Kaldi Python IO

1.2.2 · active · verified Wed Apr 15

kaldi-python-io is a pure Python library providing an I/O interface for accessing data in the Kaldi speech recognition toolkit's native formats. It allows reading and writing various Kaldi data types such as matrices, vectors, and alignments directly in Python. The current version is 1.2.2, with new releases typically occurring every few months based on development activity.

Warnings

gotcha Ensure you use the correct function for reading Kaldi data based on its format (e.g., `read_mat_s` for iterating through multiple matrices in an ARK file, versus `read_mat` for a single matrix from a stream/file-like object or stdin). Misunderstanding Kaldi's archive/stream conventions can lead to unexpected parsing errors or incomplete data reads.
Fix: Consult the `kaldi-python-io` documentation or source for the specific function signature and expected input type (e.g., file path, file-like object, generator) based on your Kaldi data source.
gotcha Kaldi often expects specific data types, particularly `float32` for acoustic features and vectors. When creating data with NumPy to be written by `kaldi-python-io`, ensure your arrays have the correct `dtype` (e.g., `np.float32`) to maintain compatibility with downstream Kaldi tools.
Fix: Explicitly set `dtype=np.float32` (or `np.float64` if appropriate for your Kaldi setup) when creating NumPy arrays that will be written to Kaldi formats.
gotcha When writing Kaldi archives (`.ark` files) that contain multiple entries, each entry typically requires a unique string `key`. While `kaldi-python-io` handles the writing, other Kaldi tools rely on these keys for indexing and accessing data. Omitting or duplicating keys when writing multiple entries can lead to issues in Kaldi's data processing pipeline.
Fix: Always provide a unique and descriptive string `key` parameter when using `write_mat`, `write_vec_flt`, etc., especially when writing to a stream that will contain multiple entries.

Install

pip install kaldi-python-io Install stable version

Imports

kaldi_io
```
import kaldi_io
```
The primary functionality of the library is exposed through the `kaldi_io` module, which contains functions like `read_mat`, `write_vec_flt`, etc.

Quickstart

This quickstart demonstrates how to create a NumPy matrix, write it to a Kaldi archive (`.ark`) file using `kaldi-python-io`, and then read it back. It includes file cleanup.

import kaldi_io
import numpy as np
import os

# Define a path for the temporary Kaldi archive file
temp_ark_path = "temp_matrix.ark"

try:
    # 1. Create a NumPy array (e.g., a feature matrix)
    # Kaldi typically uses float32 for features
    feature_matrix = np.array([[1.1, 2.2, 3.3],
                               [4.4, 5.5, 6.6]], dtype=np.float32)

    print(f"Original matrix:\n{feature_matrix}")

    # 2. Write the NumPy array to a Kaldi archive file
    # The library supports writing directly to a file path or file-like object
    # 'key' is important for Kaldi archives to identify the data
    with kaldi_io.open_or_fd(temp_ark_path, 'wb') as f:
        kaldi_io.write_mat(f, feature_matrix, key='utt1_features')

    print(f"Successfully wrote matrix to '{temp_ark_path}' with key 'utt1_features'.")

    # 3. Read the matrix back from the Kaldi archive file
    # kaldi_io.read_mat_s is a generator for multiple matrices in an ark file
    read_data_generator = kaldi_io.read_mat_s(temp_ark_path)
    
    # Since we wrote only one, we expect one (key, matrix) tuple. Get the first.
    key, loaded_matrix = next(read_data_generator)

    print(f"\nRead back data:")
    print(f"Key: {key}")
    print(f"Matrix:\n{loaded_matrix}")

    # Verify if the loaded matrix matches the original
    assert np.allclose(feature_matrix, loaded_matrix)
    print("\nVerification successful: Loaded matrix matches original.")

finally:
    # Clean up the temporary file
    if os.path.exists(temp_ark_path):
        os.remove(temp_ark_path)
        print(f"Cleaned up temporary file: {temp_ark_path}")

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