NeuralSeq/modules/parallel_wavegan/utils/utils.py

# -*- coding: utf-8 -*-

# Copyright 2019 Tomoki Hayashi
#  MIT License (https://opensource.org/licenses/MIT)

"""Utility functions."""

import fnmatch
import logging
import os
import sys

import h5py
import numpy as np


def find_files(root_dir, query="*.wav", include_root_dir=True):
    """Find files recursively.

    Args:
        root_dir (str): Root root_dir to find.
        query (str): Query to find.
        include_root_dir (bool): If False, root_dir name is not included.

    Returns:
        list: List of found filenames.

    """
    files = []
    for root, dirnames, filenames in os.walk(root_dir, followlinks=True):
        for filename in fnmatch.filter(filenames, query):
            files.append(os.path.join(root, filename))
    if not include_root_dir:
        files = [file_.replace(root_dir + "/", "") for file_ in files]

    return files


def read_hdf5(hdf5_name, hdf5_path):
    """Read hdf5 dataset.

    Args:
        hdf5_name (str): Filename of hdf5 file.
        hdf5_path (str): Dataset name in hdf5 file.

    Return:
        any: Dataset values.

    """
    if not os.path.exists(hdf5_name):
        logging.error(f"There is no such a hdf5 file ({hdf5_name}).")
        sys.exit(1)

    hdf5_file = h5py.File(hdf5_name, "r")

    if hdf5_path not in hdf5_file:
        logging.error(f"There is no such a data in hdf5 file. ({hdf5_path})")
        sys.exit(1)

    hdf5_data = hdf5_file[hdf5_path][()]
    hdf5_file.close()

    return hdf5_data


def write_hdf5(hdf5_name, hdf5_path, write_data, is_overwrite=True):
    """Write dataset to hdf5.

    Args:
        hdf5_name (str): Hdf5 dataset filename.
        hdf5_path (str): Dataset path in hdf5.
        write_data (ndarray): Data to write.
        is_overwrite (bool): Whether to overwrite dataset.

    """
    # convert to numpy array
    write_data = np.array(write_data)

    # check folder existence
    folder_name, _ = os.path.split(hdf5_name)
    if not os.path.exists(folder_name) and len(folder_name) != 0:
        os.makedirs(folder_name)

    # check hdf5 existence
    if os.path.exists(hdf5_name):
        # if already exists, open with r+ mode
        hdf5_file = h5py.File(hdf5_name, "r+")
        # check dataset existence
        if hdf5_path in hdf5_file:
            if is_overwrite:
                logging.warning("Dataset in hdf5 file already exists. "
                                "recreate dataset in hdf5.")
                hdf5_file.__delitem__(hdf5_path)
            else:
                logging.error("Dataset in hdf5 file already exists. "
                              "if you want to overwrite, please set is_overwrite = True.")
                hdf5_file.close()
                sys.exit(1)
    else:
        # if not exists, open with w mode
        hdf5_file = h5py.File(hdf5_name, "w")

    # write data to hdf5
    hdf5_file.create_dataset(hdf5_path, data=write_data)
    hdf5_file.flush()
    hdf5_file.close()


class HDF5ScpLoader(object):
    """Loader class for a fests.scp file of hdf5 file.

    Examples:
        key1 /some/path/a.h5:feats
        key2 /some/path/b.h5:feats
        key3 /some/path/c.h5:feats
        key4 /some/path/d.h5:feats
        ...
        >>> loader = HDF5ScpLoader("hdf5.scp")
        >>> array = loader["key1"]

        key1 /some/path/a.h5
        key2 /some/path/b.h5
        key3 /some/path/c.h5
        key4 /some/path/d.h5
        ...
        >>> loader = HDF5ScpLoader("hdf5.scp", "feats")
        >>> array = loader["key1"]

    """

    def __init__(self, feats_scp, default_hdf5_path="feats"):
        """Initialize HDF5 scp loader.

        Args:
            feats_scp (str): Kaldi-style feats.scp file with hdf5 format.
            default_hdf5_path (str): Path in hdf5 file. If the scp contain the info, not used.

        """
        self.default_hdf5_path = default_hdf5_path
        with open(feats_scp) as f:
            lines = [line.replace("\n", "") for line in f.readlines()]
        self.data = {}
        for line in lines:
            key, value = line.split()
            self.data[key] = value

    def get_path(self, key):
        """Get hdf5 file path for a given key."""
        return self.data[key]

    def __getitem__(self, key):
        """Get ndarray for a given key."""
        p = self.data[key]
        if ":" in p:
            return read_hdf5(*p.split(":"))
        else:
            return read_hdf5(p, self.default_hdf5_path)

    def __len__(self):
        """Return the length of the scp file."""
        return len(self.data)

    def __iter__(self):
        """Return the iterator of the scp file."""
        return iter(self.data)

    def keys(self):
        """Return the keys of the scp file."""
        return self.data.keys()