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_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)
