def diarization(wavname,ubmname,out_dir):
out = out_dir+'/'
basename = tools.gen_uid(wavname)
sadname = '%s/%s_sad.txt'%(out,basename)
featname = '%s/%s_feat.mfc'%(out,basename)
bicname = '%s/%s_bic.txt'%(out,basename)
clustname = '%s/%s_cluster.txt'%(out,basename)
viterbiname = '%s/%s_viterbi.txt'%(out,basename)
attr = {'audio':wavname,
'mfcc':featname,
'sad':sadname,
'bic':bicname,
'cluster':clustname,
'viterbi':viterbiname}
# SAD
sad.run_sad(attr)
# MFCC
feat.run_mfcc(attr)
# BIC
bic.run_bic(attr,'audioseg')
# CLUSTERING
cluster.run_clustering(attr)
# Pick top clusters
labels, segment_starts,segment_ends = tools.read_segs(attr['cluster'])
top_n = tools.top_n_clusters(labels, segment_starts,segment_ends,n=2)
# Adapt UBM for each cluster.
cluster_gmms = {}
for i in top_n:
cluster = 'C%s'%(str(i))
gmmname = gmm.adapt(attr,cluster,ubmname)
cluster_gmms[cluster] = gmmname
# Resegmentation
hmmname = '%s/%s_hmm.txt'%(out,basename)
resegment.viterbi(attr,cluster_gmms,hmmname)
labs,starts,ends = tools.merge_segs(attr['viterbi'],attr['sad'])
return labs,starts,ends
if __name__=='__main__':
root_dir = './'
out=tools.prepare_root(root_dir)
wavname,ubmname = tools.read_input()
basename = tools.gen_uid(wavname)
attr = tools.gen_attr(out,basename,wavname)
# SAD
sad.run_sad(attr)
# MFCC
feat.run_mfcc(attr)
# BIC
bic.run_bic(attr,'uniform')
# CLUSTERING
cluster.run_clustering(attr)
# Pick top clusters
labels, segment_starts,segment_ends = tools.read_segs(attr['cluster'])
n_spkrs = 4
top_n = tools.top_n_clusters(labels, segment_starts,segment_ends,n_spkrs)
# Adapt UBM for each cluster.
cluster_gmms = {}
for i in top_n:
cluster = 'C%s'%(str(i))
gmmname = gmm.adapt(attr,cluster,ubmname)
'audio': wavname,
'mfcc': featname,
'sad': sadname,
'bic': bicname,
'cluster': clustname,
'viterbi': viterbiname
}
# SAD
sad.run_sad(attr)
# MFCC
feat.run_mfcc(attr)
# BIC
bic.run_bic(attr)
# CLUSTERING
cluster.run_clustering(attr)
# Pick top clusters
labels, segment_starts, segment_ends = tools.read_segs(attr['cluster'])
top_n = tools.top_n_clusters(labels, segment_starts, segment_ends, n=2)
# Adapt UBM for each cluster.
cluster_gmms = {}
for i in top_n:
cluster = 'C%s' % (str(i))
gmmname = gmm.adapt(attr, cluster, ubmname)
cluster_gmms[cluster] = gmmname