b2txt25/language_model/wenet/dataset/kaldi_io.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright 2014-2016  Brno University of Technology (author: Karel Vesely)
# Licensed under the Apache License, Version 2.0 (the "License")

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,
os.environ['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/').readline().strip() + ':' + os.environ['PATH']


#################################################
# 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/python3.4/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)
    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)
    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)
    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)
    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'.
  """
  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_scp(file_or_fd):
  """ generator(key,vec) = read_vec_int_scp(file_or_fd)
   Returns generator of (key,vector<int>) 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_int_scp(file):
     ...

   Read scp to a 'dictionary':
   d = { key:vec for key,mat in kaldi_io.read_vec_int_scp(file) }
  """
  fd = open_or_fd(file_or_fd)
  try:
    for line in fd:
      (key,rxfile) = line.decode().split(' ')
      vec = read_vec_int(rxfile)
      yield key, vec
  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
    # 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)
  """
  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
    # Data type,
    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
    # 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
  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

# 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)
  """
  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')])

  # Mapping for percentiles in col-headers,
  def uint16_to_float(value, min, range):
    return np.float32(min + range * 1.52590218966964e-05 * value)

  # Mapping for matrix elements,
  def uint8_to_float_v2(vec, p0, p25, p75, p100):
    # Split the vector by masks,
    mask_0_64 = (vec <= 64);
    mask_193_255 = (vec > 192);
    mask_65_192 = (~(mask_0_64 | mask_193_255));
    # Sanity check (useful but slow...),
    # assert(len(vec) == np.sum(np.hstack([mask_0_64,mask_65_192,mask_193_255])))
    # assert(len(vec) == np.sum(np.any([mask_0_64,mask_65_192,mask_193_255], axis=0)))
    # Build the float vector,
    ans = np.empty(len(vec), dtype='float32')
    ans[mask_0_64] = p0 + (p25 - p0) / 64. * vec[mask_0_64]
    ans[mask_65_192] = p25 + (p75 - p25) / 128. * (vec[mask_65_192] - 64)
    ans[mask_193_255] = p75 + (p100 - p75) / 63. * (vec[mask_193_255] - 192)
    return ans

  # 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)
  data = np.reshape(np.frombuffer(fd.read(cols*rows), dtype='uint8', count=cols*rows), newshape=(cols,rows)) # stored as col-major,

  mat = np.empty((cols,rows), dtype='float32')
  for i, col_header in enumerate(col_headers):
    col_header_flt = [ uint16_to_float(percentile, globmin, globrange) for percentile in col_header ]
    mat[i] = uint8_to_float_v2(data[i], *col_header_flt)

  return mat.T # transpose! col-major -> row-major,

def write_ark_scp(key, mat, ark_fout, scp_out):
  mat_offset = write_mat(ark_fout, mat, key)
  scp_line = '{}\t{}:{}'.format(key, ark_fout.name, mat_offset)
  scp_out.write(scp_line)
  scp_out.write('\n')

# 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)
  """
  mat_offset = 0
  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),
    mat_offset = fd.tell()
    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()
  return mat_offset

#################################################
# '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_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