Hykilpikonna
127 lines
4.4 KiB
Python
Executable File
127 lines
4.4 KiB
Python
Executable File
#!/Volumes/macWorkspace/CS/SpeechGenderAnalysis/venv8/bin/python3.8
|
|
# encoding: utf-8
|
|
|
|
# The MIT License (MIT)
|
|
|
|
# Copyright (c) 2014 CNRS
|
|
|
|
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
|
# of this software and associated documentation files (the "Software"), to deal
|
|
# in the Software without restriction, including without limitation the rights
|
|
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
# copies of the Software, and to permit persons to whom the Software is
|
|
# furnished to do so, subject to the following conditions:
|
|
|
|
# The above copyright notice and this permission notice shall be included in
|
|
# all copies or substantial portions of the Software.
|
|
|
|
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
|
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|
# SOFTWARE.
|
|
|
|
# AUTHORS
|
|
# Hervé BREDIN - http://herve.niderb.fr
|
|
|
|
"""
|
|
Hidden Markov Model with (constrained) Viterbi decoding
|
|
|
|
Usage:
|
|
hmm train [-g <gaussian>] [-c <covariance>] [-d <duration>] <uris.lst> <references.mdtm> <features.pkl> <model.pkl>
|
|
hmm apply [-d <duration>] [-f <constraint.mdtm>] <model.pkl> <features.pkl> <hypothesis.mdtm>
|
|
hmm -h | --help
|
|
hmm --version
|
|
|
|
Options:
|
|
-g <gaussian> Number of gaussian components [default: 16].
|
|
-c <covariance> Covariance type (diag or full) [default: diag].
|
|
-d <duration> Minimum duration in seconds [default: 0.250].
|
|
-f <constraint.mdtm> Constrain Viterbi decoding to follow this path.
|
|
-h --help Show this screen.
|
|
--version Show version.
|
|
"""
|
|
|
|
from pyannote.algorithms.segmentation.hmm import ViterbiHMM
|
|
from pyannote.parser.util import CoParser
|
|
from pyannote.parser import MDTMParser
|
|
from docopt import docopt
|
|
import pickle
|
|
|
|
|
|
def do_train(
|
|
uris_lst, references_mdtm, features_pkl, model_pkl,
|
|
n_components=16, covariance_type='diag', min_duration=0.250,
|
|
):
|
|
|
|
hmm = ViterbiHMM(
|
|
n_components=n_components, covariance_type=covariance_type,
|
|
random_state=None, thresh=1e-2, min_covar=1e-3, n_iter=10,
|
|
disturb=0.05, sampling=1000, min_duration=min_duration)
|
|
|
|
# iterate over all uris in a synchronous manner
|
|
coParser = CoParser(uris=uris_lst,
|
|
reference=references_mdtm,
|
|
features=features_pkl)
|
|
references, features = coParser.generators('reference', 'features')
|
|
|
|
hmm.fit(references, features)
|
|
|
|
with open(model_pkl, 'wb') as f:
|
|
pickle.dump(hmm, f)
|
|
|
|
|
|
def do_apply(model_pkl, features_pkl, hypothesis_mdtm,
|
|
min_duration=0.250, constraint_mdtm=None):
|
|
|
|
with open(model_pkl, 'rb') as f:
|
|
hmm = pickle.load(f)
|
|
|
|
hmm.min_duration = min_duration
|
|
|
|
with open(features_pkl, 'rb') as f:
|
|
features = pickle.load(f)
|
|
|
|
constraint = None
|
|
if constraint_mdtm:
|
|
constraint = MDTMParser().read(constraint_mdtm)()
|
|
|
|
hypothesis = hmm.apply(features, constraint=constraint)
|
|
|
|
with open(hypothesis_mdtm, 'w') as f:
|
|
MDTMParser().write(hypothesis, f=f)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
arguments = docopt(__doc__, version='Hidden Markov Models 1.0')
|
|
|
|
if arguments['train']:
|
|
|
|
uris_lst = arguments['<uris.lst>']
|
|
references_mdtm = arguments['<references.mdtm>']
|
|
features_pkl = arguments['<features.pkl>']
|
|
model_pkl = arguments['<model.pkl>']
|
|
|
|
n_components = int(arguments['-g'])
|
|
covariance_type = arguments['-c']
|
|
min_duration = float(arguments['-d'])
|
|
|
|
do_train(
|
|
uris_lst, references_mdtm, features_pkl, model_pkl,
|
|
n_components=n_components, covariance_type=covariance_type,
|
|
min_duration=min_duration)
|
|
|
|
elif arguments['apply']:
|
|
|
|
model_pkl = arguments['<model.pkl>']
|
|
features_pkl = arguments['<features.pkl>']
|
|
hypothesis_mdtm = arguments['<hypothesis.mdtm>']
|
|
min_duration = float(arguments['-d'])
|
|
constraint_mdtm = arguments['-f']
|
|
|
|
do_apply(model_pkl, features_pkl, hypothesis_mdtm,
|
|
min_duration=min_duration, constraint_mdtm=constraint_mdtm)
|