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data_io.py
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data_io.py
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##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import kaldi_io
import numpy as np
import sys
from scipy.ndimage.interpolation import shift
import time
def load_dataset(fea_scp,fea_opts,lab_folder,lab_opts,left,right, max_sequence_length):
fea= { k:m for k,m in kaldi_io.read_mat_ark('ark:copy-feats scp:'+fea_scp+' ark:- |'+fea_opts) }
lab= { k:v for k,v in kaldi_io.read_vec_int_ark('gunzip -c '+lab_folder+'/ali*.gz | '+lab_opts+' '+lab_folder+'/final.mdl ark:- ark:-|') if k in fea} # Note that I'm copying only the aligments of the loaded fea
fea={k: v for k, v in fea.items() if k in lab} # This way I remove all the features without an aligment (see log file in alidir "Did not Succeded")
end_snt=0
end_index=[]
snt_name=[]
fea_conc=[]
lab_conc=[]
tmp=0
for k in sorted(sorted(fea.keys()), key=lambda k: len(fea[k])):
#####
# If the sequence length is above the threshold, we split it with a minimal length max/4
# If max length = 500, then the split will start at 500 + (500/4) = 625.
# A seq of length 625 will be splitted in one of 500 and one of 125
if(len(fea[k]) > max_sequence_length) and max_sequence_length>0:
fea_chunked = []
lab_chunked = []
for i in range((len(fea[k]) + max_sequence_length - 1) // max_sequence_length):
if(len(fea[k][i * max_sequence_length:]) > max_sequence_length + (max_sequence_length/4)):
fea_chunked.append(fea[k][i * max_sequence_length:(i + 1) * max_sequence_length])
lab_chunked.append(lab[k][i * max_sequence_length:(i + 1) * max_sequence_length])
else:
fea_chunked.append(fea[k][i * max_sequence_length:])
lab_chunked.append(lab[k][i * max_sequence_length:])
break
for j in range(0, len(fea_chunked)):
fea_conc.append(fea_chunked[j])
lab_conc.append(lab_chunked[j])
snt_name.append(k+'_split'+str(j))
else:
fea_conc.append(fea[k])
lab_conc.append(lab[k])
snt_name.append(k)
tmp+=1
fea_zipped = zip(fea_conc,lab_conc)
fea_sorted = sorted(fea_zipped, key=lambda x: x[0].shape[0])
fea_conc,lab_conc = zip(*fea_sorted)
for entry in fea_conc:
end_snt=end_snt+entry.shape[0]
end_index.append(end_snt)
fea_conc=np.concatenate(fea_conc)
lab_conc=np.concatenate(lab_conc)
return [snt_name,fea_conc,lab_conc,np.asarray(end_index)]
def context_window_old(fea,left,right):
N_row=fea.shape[0]
N_fea=fea.shape[1]
frames = np.empty((N_row-left-right, N_fea*(left+right+1)))
for frame_index in range(left,N_row-right):
right_context=fea[frame_index+1:frame_index+right+1].flatten() # right context
left_context=fea[frame_index-left:frame_index].flatten() # left context
current_frame=np.concatenate([left_context,fea[frame_index],right_context])
frames[frame_index-left]=current_frame
return frames
def context_window(fea,left,right):
N_elem=fea.shape[0]
N_fea=fea.shape[1]
fea_conc=np.empty([N_elem,N_fea*(left+right+1)])
index_fea=0
for lag in range(-left,right+1):
fea_conc[:,index_fea:index_fea+fea.shape[1]]=np.roll(fea,lag,axis=0)
index_fea=index_fea+fea.shape[1]
fea_conc=fea_conc[left:fea_conc.shape[0]-right]
return fea_conc
def load_chunk(fea_scp,fea_opts,lab_folder,lab_opts,left,right,max_sequence_length):
# open the file
[data_name,data_set,data_lab,end_index]=load_dataset(fea_scp,fea_opts,lab_folder,lab_opts,left,right, max_sequence_length)
# Context window
if left!=0 or right!=0:
data_set=context_window(data_set,left,right)
end_index=end_index-left
end_index[-1]=end_index[-1]-right
# mean and variance normalization
data_set=(data_set-np.mean(data_set,axis=0))/np.std(data_set,axis=0)
# Label processing
data_lab=data_lab-data_lab.min()
if right>0:
data_lab=data_lab[left:-right]
else:
data_lab=data_lab[left:]
data_set=np.column_stack((data_set, data_lab))
return [data_name,data_set,end_index]
def load_counts(class_counts_file):
with open(class_counts_file) as f:
row = next(f).strip().strip('[]').strip()
counts = np.array([ np.float32(v) for v in row.split() ])
return counts
def read_lab_fea(fea_dict,lab_dict,cw_left_max,cw_right_max,max_seq_length):
fea_index=0
cnt_fea=0
for fea in fea_dict.keys():
# reading the features
fea_scp=fea_dict[fea][1]
fea_opts=fea_dict[fea][2]
cw_left=int(fea_dict[fea][3])
cw_right=int(fea_dict[fea][4])
cnt_lab=0
for lab in lab_dict.keys():
lab_folder=lab_dict[lab][1]
lab_opts=lab_dict[lab][2]
[data_name_fea,data_set_fea,data_end_index_fea]=load_chunk(fea_scp,fea_opts,lab_folder,lab_opts,cw_left,cw_right,max_seq_length)
# making the same dimenion for all the features (compensating for different context windows)
labs_fea=data_set_fea[cw_left_max-cw_left:data_set_fea.shape[0]-(cw_right_max-cw_right),-1]
data_set_fea=data_set_fea[cw_left_max-cw_left:data_set_fea.shape[0]-(cw_right_max-cw_right),0:-1]
data_end_index_fea=data_end_index_fea-(cw_left_max-cw_left)
data_end_index_fea[-1]=data_end_index_fea[-1]-(cw_right_max-cw_right)
if cnt_fea==0 and cnt_lab==0:
data_set=data_set_fea
labs=labs_fea
data_end_index=data_end_index_fea
data_end_index=data_end_index_fea
data_name=data_name_fea
fea_dict[fea].append(fea_index)
fea_index=fea_index+data_set_fea.shape[1]
fea_dict[fea].append(fea_index)
fea_dict[fea].append(fea_dict[fea][6]-fea_dict[fea][5])
else:
if cnt_fea==0:
labs=np.column_stack((labs,labs_fea))
if cnt_lab==0:
data_set=np.column_stack((data_set,data_set_fea))
fea_dict[fea].append(fea_index)
fea_index=fea_index+data_set_fea.shape[1]
fea_dict[fea].append(fea_index)
fea_dict[fea].append(fea_dict[fea][6]-fea_dict[fea][5])
# Checks if lab_names are the same for all the features
if not(data_name==data_name_fea):
sys.stderr.write('ERROR: different sentence ids are detected for the different features. Plase check again input feature lists"\n')
sys.exit(0)
# Checks if end indexes are the same for all the features
if not(data_end_index==data_end_index_fea).all():
sys.stderr.write('ERROR end_index must be the same for all the sentences"\n')
sys.exit(0)
cnt_lab=cnt_lab+1
cnt_fea=cnt_fea+1
cnt_lab=0
for lab in lab_dict.keys():
lab_dict[lab].append(data_set.shape[1]+cnt_lab)
cnt_lab=cnt_lab+1
data_set=np.column_stack((data_set,labs))
return [data_name,data_set,data_end_index]