{"id":6697,"library":"kaldi-python-io","title":"Kaldi Python IO","description":"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.","status":"active","version":"1.2.2","language":"en","source_language":"en","source_url":"https://github.com/funcwj/kaldi-python-io","tags":["kaldi","speech-recognition","audio","io","data-processing","feature-extraction"],"install":[{"cmd":"pip install kaldi-python-io","lang":"bash","label":"Install stable version"}],"dependencies":[{"reason":"Required for numerical array operations, which are central to handling Kaldi's matrix and vector data.","package":"numpy","optional":false}],"imports":[{"note":"The primary functionality of the library is exposed through the `kaldi_io` module, which contains functions like `read_mat`, `write_vec_flt`, etc.","symbol":"kaldi_io","correct":"import kaldi_io"}],"quickstart":{"code":"import kaldi_io\nimport numpy as np\nimport os\n\n# Define a path for the temporary Kaldi archive file\ntemp_ark_path = \"temp_matrix.ark\"\n\ntry:\n    # 1. Create a NumPy array (e.g., a feature matrix)\n    # Kaldi typically uses float32 for features\n    feature_matrix = np.array([[1.1, 2.2, 3.3],\n                               [4.4, 5.5, 6.6]], dtype=np.float32)\n\n    print(f\"Original matrix:\\n{feature_matrix}\")\n\n    # 2. Write the NumPy array to a Kaldi archive file\n    # The library supports writing directly to a file path or file-like object\n    # 'key' is important for Kaldi archives to identify the data\n    with kaldi_io.open_or_fd(temp_ark_path, 'wb') as f:\n        kaldi_io.write_mat(f, feature_matrix, key='utt1_features')\n\n    print(f\"Successfully wrote matrix to '{temp_ark_path}' with key 'utt1_features'.\")\n\n    # 3. Read the matrix back from the Kaldi archive file\n    # kaldi_io.read_mat_s is a generator for multiple matrices in an ark file\n    read_data_generator = kaldi_io.read_mat_s(temp_ark_path)\n    \n    # Since we wrote only one, we expect one (key, matrix) tuple. Get the first.\n    key, loaded_matrix = next(read_data_generator)\n\n    print(f\"\\nRead back data:\")\n    print(f\"Key: {key}\")\n    print(f\"Matrix:\\n{loaded_matrix}\")\n\n    # Verify if the loaded matrix matches the original\n    assert np.allclose(feature_matrix, loaded_matrix)\n    print(\"\\nVerification successful: Loaded matrix matches original.\")\n\nfinally:\n    # Clean up the temporary file\n    if os.path.exists(temp_ark_path):\n        os.remove(temp_ark_path)\n        print(f\"Cleaned up temporary file: {temp_ark_path}\")","lang":"python","description":"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."},"warnings":[{"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.","message":"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.","severity":"gotcha","affected_versions":"All versions"},{"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.","message":"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.","severity":"gotcha","affected_versions":"All versions"},{"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.","message":"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.","severity":"gotcha","affected_versions":"All versions"}],"env_vars":null,"last_verified":"2026-04-15T00:00:00.000Z","next_check":"2026-07-14T00:00:00.000Z","problems":[]}