math.log(1.0 - args.edge_silence_probability)),
file=file_handle)
def main():
try:
args = get_args()
with common_lib.smart_open(args.output_graph, 'w') as f:
print_states(args, f)
except Exception:
raise
if __name__ == '__main__':
main()
|