def load(filepath, out=None, normalization=None, num_frames=-1, offset=0):
"""Loads an audio file from disk into a Tensor
Args:
filepath (string): path to audio file
out (Tensor, optional): an output Tensor to use instead of creating one
normalization (bool or number, optional): If boolean `True`, then output is divided by `1 << 31`
(assumes 16-bit depth audio, and normalizes to `[0, 1]`.
If `number`, then output is divided by that number
num_frames (int, optional): number of frames to load. -1 to load everything after the offset.
offset (int, optional): number of frames from the start of the file to begin data loading.
Returns: tuple(Tensor, int)
- Tensor: output Tensor of size `[L x C]` where L is the number of audio frames, C is the number of channels
- int: the sample-rate of the audio (as listed in the metadata of the file)
Example::
>>> data, sample_rate = torchaudio.load('foo.mp3')
>>> print(data.size())
torch.Size([278756, 2])
>>> print(sample_rate)
44100
"""
# check if valid file
if not os.path.isfile(filepath):
raise OSError("{} not found or is a directory".format(filepath))
# initialize output tensor
if out is not None:
check_input(out)
else:
out = torch.FloatTensor()
if num_frames < -1:
raise ValueError(
"Expected value for num_samples -1 (entire file) or >=0")
if offset < 0:
raise ValueError("Expected positive offset value")
sample_rate = _torch_sox.read_audio_file(filepath, out, num_frames, offset)
# normalize if needed
if isinstance(normalization, bool) and normalization:
out /= 1 << 31 # assuming 16-bit depth
elif isinstance(normalization, (float, int)):
out /= normalization # normalize with custom value
return out, sample_rate
def load(filepath, out=None, normalization=None):
"""Loads an audio file from disk into a Tensor
Args:
filepath (string): path to audio file
out (Tensor, optional): an output Tensor to use instead of creating one
normalization (bool or number, optional): If boolean `True`, then output is divided by `1 << 31`
(assumes 16-bit depth audio, and normalizes to `[0, 1]`.
If `number`, then output is divided by that number
Returns: tuple(Tensor, int)
- Tensor: output Tensor of size `[L x C]` where L is the number of audio frames, C is the number of channels
- int: the sample-rate of the audio (as listed in the metadata of the file)
Example::
>>> data, sample_rate = torchaudio.load('foo.mp3')
>>> print(data.size())
torch.Size([278756, 2])
>>> print(sample_rate)
44100
"""
# check if valid file
if not os.path.isfile(filepath):
raise OSError("{} not found or is a directory".format(filepath))
# initialize output tensor
if out is not None:
check_input(out)
else:
out = torch.FloatTensor()
sample_rate = _torch_sox.read_audio_file(filepath, out)
# normalize if needed
if isinstance(normalization, bool) and normalization:
out /= 1 << 31 # assuming 16-bit depth
elif isinstance(normalization, (float, int)):
out /= normalization # normalize with custom value
return out, sample_rate
def load(
filepath,
out=None,
normalization=True,
channels_first=True,
num_frames=0,
offset=0,
signalinfo=None,
encodinginfo=None,
filetype=None,
):
r"""See torchaudio.load"""
# stringify if `pathlib.Path` (noop if already `str`)
filepath = str(filepath)
# check if valid file
if not os.path.isfile(filepath):
raise OSError("{} not found or is a directory".format(filepath))
# initialize output tensor
if out is not None:
torchaudio.check_input(out)
else:
out = torch.FloatTensor()
if num_frames < -1:
raise ValueError(
"Expected value for num_samples -1 (entire file) or >=0")
if offset < 0:
raise ValueError("Expected positive offset value")
import _torch_sox
sample_rate = _torch_sox.read_audio_file(filepath, out, channels_first,
num_frames, offset, signalinfo,
encodinginfo, filetype)
# normalize if needed
torchaudio._audio_normalization(out, normalization)
return out, sample_rate
def load(filepath,
out=None,
normalization=True,
channels_first=True,
num_frames=0,
offset=0,
signalinfo=None,
encodinginfo=None,
filetype=None):
r"""Loads an audio file from disk into a tensor
Args:
filepath (str or pathlib.Path): Path to audio file
out (torch.Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``)
normalization (bool, number, or callable, optional): If boolean `True`, then output is divided by `1 << 31`
(assumes signed 32-bit audio), and normalizes to `[-1, 1]`.
If `number`, then output is divided by that number
If `callable`, then the output is passed as a parameter
to the given function, then the output is divided by
the result. (Default: ``True``)
channels_first (bool): Set channels first or length first in result. (Default: ``True``)
num_frames (int, optional): Number of frames to load. 0 to load everything after the offset.
(Default: ``0``)
offset (int, optional): Number of frames from the start of the file to begin data loading.
(Default: ``0``)
signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the
audio type cannot be automatically determined. (Default: ``None``)
encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the
audio type cannot be automatically determined. (Default: ``None``)
filetype (str, optional): A filetype or extension to be set if sox cannot determine it
automatically. (Default: ``None``)
Returns:
Tuple[torch.Tensor, int]: An output tensor of size `[C x L]` or `[L x C]` where L is the number
of audio frames and C is the number of channels. An integer which is the sample rate of the
audio (as listed in the metadata of the file)
Example
>>> data, sample_rate = torchaudio.load('foo.mp3')
>>> print(data.size())
torch.Size([2, 278756])
>>> print(sample_rate)
44100
>>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max())
>>> print(data_vol_normalized.abs().max())
1.
"""
# stringify if `pathlib.Path` (noop if already `str`)
filepath = str(filepath)
# check if valid file
if not os.path.isfile(filepath):
raise OSError("{} not found or is a directory".format(filepath))
# initialize output tensor
if out is not None:
check_input(out)
else:
out = torch.FloatTensor()
if num_frames < -1:
raise ValueError(
"Expected value for num_samples -1 (entire file) or >=0")
if offset < 0:
raise ValueError("Expected positive offset value")
sample_rate = _torch_sox.read_audio_file(filepath, out, channels_first,
num_frames, offset, signalinfo,
encodinginfo, filetype)
# normalize if needed
_audio_normalization(out, normalization)
return out, sample_rate