Source code for torchaudio.kaldi_io
# To use this file, the dependency (https://github.com/vesis84/kaldi-io-for-python)
# needs to be installed. This is a light wrapper around kaldi_io that returns
# torch.Tensors.
import torch
from torchaudio.common_utils import IMPORT_KALDI_IO, IMPORT_NUMPY
if IMPORT_NUMPY:
import numpy as np
if IMPORT_KALDI_IO:
import kaldi_io
__all__ = [
'read_vec_int_ark',
'read_vec_flt_scp',
'read_vec_flt_ark',
'read_mat_scp',
'read_mat_ark',
]
def _convert_method_output_to_tensor(file_or_fd, fn, convert_contiguous=False):
r"""Takes a method invokes it. The output is converted to a tensor.
Args:
file_or_fd (str/FileDescriptor): File name or file descriptor
fn (Callable[[...], Generator[str, numpy.ndarray]]): Function that has the signature (
file name/descriptor) -> Generator(str, numpy.ndarray) and converts it to (
file name/descriptor) -> Generator(str, torch.Tensor).
convert_contiguous (bool): Determines whether the array should be converted into a
contiguous layout. (Default: ``None``)
Returns:
Generator[str, torch.Tensor]: The string is the key and the tensor is vec/mat
"""
if not IMPORT_KALDI_IO:
raise ImportError('Could not import kaldi_io. Did you install it?')
for key, np_arr in fn(file_or_fd):
if convert_contiguous:
np_arr = np.ascontiguousarray(np_arr)
yield key, torch.from_numpy(np_arr)
[docs]def read_vec_int_ark(file_or_fd):
r"""Create generator of (key,vector<int>) tuples, which reads from the ark file/stream.
Args:
file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor
Returns:
Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file
Example
>>> # read ark to a 'dictionary'
>>> d = { u:d for u,d in torchaudio.kaldi_io.read_vec_int_ark(file) }
"""
# Requires convert_contiguous to be True because elements from int32 vector are
# sored in tuples: (sizeof(int32), value) so strides are (5,) instead of (4,) which will throw an error
# in from_numpy as it expects strides to be a multiple of 4 (int32).
return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True)
[docs]def read_vec_flt_scp(file_or_fd):
r"""Create generator of (key,vector<float32/float64>) tuples, read according to Kaldi scp.
Args:
file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor
Returns:
Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file
Example
>>> # read scp to a 'dictionary'
>>> # d = { u:d for u,d in torchaudio.kaldi_io.read_vec_flt_scp(file) }
"""
return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_scp)
[docs]def read_vec_flt_ark(file_or_fd):
r"""Create generator of (key,vector<float32/float64>) tuples, which reads from the ark file/stream.
Args:
file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor
Returns:
Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file
Example
>>> # read ark to a 'dictionary'
>>> d = { u:d for u,d in torchaudio.kaldi_io.read_vec_flt_ark(file) }
"""
return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_ark)
[docs]def read_mat_scp(file_or_fd):
r"""Create generator of (key,matrix<float32/float64>) tuples, read according to Kaldi scp.
Args:
file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor
Returns:
Generator[str, torch.Tensor]: The string is the key and the tensor is the matrix read from file
Example
>>> # read scp to a 'dictionary'
>>> d = { u:d for u,d in torchaudio.kaldi_io.read_mat_scp(file) }
"""
return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_scp)
[docs]def read_mat_ark(file_or_fd):
r"""Create generator of (key,matrix<float32/float64>) tuples, which reads from the ark file/stream.
Args:
file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor
Returns:
Generator[str, torch.Tensor]: The string is the key and the tensor is the matrix read from file
Example
>>> # read ark to a 'dictionary'
>>> d = { u:d for u,d in torchaudio.kaldi_io.read_mat_ark(file) }
"""
return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_ark)
