Kaldi ark readers for tensorflow

Introduction

This project aims to enable reading kaldi ark files into tensorflow. It adds following operators to tensorflow:

• read_kaldi_matrix(rspecific)
• decode_kaldi_matrix(data, element-type)
• read_kaldi_post_and_ali(rspecific, is_reading_post(bool))
• decode_kaldi_ali(data, element-type, is_reading_post(bool))

For kaldi matrix, only float matrix are supported. Please pass "tf.float32" in through the element-type argument.

For compressed kaldi matrix, only compression method 2 (kSpeechFeature) is supported.

For alignment, user need to specify weather is reading posteriors or pdfs by the argument is_reading_post. Operator decode_kaldi_ali produces alignment pdfs output, in format of a one dimension int32 tensor (a int32 tensor). Please pass "tf.int32" in through the element-type argument.

Contributions are welcome. Feel free to fork and send pull request or to create issues.

Build

Prerequisitions:

• Linux
• GCC version > 5.1 (for use of c++11 regex)
• python with tensorflow installed

Steps:

1. git clone
2. cd kaldi-reader-standalone
3. mkdir build && cd build
4. cmake .. -DPYTHONBIN=/path/to/your/correct/version/of/python
5. make

Then you are all set.

Usage example

    kaldi_module = kaldi_module = tf.load_op_library("/path/to/this/project/libkaldi_readers.so")
    feats_raw_value = kaldi_module.read_kaldi_matrix("/path/to/somearks/file1.ark:2321")
    feats_value = kaldi_module.decode_kaldi_matrix(feats_raw_value, tf.float32)
    feats_value.set_shape([None, num_dim])

There are some examples under the example directory. To run them, please modify the library path (in contents of the python files) to the correct path.

1. cd example
2. python read-compressed-matrix.py
3. python read-uncompressed-matrix.py
4. python read-post.py
5. python read-ali.py

Author

Fan Ziye

Reference

Kaldi: https://github.com/kaldi-asr/kaldi Tensorflow: https://www.tensorflow.org/extend/adding_an_op