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使用此库可以用作“ kaldi I / O C ++ API”，可以读写“ .ark， .scp”格式，还可以使用kaldi :: [Matrix | Vector | ..]
编译要求：
cmake> = 3.0
数学库：mkl（推荐），安装conda，并使用它来安装mkl：（conda install mkl默认情况下，mkl与conda一起安装），
当cmake时，conda应该确保已经安装好，
cd kaldi-io
mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX=.. .. # set install prefix as '../kaldi-io'
make
make install
结果：
kaldi-io / lib：
libkaldi_io_static.a
libkaldi_io_shared.so
kaldi-io /包括：
kaldi-io.h（标头，可以根据需要进行修改）
子标题目录...
其他：
数学依赖关系是通过cmake从系统路径自动解决的find_package（cmake / Modules / FindBLAS.cmake）
首选MKL / ATLAS / Accelerate（osx）
如果要设置特定的数学库：
＃确保构建目录是干净的
cmake -DBLAS_VENDORS = [ ATLAS | MKL | OPEN | ..] ..
＃另外，可以设置自定义数学库搜索路径，例如：
cmake -DBLAS_VENDORS = ATLAS -DBLAS_ATLAS_LIB_DIRS = ... / atlas / build / lib ..
cmake -DBLAS_VENDORS = MKL -DBLAS_MKL_LIB_DIRS = / opt / intel / mkl / lib / intel64 ..
可以通过kaldi_io.py读取kaldi特征
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import division
import numpy as np
import sys, os, re, gzip, struct
# Adding kaldi tools to shell path,
# Select kaldi,
if not 'KALDI_ROOT' in os.environ:
# Default! To change run python with 'export KALDI_ROOT=/some_dir python'
os.environ['KALDI_ROOT']='/mnt/matylda5/iveselyk/Tools/kaldi-trunk'
# Add kaldi tools to path,
path = os.popen('echo $KALDI_ROOT/src/bin:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/src/fstbin/:$KALDI_ROOT/src/gmmbin/:$KALDI_ROOT/src/featbin/:$KALDI_ROOT/src/lm/:$KALDI_ROOT/src/sgmmbin/:$KALDI_ROOT/src/sgmm2bin/:$KALDI_ROOT/src/fgmmbin/:$KALDI_ROOT/src/latbin/:$KALDI_ROOT/src/nnetbin:$KALDI_ROOT/src/nnet2bin:$KALDI_ROOT/src/nnet3bin:$KALDI_ROOT/src/online2bin/:$KALDI_ROOT/src/ivectorbin/:$KALDI_ROOT/src/lmbin/')
os.environ['PATH'] = path.readline().strip() + ':' + os.environ['PATH']
path.close()
# Define all custom exceptions,
class UnsupportedDataType(Exception): pass
class UnknownVectorHeader(Exception): pass
class UnknownMatrixHeader(Exception): pass
class BadSampleSize(Exception): pass
class BadInputFormat(Exception): pass
class SubprocessFailed(Exception): pass
# Data-type independent helper functions,
def open_or_fd(file, mode='rb'):
""" fd = open_or_fd(file)
Open file, gzipped file, pipe, or forward the file-descriptor.
Eventually seeks in the 'file' argument contains ':offset' suffix.
"""
offset = None
try:
# strip 'ark:' prefix from r{x,w}filename (optional),
if re.search('^(ark|scp)(,scp|,b|,t|,n?f|,n?p|,b?o|,n?s|,n?cs)*:', file):
(prefix,file) = file.split(':',1)
# separate offset from filename (optional),
if re.search(':[0-9]+$', file):
(file,offset) = file.rsplit(':',1)
# input pipe?
if file[-1] == '|':
fd = popen(file[:-1], 'rb') # custom,
# output pipe?
elif file[0] == '|':
fd = popen(file[1:], 'wb') # custom,
# is it gzipped?
elif file.split('.')[-1] == 'gz':
fd = gzip.open(file, mode)
# a normal file...
else:
fd = open(file, mode)
except TypeError:
# 'file' is opened file descriptor,
fd = file
# Eventually seek to offset,
if offset != None: fd.seek(int(offset))
return fd
# based on '/usr/local/lib/python3.6/os.py'
def popen(cmd, mode="rb"):
if not isinstance(cmd, str):
raise TypeError("invalid cmd type (%s, expected string)" % type(cmd))
import subprocess, io, threading
# cleanup function for subprocesses,
def cleanup(proc, cmd):
ret = proc.wait()
if ret > 0:
raise SubprocessFailed('cmd %s returned %d !' % (cmd,ret))
return
# text-mode,
if mode == "r":
proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=sys.stderr)
threading.Thread(target=cleanup,args=(proc,cmd)).start() # clean-up thread,
return io.TextIOWrapper(proc.stdout)
elif mode == "w":
proc = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, stderr=sys.stderr)
threading.Thread(target=cleanup,args=(proc,cmd)).start() # clean-up thread,
return io.TextIOWrapper(proc.stdin)
# binary,
elif mode == "rb":
proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=sys.stderr)
threading.Thread(target=cleanup,args=(proc,cmd)).start() # clean-up thread,
return proc.stdout
elif mode == "wb":
proc = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, stderr=sys.stderr)
threading.Thread(target=cleanup,args=(proc,cmd)).start() # clean-up thread,
return proc.stdin
# sanity,
else:
raise ValueError("invalid mode %s" % mode)
def read_key(fd):
""" [key] = read_key(fd)
Read the utterance-key from the opened ark/stream descriptor 'fd'.
"""
assert('b' in fd.mode), "Error: 'fd' was opened in text mode (in python3 use sys.stdin.buffer)"
key = ''
while 1:
char = fd.read(1).decode("latin1")
if char == '' : break
if char == ' ' : break
key += char
key = key.strip()
if key == '': return None # end of file,
assert(re.match('^\S+$',key) != None) # check format (no whitespace!)
return key
# Integer vectors (alignments, ...),
def read_ali_ark(file_or_fd):
""" Alias to 'read_vec_int_ark()' """
return read_vec_int_ark(file_or_fd)
def read_vec_int_ark(file_or_fd):
""" generator(key,vec) = read_vec_int_ark(file_or_fd)
Create generator of (key,vector<int>) tuples, which reads from the ark file/stream.
file_or_fd : ark, gzipped ark, pipe or opened file descriptor.
Read ark to a 'dictionary':
d = { u:d for u,d in kaldi_io.read_vec_int_ark(file) }
"""
fd = open_or_fd(file_or_fd)
try:
key = read_key(fd)
while key:
ali = read_vec_int(fd)
yield key, ali
key = read_key(fd)
finally:
if fd is not file_or_fd: fd.close()
def read_vec_int(file_or_fd):
""" [int-vec] = read_vec_int(file_or_fd)
Read kaldi integer vector, ascii or binary input,
"""
fd = open_or_fd(file_or_fd)
binary = fd.read(2).decode()
if binary == '\0B': # binary flag
assert(fd.read(1).decode() == '\4'); # int-size
vec_size = np.frombuffer(fd.read(4), dtype='int32', count=1)[0] # vector dim
if vec_size == 0:
return np.array([], dtype='int32')
# Elements from int32 vector are sored in tuples: (sizeof(int32), value),
vec = np.frombuffer(fd.read(vec_size*5), dtype=[('size','int8'),('value','int32')], count=vec_size)
assert(vec[0]['size'] == 4) # int32 size,
ans = vec[:]['value'] # values are in 2nd column,
else: # ascii,
arr = (binary + fd.readline().decode()).strip().split()
try:
arr.remove('['); arr.remove(']') # optionally
except ValueError:
pass
ans = np.array(arr, dtype=int)
if fd is not file_or_fd : fd.close() # cleanup
return ans
# Writing,
def write_vec_int(file_or_fd, v, key=''):
""" write_vec_int(f, v, key='')
Write a binary kaldi integer vector to filename or stream.
Arguments:
file_or_fd : filename or opened file descriptor for writing,
v : the vector to be stored,
key (optional) : used for writing ark-file, the utterance-id gets written before the vector.
Example of writing single vector:
kaldi_io.write_vec_int(filename, vec)
Example of writing arkfile:
with open(ark_file,'w') as f:
for key,vec in dict.iteritems():
kaldi_io.write_vec_flt(f, vec, key=key)
"""
assert(isinstance(v, np.ndarray))
assert(v.dtype == np.int32)
fd = open_or_fd(file_or_fd, mode='wb')
if sys.version_info[0] == 3: assert(fd.mode == 'wb')
try:
if key != '' : fd.write((key+' ').encode("latin1")) # ark-files have keys (utterance-id),
fd.write('\0B'.encode()) # we write binary!
# dim,
fd.write('\4'.encode()) # int32 type,
fd.write(struct.pack(np.dtype('int32').char, v.shape[0]))
# data,
for i in range(len(v)):
fd.write('\4'.encode()) # int32 type,
fd.write(struct.pack(np.dtype('int32').char, v[i])) # binary,
finally:
if fd is not file_or_fd : fd.close()
# Float vectors (confidences, ivectors, ...),
# Reading,
def read_vec_flt_scp(file_or_fd):
""" generator(key,mat) = read_vec_flt_scp(file_or_fd)
Returns generator of (key,vector) tuples, read according to kaldi scp.
file_or_fd : scp, gzipped scp, pipe or opened file descriptor.
Iterate the scp:
for key,vec in kaldi_io.read_vec_flt_scp(file):
...
Read scp to a 'dictionary':
d = { key:mat for key,mat in kaldi_io.read_mat_scp(file) }
"""
fd = open_or_fd(file_or_fd)
try:
for line in fd:
(key,rxfile) = line.decode().split(' ')
vec = read_vec_flt(rxfile)
yield key, vec
finally:
if fd is not file_or_fd : fd.close()
def read_vec_flt_ark(file_or_fd):
""" generator(key,vec) = read_vec_flt_ark(file_or_fd)
Create generator of (key,vector<float>) tuples, reading from an ark file/stream.
file_or_fd : ark, gzipped ark, pipe or opened file descriptor.
Read ark to a 'dictionary':
d = { u:d for u,d in kaldi_io.read_vec_flt_ark(file) }
"""
fd = open_or_fd(file_or_fd)
try:
key = read_key(fd)
while key:
ali = read_vec_flt(fd)
yield key, ali
key = read_key(fd)
finally:
if fd is not file_or_fd : fd.close()
def read_vec_flt(file_or_fd):
""" [flt-vec] = read_vec_flt(file_or_fd)
Read kaldi float vector, ascii or binary input,
"""
fd = open_or_fd(file_or_fd)
binary = fd.read(2).decode()
if binary == '\0B': # binary flag
ans = _read_vec_flt_binary(fd)
else: # ascii,
arr = (binary + fd.readline().decode()).strip().split()
try:
arr.remove('['); arr.remove(']') # optionally
except ValueError:
pass
ans = np.array(arr, dtype=float)
if fd is not file_or_fd : fd.close() # cleanup
return ans
def _read_vec_flt_binary(fd):
header = fd.read(3).decode()
if header == 'FV ' : sample_size = 4 # floats
elif header == 'DV ' : sample_size = 8 # doubles
else : raise UnknownVectorHeader("The header contained '%s'" % header)
assert (sample_size > 0)
# Dimension,
assert (fd.read(1).decode() == '\4'); # int-size
vec_size = np.frombuffer(fd.read(4), dtype='int32', count=1)[0] # vector dim
if vec_size == 0:
return np.array([], dtype='float32')
# Read whole vector,
buf = fd.read(vec_size * sample_size)
if sample_size == 4 : ans = np.frombuffer(buf, dtype='float32')
elif sample_size == 8 : ans = np.frombuffer(buf, dtype='float64')
else : raise BadSampleSize
return ans
# Writing,
def write_vec_flt(file_or_fd, v, key=''):
""" write_vec_flt(f, v, key='')
Write a binary kaldi vector to filename or stream. Supports 32bit and 64bit floats.
Arguments:
file_or_fd : filename or opened file descriptor for writing,
v : the vector to be stored,
key (optional) : used for writing ark-file, the utterance-id gets written before the vector.
Example of writing single vector:
kaldi_io.write_vec_flt(filename, vec)
Example of writing arkfile:
with open(ark_file,'w') as f:
for key,vec in dict.iteritems():
kaldi_io.write_vec_flt(f, vec, key=key)
"""
assert(isinstance(v, np.ndarray))
fd = open_or_fd(file_or_fd, mode='wb')
if sys.version_info[0] == 3: assert(fd.mode == 'wb')
try:
if key != '' : fd.write((key+' ').encode("latin1")) # ark-files have keys (utterance-id),
fd.write('\0B'.encode()) # we write binary!
# Data-type,
if v.dtype == 'float32': fd.write('FV '.encode())
elif v.dtype == 'float64': fd.write('DV '.encode())
else: raise UnsupportedDataType("'%s', please use 'float32' or 'float64'" % v.dtype)
# Dim,
fd.write('\04'.encode())
fd.write(struct.pack(np.dtype('uint32').char, v.shape[0])) # dim
# Data,
fd.write(v.tobytes())
finally:
if fd is not file_or_fd : fd.close()
# Float matrices (features, transformations, ...),
# Reading,
def read_mat_scp(file_or_fd):
""" generator(key,mat) = read_mat_scp(file_or_fd)
Returns generator of (key,matrix) tuples, read according to kaldi scp.
file_or_fd : scp, gzipped scp, pipe or opened file descriptor.
Iterate the scp:
for key,mat in kaldi_io.read_mat_scp(file):
...
Read scp to a 'dictionary':
d = { key:mat for key,mat in kaldi_io.read_mat_scp(file) }
"""
fd = open_or_fd(file_or_fd)
try:
for line in fd:
(key,rxfile) = line.decode().split(' ')
mat = read_mat(rxfile)
yield key, mat
finally:
if fd is not file_or_fd : fd.close()
def read_mat_ark(file_or_fd):
""" generator(key,mat) = read_mat_ark(file_or_fd)
Returns generator of (key,matrix) tuples, read from ark file/stream.
file_or_fd : scp, gzipped scp, pipe or opened file descriptor.
Iterate the ark:
for key,mat in kaldi_io.read_mat_ark(file):
...
Read ark to a 'dictionary':
d = { key:mat for key,mat in kaldi_io.read_mat_ark(file) }
"""
fd = open_or_fd(file_or_fd)
try:
key = read_key(fd)
while key:
mat = read_mat(fd)
yield key, mat
key = read_key(fd)
finally:
if fd is not file_or_fd : fd.close()
def read_mat(file_or_fd):
""" [mat] = read_mat(file_or_fd)
Reads single kaldi matrix, supports ascii and binary.
file_or_fd : file, gzipped file, pipe or opened file descriptor.
"""
fd = open_or_fd(file_or_fd)
try:
binary = fd.read(2).decode()
if binary == '\0B' :
mat = _read_mat_binary(fd)
else:
assert(binary == ' [')
mat = _read_mat_ascii(fd)
finally:
if fd is not file_or_fd: fd.close()
return mat
def _read_mat_binary(fd):
# Data type
header = fd.read(3).decode()
# 'CM', 'CM2', 'CM3' are possible values,
if header.startswith('CM'): return _read_compressed_mat(fd, header)
elif header == 'FM ': sample_size = 4 # floats
elif header == 'DM ': sample_size = 8 # doubles
else: raise UnknownMatrixHeader("The header contained '%s'" % header)
assert(sample_size > 0)
# Dimensions
s1, rows, s2, cols = np.frombuffer(fd.read(10), dtype='int8,int32,int8,int32', count=1)[0]
# Read whole matrix
buf = fd.read(rows * cols * sample_size)
if sample_size == 4 : vec = np.frombuffer(buf, dtype='float32')
elif sample_size == 8 : vec = np.frombuffer(buf, dtype='float64')
else : raise BadSampleSize
mat = np.reshape(vec,(rows,cols))
return mat
def _read_mat_ascii(fd):
rows = []
while 1:
line = fd.readline().decode()
if (len(line) == 0) : raise BadInputFormat # eof, should not happen!
if len(line.strip()) == 0 : continue # skip empty line
arr = line.strip().split()
if arr[-1] != ']':
rows.append(np.array(arr,dtype='float32')) # not last line
else:
rows.append(np.array(arr[:-1],dtype='float32')) # last line
mat = np.vstack(rows)
return mat
def _read_compressed_mat(fd, format):
""" Read a compressed matrix,
see: https://github.com/kaldi-asr/kaldi/blob/master/src/matrix/compressed-matrix.h
methods: CompressedMatrix::Read(...), CompressedMatrix::CopyToMat(...),
"""
assert(format == 'CM ') # The formats CM2, CM3 are not supported...
# Format of header 'struct',
global_header = np.dtype([('minvalue','float32'),('range','float32'),('num_rows','int32'),('num_cols','int32')]) # member '.format' is not written,
per_col_header = np.dtype([('percentile_0','uint16'),('percentile_25','uint16'),('percentile_75','uint16'),('percentile_100','uint16')])
# Read global header,
globmin, globrange, rows, cols = np.frombuffer(fd.read(16), dtype=global_header, count=1)[0]
# The data is structed as [Colheader, ... , Colheader, Data, Data , .... ]
# { cols }{ size }
col_headers = np.frombuffer(fd.read(cols*8), dtype=per_col_header, count=cols)
col_headers = np.array([np.array([x for x in y]) * globrange * 1.52590218966964e-05 + globmin for y in col_headers], dtype=np.float32)
data = np.reshape(np.frombuffer(fd.read(cols*rows), dtype='uint8', count=cols*rows), newshape=(cols,rows)) # stored as col-major,
mat = np.zeros((cols,rows), dtype='float32')
p0 = col_headers[:, 0].reshape(-1, 1)
p25 = col_headers[:, 1].reshape(-1, 1)
p75 = col_headers[:, 2].reshape(-1, 1)
p100 = col_headers[:, 3].reshape(-1, 1)
mask_0_64 = (data <= 64)
mask_193_255 = (data > 192)
mask_65_192 = (~(mask_0_64 | mask_193_255))
mat += (p0 + (p25 - p0) / 64. * data) * mask_0_64.astype(np.float32)
mat += (p25 + (p75 - p25) / 128. * (data - 64)) * mask_65_192.astype(np.float32)
mat += (p75 + (p100 - p75) / 63. * (data - 192)) * mask_193_255.astype(np.float32)
return mat.T # transpose! col-major -> row-major,
# Writing,
def write_mat(file_or_fd, m, key=''):
""" write_mat(f, m, key='')
Write a binary kaldi matrix to filename or stream. Supports 32bit and 64bit floats.
Arguments:
file_or_fd : filename of opened file descriptor for writing,
m : the matrix to be stored,
key (optional) : used for writing ark-file, the utterance-id gets written before the matrix.
Example of writing single matrix:
kaldi_io.write_mat(filename, mat)
Example of writing arkfile:
with open(ark_file,'w') as f:
for key,mat in dict.iteritems():
kaldi_io.write_mat(f, mat, key=key)
"""
assert(isinstance(m, np.ndarray))
assert(len(m.shape) == 2), "'m' has to be 2d matrix!"
fd = open_or_fd(file_or_fd, mode='wb')
if sys.version_info[0] == 3: assert(fd.mode == 'wb')
try:
if key != '' : fd.write((key+' ').encode("latin1")) # ark-files have keys (utterance-id),
fd.write('\0B'.encode()) # we write binary!
# Data-type,
if m.dtype == 'float32': fd.write('FM '.encode())
elif m.dtype == 'float64': fd.write('DM '.encode())
else: raise UnsupportedDataType("'%s', please use 'float32' or 'float64'" % m.dtype)
# Dims,
fd.write('\04'.encode())
fd.write(struct.pack(np.dtype('uint32').char, m.shape[0])) # rows
fd.write('\04'.encode())
fd.write(struct.pack(np.dtype('uint32').char, m.shape[1])) # cols
# Data,
fd.write(m.tobytes())
finally:
if fd is not file_or_fd : fd.close()
# 'Posterior' kaldi type (posteriors, confusion network, nnet1 training targets, ...)
# Corresponds to: vector<vector<tuple<int,float> > >
# - outer vector: time axis
# - inner vector: records at the time
# - tuple: int = index, float = value
#
def read_cnet_ark(file_or_fd):
""" Alias of function 'read_post_ark()', 'cnet' = confusion network """
return read_post_ark(file_or_fd)
def read_post_rxspec(file_):
""" adaptor to read both 'ark:...' and 'scp:...' inputs of posteriors,
"""
if file_.startswith("ark:"):
return read_post_ark(file_)
elif file_.startswith("scp:"):
return read_post_scp(file_)
else:
print("unsupported intput type: %s" % file_)
print("it should begint with 'ark:' or 'scp:'")
sys.exit(1)
def read_post_scp(file_or_fd):
""" generator(key,post) = read_post_scp(file_or_fd)
Returns generator of (key,post) tuples, read according to kaldi scp.
file_or_fd : scp, gzipped scp, pipe or opened file descriptor.
Iterate the scp:
for key,post in kaldi_io.read_post_scp(file):
...
Read scp to a 'dictionary':
d = { key:post for key,post in kaldi_io.read_post_scp(file) }
"""
fd = open_or_fd(file_or_fd)
try:
for line in fd:
(key,rxfile) = line.decode().split(' ')
post = read_post(rxfile)
yield key, post
finally:
if fd is not file_or_fd : fd.close()
def read_post_ark(file_or_fd):
""" generator(key,vec<vec<int,float>>) = read_post_ark(file)
Returns generator of (key,posterior) tuples, read from ark file.
file_or_fd : ark, gzipped ark, pipe or opened file descriptor.
Iterate the ark:
for key,post in kaldi_io.read_post_ark(file):
...
Read ark to a 'dictionary':
d = { key:post for key,post in kaldi_io.read_post_ark(file) }
"""
fd = open_or_fd(file_or_fd)
try:
key = read_key(fd)
while key:
post = read_post(fd)
yield key, post
key = read_key(fd)
finally:
if fd is not file_or_fd: fd.close()
def read_post(file_or_fd):
""" [post] = read_post(file_or_fd)
Reads single kaldi 'Posterior' in binary format.
The 'Posterior' is C++ type 'vector<vector<tuple<int,float> > >',
the outer-vector is usually time axis, inner-vector are the records
at given time, and the tuple is composed of an 'index' (integer)
and a 'float-value'. The 'float-value' can represent a probability
or any other numeric value.
Returns vector of vectors of tuples.
"""
fd = open_or_fd(file_or_fd)
ans=[]
binary = fd.read(2).decode(); assert(binary == '\0B'); # binary flag
assert(fd.read(1).decode() == '\4'); # int-size
outer_vec_size = np.frombuffer(fd.read(4), dtype='int32', count=1)[0] # number of frames (or bins)
# Loop over 'outer-vector',
for i in range(outer_vec_size):
assert(fd.read(1).decode() == '\4'); # int-size
inner_vec_size = np.frombuffer(fd.read(4), dtype='int32', count=1)[0] # number of records for frame (or bin)
data = np.frombuffer(fd.read(inner_vec_size*10), dtype=[('size_idx','int8'),('idx','int32'),('size_post','int8'),('post','float32')], count=inner_vec_size)
assert(data[0]['size_idx'] == 4)
assert(data[0]['size_post'] == 4)
ans.append(data[['idx','post']].tolist())
if fd is not file_or_fd: fd.close()
return ans
# Kaldi Confusion Network bin begin/end times,
# (kaldi stores CNs time info separately from the Posterior).
#
def read_cntime_ark(file_or_fd):
""" generator(key,vec<tuple<float,float>>) = read_cntime_ark(file_or_fd)
Returns generator of (key,cntime) tuples, read from ark file.
file_or_fd : file, gzipped file, pipe or opened file descriptor.
Iterate the ark:
for key,time in kaldi_io.read_cntime_ark(file):
...
Read ark to a 'dictionary':
d = { key:time for key,time in kaldi_io.read_post_ark(file) }
"""
fd = open_or_fd(file_or_fd)
try:
key = read_key(fd)
while key:
cntime = read_cntime(fd)
yield key, cntime
key = read_key(fd)
finally:
if fd is not file_or_fd : fd.close()
def read_cntime(file_or_fd):
""" [cntime] = read_cntime(file_or_fd)
Reads single kaldi 'Confusion Network time info', in binary format:
C++ type: vector<tuple<float,float> >.
(begin/end times of bins at the confusion network).
Binary layout is '<num-bins> <beg1> <end1> <beg2> <end2> ...'
file_or_fd : file, gzipped file, pipe or opened file descriptor.
Returns vector of tuples.
"""
fd = open_or_fd(file_or_fd)
binary = fd.read(2).decode(); assert(binary == '\0B'); # assuming it's binary
assert(fd.read(1).decode() == '\4'); # int-size
vec_size = np.frombuffer(fd.read(4), dtype='int32', count=1)[0] # number of frames (or bins)
data = np.frombuffer(fd.read(vec_size*10), dtype=[('size_beg','int8'),('t_beg','float32'),('size_end','int8'),('t_end','float32')], count=vec_size)
assert(data[0]['size_beg'] == 4)
assert(data[0]['size_end'] == 4)
ans = data[['t_beg','t_end']].tolist() # Return vector of tuples (t_beg,t_end),
if fd is not file_or_fd : fd.close()
return ans
# Segments related,
#
# Segments as 'Bool vectors' can be handy,
# - for 'superposing' the segmentations,
# - for frame-selection in Speaker-ID experiments,
def read_segments_as_bool_vec(segments_file):
""" [ bool_vec ] = read_segments_as_bool_vec(segments_file)
using kaldi 'segments' file for 1 wav, format : '<utt> <rec> <t-beg> <t-end>'
- t-beg, t-end is in seconds,
- assumed 100 frames/second,
"""
segs = np.loadtxt(segments_file, dtype='object,object,f,f', ndmin=1)
# Sanity checks,
assert(len(segs) > 0) # empty segmentation is an error,
assert(len(np.unique([rec[1] for rec in segs ])) == 1) # segments with only 1 wav-file,
# Convert time to frame-indexes,
start = np.rint([100 * rec[2] for rec in segs]).astype(int)
end = np.rint([100 * rec[3] for rec in segs]).astype(int)
# Taken from 'read_lab_to_bool_vec', htk.py,
frms = np.repeat(np.r_[np.tile([False,True], len(end)), False],
np.r_[np.c_[start - np.r_[0, end[:-1]], end-start].flat, 0])
assert np.sum(end-start) == np.sum(frms)
return frms
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原文链接：https://blog.csdn.net/nwnu_908/article/details/117354174