def sox_build_flow_effects(self, out=None):
r"""Build effects chain and flow effects from input file to output tensor
Args:
out (torch.Tensor): Where the output will be written to. (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)
"""
# initialize output tensor
if out is not None:
torchaudio.check_input(out)
else:
out = torch.FloatTensor()
if not len(self.chain):
e = SoxEffect()
e.ename = "no_effects"
e.eopts = [""]
self.chain.append(e)
# print("effect options:", [x.eopts for x in self.chain])
sr = _torch_sox.build_flow_effects(self.input_file,
out,
self.channels_first,
self.out_siginfo,
self.out_encinfo,
self.filetype,
self.chain,
self.MAX_EFFECT_OPTS)
torchaudio._audio_normalization(out, self.normalization)
return out, sr
def load(filepath: str,
out: Optional[Tensor] = None,
normalization: bool = True,
channels_first: bool = True,
num_frames: int = 0,
offset: int = 0,
signalinfo: SignalInfo = None,
encodinginfo: EncodingInfo = None,
filetype: Optional[str] = None) -> Tuple[Tensor, int]:
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")
from . import _torchaudio
sample_rate = _torchaudio.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 sox_build_flow_effects(self, out=None):
"""Build effects chain and flow effects from input file to output tensor
"""
# initialize output tensor
if out is not None:
torchaudio.check_input(out)
else:
out = torch.FloatTensor()
if not len(self.chain):
e = SoxEffect()
e.ename = "no_effects"
e.eopts = [""]
self.chain.append(e)
# print("effect options:", [x.eopts for x in self.chain])
sr = _torch_sox.build_flow_effects(self.input_file, out,
self.channels_first,
self.out_siginfo, self.out_encinfo,
self.filetype, self.chain,
self.MAX_EFFECT_OPTS)
torchaudio._audio_normalization(out, self.normalization)
return out, sr