torchaudio.sox_effects¶
Warning
The SoxEffect
and SoxEffectsChain
classes are deprecated. Please migrate to apply_effects_tensor()
and apply_effects_file()
.
Resource initialization / shutdown¶

torchaudio.sox_effects.
init_sox_effects
()[source]¶ Initialize resources required to use sox effects.
Note
You do not need to call this function manually. It is called automatically.
Once initialized, you do not need to call this function again across the multiple uses of sox effects though it is safe to do so as long as
shutdown_sox_effects()
is not called yet. Onceshutdown_sox_effects()
is called, you can no longer use SoX effects and initializing again will result in error.

torchaudio.sox_effects.
shutdown_sox_effects
()[source]¶ Clean up resources required to use sox effects.
Note
You do not need to call this function manually. It is called automatically.
It is safe to call this function multiple times. Once
shutdown_sox_effects()
is called, you can no longer use SoX effects and initializing again will result in error.
Listing supported effects¶
Applying effects¶
Apply SoX effects chain on torch.Tensor or on file and load as torch.Tensor.
Applying effects on Tensor¶

torchaudio.sox_effects.
apply_effects_tensor
(tensor: torch.Tensor, sample_rate: int, effects: List[List[str]], channels_first: bool = True) → Tuple[torch.Tensor, int][source]¶ Apply sox effects to given Tensor
Note
This function works in the way very similar to
sox
command, however there are slight differences. For example,sox
commnad adds certain effects automatically (such asrate
effect afterspeed
andpitch
and other effects), but this function does only applies the given effects. (Therefore, to actually applyspeed
effect, you also need to giverate
effect with desired sampling rate.) Parameters
tensor (torch.Tensor) – Input 2D Tensor.
sample_rate (int) – Sample rate
effects (List[List[str]]) – List of effects.
channels_first (bool) – Indicates if the input Tensor’s dimension is
[channels, time]
or[time, channels]
 Returns
Resulting Tensor and sample rate. The resulting Tensor has the same
dtype
as the input Tensor, and the same channels order. The shape of the Tensor can be different based on the effects applied. Sample rate can also be different based on the effects applied. Return type
Tuple[torch.Tensor, int]
 Example  Basic usage
>>> >>> # Defines the effects to apply >>> effects = [ ... ['gain', 'n'], # normalises to 0dB ... ['pitch', '5'], # 5 cent pitch shift ... ['rate', '8000'], # resample to 8000 Hz ... ] >>> >>> # Generate pseudo wave: >>> # normalized, channels first, 2ch, sampling rate 16000, 1 second >>> sample_rate = 16000 >>> waveform = 2 * torch.rand([2, sample_rate * 1])  1 >>> waveform.shape torch.Size([2, 16000]) >>> waveform tensor([[ 0.3138, 0.7620, 0.9019, ..., 0.7495, 0.4935, 0.5442], [0.0832, 0.0061, 0.8233, ..., 0.5176, 0.9140, 0.2434]]) >>> >>> # Apply effects >>> waveform, sample_rate = apply_effects_tensor( ... wave_form, sample_rate, effects, channels_first=True) >>> >>> # Check the result >>> # The new waveform is sampling rate 8000, 1 second. >>> # normalization and channel order are preserved >>> waveform.shape torch.Size([2, 8000]) >>> waveform tensor([[ 0.5054, 0.5518, 0.4800, ..., 0.0076, 0.0096, 0.0110], [ 0.1331, 0.0436, 0.3783, ..., 0.0035, 0.0012, 0.0008]]) >>> sample_rate 8000
 Example  Torchscriptable transform
>>> >>> # Use `apply_effects_tensor` in `torch.nn.Module` and dump it to file, >>> # then run sox effect via Torchscript runtime. >>> >>> class SoxEffectTransform(torch.nn.Module): ... effects: List[List[str]] ... ... def __init__(self, effects: List[List[str]]): ... super().__init__() ... self.effects = effects ... ... def forward(self, tensor: torch.Tensor, sample_rate: int): ... return sox_effects.apply_effects_tensor( ... tensor, sample_rate, self.effects) ... ... >>> # Create transform object >>> effects = [ ... ["lowpass", "1", "300"], # apply singlepole lowpass filter ... ["rate", "8000"], # change sample rate to 8000 ... ] >>> transform = SoxEffectTensorTransform(effects, input_sample_rate) >>> >>> # Dump it to file and load >>> path = 'sox_effect.zip' >>> torch.jit.script(trans).save(path) >>> transform = torch.jit.load(path) >>> >>>> # Run transform >>> waveform, input_sample_rate = torchaudio.load("input.wav") >>> waveform, sample_rate = transform(waveform, input_sample_rate) >>> assert sample_rate == 8000
Applying effects on file¶

torchaudio.sox_effects.
apply_effects_file
(path: str, effects: List[List[str]], normalize: bool = True, channels_first: bool = True) → Tuple[torch.Tensor, int][source]¶ Apply sox effects to the audio file and load the resulting data as Tensor
Note
This function works in the way very similar to
sox
command, however there are slight differences. For example,sox
commnad adds certain effects automatically (such asrate
effect afterspeed
,pitch
etc), but this function only applies the given effects. Therefore, to actually applyspeed
effect, you also need to giverate
effect with desired sampling rate, because internally,speed
effects only alter sampling rate and leave samples untouched. Parameters
path (str) – Path to the audio file.
effects (List[List[str]]) – List of effects.
normalize (bool) – When
True
, this function always returnfloat32
, and sample values are normalized to[1.0, 1.0]
. If input file is integer WAV, givingFalse
will change the resulting Tensor type to integer type. This argument has no effect for formats other than integer WAV type.channels_first (bool) – When True, the returned Tensor has dimension
[channel, time]
. Otherwise, the returned Tensor’s dimension is[time, channel]
.
 Returns
Resulting Tensor and sample rate. If
normalize=True
, the resulting Tensor is alwaysfloat32
type. Ifnormalize=False
and the input audio file is of integer WAV file, then the resulting Tensor has corresponding integer type. (Note 24 bit integer type is not supported) Ifchannels_first=True
, the resulting Tensor has dimension[channel, time]
, otherwise[time, channel]
. Return type
Tuple[torch.Tensor, int]
 Example  Basic usage
>>> >>> # Defines the effects to apply >>> effects = [ ... ['gain', 'n'], # normalises to 0dB ... ['pitch', '5'], # 5 cent pitch shift ... ['rate', '8000'], # resample to 8000 Hz ... ] >>> >>> # Apply effects and load data with channels_first=True >>> waveform, sample_rate = apply_effects_file("data.wav", effects, channels_first=True) >>> >>> # Check the result >>> waveform.shape torch.Size([2, 8000]) >>> waveform tensor([[ 5.1151e03, 1.8073e02, 2.2188e02, ..., 1.0431e07, 1.4761e07, 1.8114e07], [2.6924e03, 2.1860e03, 1.0650e02, ..., 6.4122e07, 5.6159e07, 4.8103e07]]) >>> sample_rate 8000
 Example  Apply random speed perturbation to dataset
>>> >>> # Load data from file, apply random speed perturbation >>> class RandomPerturbationFile(torch.utils.data.Dataset): ... """Given flist, apply random speed perturbation ... ... Suppose all the input files are at least one second long. ... """ ... def __init__(self, flist: List[str], sample_rate: int): ... super().__init__() ... self.flist = flist ... self.sample_rate = sample_rate ... self.rng = None ... ... def __getitem__(self, index): ... speed = self.rng.uniform(0.5, 2.0) ... effects = [ ... ['gain', 'n', '10'], # apply 10 db attenuation ... ['remix', ''], # merge all the channels ... ['speed', f'{speed:.5f}'], # duration is now 0.5 ~ 2.0 seconds. ... ['rate', f'{self.sample_rate}'], ... ['pad', '0', '1.5'], # add 1.5 seconds silence at the end ... ['trim', '0', '2'], # get the first 2 seconds ... ] ... waveform, _ = torchaudio.sox_effects.apply_effects_file( ... self.flist[index], effects) ... return waveform ... ... def __len__(self): ... return len(self.flist) ... >>> dataset = RandomPerturbationFile(file_list, sample_rate=8000) >>> loader = torch.utils.data.DataLoader(dataset, batch_size=32) >>> for batch in loader: >>> pass
Legacy¶
SoxEffect¶

class
torchaudio.sox_effects.
SoxEffect
[source]¶ Create an object for passing sox effect information between python and c++
Warning
This function is deprecated. Please migrate to
apply_effects_file()
orapply_effects_tensor()
. Returns
An object with the following attributes: ename (str) which is the name of effect, and eopts (List[str]) which is a list of effect options.
 Return type
SoxEffectsChain¶

class
torchaudio.sox_effects.
SoxEffectsChain
(normalization: Union[bool, float, Callable] = True, channels_first: bool = True, out_siginfo: Any = None, out_encinfo: Any = None, filetype: str = 'raw')[source]¶ SoX effects chain class.
Warning
This class is deprecated. Please migrate to
apply_effects_file()
orapply_effects_tensor()
. Parameters
normalization (bool, number, or callable, optional) – If boolean
True
, then output is divided by1 << 31
(assumes signed 32bit audio), and normalizes to[1, 1]
. Ifnumber
, then output is divided by that number. Ifcallable
, 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, optional) – Set channels first or length first in result. (Default:
True
)out_siginfo (sox_signalinfo_t, optional) – a sox_signalinfo_t type, which could be helpful if the audio type cannot be automatically determined. (Default:
None
)out_encinfo (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:
'raw'
)
 Returns
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) Return type
Tuple[Tensor, int]
 Example
>>> class MyDataset(Dataset): ... def __init__(self, audiodir_path): ... self.data = [ ... os.path.join(audiodir_path, fn) ... for fn in os.listdir(audiodir_path)] ... self.E = torchaudio.sox_effects.SoxEffectsChain() ... self.E.append_effect_to_chain("rate", [16000]) # resample to 16000hz ... self.E.append_effect_to_chain("channels", ["1"]) # mono signal ... def __getitem__(self, index): ... fn = self.data[index] ... self.E.set_input_file(fn) ... x, sr = self.E.sox_build_flow_effects() ... return x, sr ... ... def __len__(self): ... return len(self.data) ... >>> ds = MyDataset(path_to_audio_files) >>> for sig, sr in ds: ... pass

append_effect_to_chain
(ename: str, eargs: Union[List[str], str, None] = None) → None[source]¶ Append effect to a sox effects chain.

set_input_file
(input_file: str) → None[source]¶ Set input file for input of chain
 Parameters
input_file (str) – The path to the input file.

sox_build_flow_effects
(out: Optional[torch.Tensor] = None) → Tuple[torch.Tensor, int][source]¶ Build effects chain and flow effects from input file to output tensor
 Parameters
out (Tensor, optional) – Where the output will be written to. (Default:
None
) Returns
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)
 Return type
Tuple[Tensor, int]