prepare_sad_graph.py
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#!/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()