Tf Kaldi Io Save

What is tf_kaldi_io?

A python package: provide a custom tensorflow dataset for kaldi io

Python is its wrapper, C++ is its backend implemention. It depends on two things:

Through kaldi-io lib, it is able to:

• direct read from kaldi rspecifier(scp, ark, in text or binary, just as kaldi)
• support multiple feature transforms:
  • delta
  • cmvn
  • splice
  • sampling
• compute fast: kaldi Matrix|Vector with blas math lib is used

Through tensorflow dataset, it is able to:

• shuffling
• batching at frame or utt level
• bucketing with input sequence lengths
• and all other tensorflow native dataset manipulations and features (parellel, prefetch, ..)

usage

There are two python readers:

• KaldiReaderDataset: a python warpper of tf_kaldi_io, read at utterence level, a custom tf dataset

  • be able to read matrix(kaldi feat), int-vector(kaldi label), vector(kaldi vector, ivector e.g.)
  • these matrix/vector/int_vector readers are optional, use what you need
  • other optional arguments (their default values don't change anything) are for kaldi transformation:
    • delta
    • cmvn
    • sampling

  import tensorflow as tf
  from tf_kaldi_io import KaldiReaderDataset
  
  # Create a KaldiReaderDataset and print its elements.
  with tf.Session() as sess:
      kaldi_dataset = KaldiReaderDataset(matrix_rspecifier="ark:matrix.ark",
                                         vector_rspecifier="ark:vector.ark",
                                         int_vector_rspecifier="ark:int_vec.ark",
                                         # delta_order=0,
                                         # norm_means=False, norm_vars=False, global_cmvn_file="test/data/global.cmvn"
                                         # left_context=0, right_context=0,
                                         # num_downsample=1, offset=0,
                                         )
  
      iterator = kaldi_dataset.make_one_shot_iterator()
      next_element = iterator.get_next()
  
      try:
        while True:
          print(sess.run(next_element))
      except tf.errors.OutOfRangeError:
        pass
  

  • If you are familiar with tf dataset api, use KaldiReaderDataset is enough, otherwise KaldiDataset give a dataset warpper with common tf dataset api.

• KaldiDataset: a python warpper of kaldiReaderDataset, read at frame or utt level. Based on tf dataset api, it's able to:

  • shuffle
  • batch
  • dynamic pad
  • bucket with length
  • ...

  import tensorflow as tf
  from tf_kaldi_io import KaldiDataset
  
  with tf.Session() as sess:
      kaldi_dataset = KaldiDataset(matrix_rspecifier="ark:matrix.ark",
                                   vector_rspecifier="ark:vector.ark", 
                                   int_vec_rspecifier="ark:int_vec.ark",
                                   batch_size=1, batch_mode="utt", # batch_mode="frame",
                                   # delta_order=0,
                                   # norm_means=False, norm_vars=False, global_cmvn_file="test/data/global.cmvn"
                                   # left_context=0, right_context=0,
                                   # num_downsample=1, offset=0,
                                   )
  
      iterator = tf.data.Iterator.from_structure(
        kaldi_dataset.dataset.output_types,
        kaldi_dataset.dataset.output_shapes)
  
      next_element = iterator.get_next()
      # next_element: 
      #	in utt mode: (utt_keys, inputs, input_lengths, [targets, target_lengths])
      #   in frame mode: (inputs, [targets])
  
      iterator_init_op = iterator.make_initializer(kaldi_dataset.dataset)
  
      sess.run(iterator_init_op)
  
      try:
        while True:
          print(sess.run(next_element))
      except tf.errors.OutOfRangeError:
        pass
  

Install

1. requirements

• tensorflow >= 1.4
• a blas math lib
  • recommended: MKL
    • install conda (mkl is installed with conda by default, or you can install it by conda install mkl)

2. install

• install locally

  git clone https://github.com/open-speech/tf_kaldi_io.git
  cd tf_kaldi_io
  
  # checkout needed branch, master is with the lastest tf api (current: r1.12)
  git checkout -b [the_branch_as_your_tf_version] origin/[the_branch_as_your_tf_version]
  
  pip install .
  

• or install from pypi (which is the master branch)

  pip --no-cache-dir install tf_kaldi_io
  

3. test

cd test
python test_tf_kaldi_dataset.py # test KaldiDataset: a python class wrapper of custom dataset
python test_tf_kaldi_io.py # test custom dataset: KaldiReaderDataset

More

• inputs is kaldi feature storage format, target is kaldi alignments format(int-vector).
• Only input_rspecifier is required argument, others are optional or have default values(see in tf_kaldi_io.py).
• If use num_downsample in utt mode: just the inputs get sampling, the target will not. It's sensible for sequence traing(CTC).
• There are many tf kaldi io implementions, but with one or more defects:
  • just python - io itself is slow.
  • sequential with training - have to wait io done.
  • just kaldi ark in text or binary - text is big, binary is unreadable.
  • no transformations support - you need prepare many feature varieties for one task.
  • no way to become tensorflow native io(dataset) - no parallel, prefetch, shuffle, bucket, ...
  • depend on TFRecodes(protobuf) - unnecessary(need convert to it then to tensor), and protobuf is a nightmare(version incompatible) everytime we meet.
  • all of above disappointments make tf_kaldi_io appear.

Todo:

• support TFRecord files as output
• examples of making use of tf_kaldi_io to train a TF model. Will be in another repo.