Source code for espnet2.fileio.multi_sound_scp

import collections.abc
from typing import Tuple

import numpy as np
from typeguard import check_argument_types

from espnet2.fileio.read_text import read_multi_columns_text
from espnet2.fileio.sound_scp import soundfile_read


[docs]class MultiSoundScpReader(collections.abc.Mapping): """Reader class for 'wav.scp' containing multiple sounds. This is useful when loading variable numbers of audios for different samples. Examples: wav.scp is a text file that looks like the following: key1 /some/path/a1.wav /another/path/a2.wav /yet/another/path/a3.wav key2 /some/path/b1.wav /another/path/b2.wav key3 /some/path/c1.wav /another/path/c2.wav /yet/another/path/c3.wav key4 /some/path/d1.wav ... >>> reader = SoundScpReader('wav.scp', stack_axis=0) >>> rate, stacked_arrays = reader['key1'] >>> assert stacked_arrays.shape[0] == 3 Note: All audios in each sample must have the same sampling rates. Audios of different lengths in each sample will be right-padded with np.nan to the same length. """ def __init__( self, fname, dtype=None, always_2d: bool = False, stack_axis=0, pad=np.nan ): assert check_argument_types() self.fname = fname self.dtype = dtype self.always_2d = always_2d self.stack_axis = stack_axis self.pad = pad self.data, _ = read_multi_columns_text(fname) def __getitem__(self, key) -> Tuple[int, np.ndarray]: wavs = self.data[key] arrays, prev_rate = [], None for wav in wavs: if self.dtype == "float16": array, rate = soundfile_read( wav, dtype="float32", always_2d=self.always_2d ) array = array.astype(self.dtype) else: array, rate = soundfile_read( wav, dtype=self.dtype, always_2d=self.always_2d ) arrays.append(array) if prev_rate is not None: assert rate == prev_rate, (prev_rate, rate) prev_rate = rate # Returned as scipy.io.wavread's order return rate, self.pad_to_same_length(arrays, pad=self.pad, axis=self.stack_axis)
[docs] def pad_to_same_length(self, arrays, pad=np.nan, axis=0): """Right-pad arrays to the same length. Args: arrays (List[np.ndarray]): List of arrays to pad pad (float): Value to pad axis (int): Axis to pad Returns: np.ndarray: Padded array """ max_length = max([array.shape[axis] for array in arrays]) padded_arrays = [] for array in arrays: if array.shape[axis] < max_length: pad_width = [(0, 0)] * array.ndim pad_width[axis] = (0, max_length - array.shape[axis]) padded_arrays.append( np.pad(array, pad_width, mode="constant", constant_values=pad) ) else: padded_arrays.append(array) return np.stack(padded_arrays, axis=axis)
[docs] def get_path(self, key): return self.data[key]
def __contains__(self, item): return item def __len__(self): return len(self.data) def __iter__(self): return iter(self.data)
[docs] def keys(self): return self.data.keys()