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 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