KALDI-IO库的生成与读取
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) 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 ) 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 ...' 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 : ' ' - 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