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# torchaudio.sox_effects¶

## 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. Once shutdown_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¶

torchaudio.sox_effects.effect_names() → List[str][source]

Gets list of valid sox effect names

Returns

list of available effect names.

Return type

List[str]

Example
>>> torchaudio.sox_effects.effect_names()
['allpass', 'band', 'bandpass', ... ]


## 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 as rate effect after speed and pitch and other effects), but this function does only applies the given effects. (Therefore, to actually apply speed effect, you also need to give rate 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 - Torchscript-able 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 single-pole 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)
>>>
>>>> # Run transform
>>> 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 as rate effect after speed, pitch etc), but this function only applies the given effects. Therefore, to actually apply speed effect, you also need to give rate 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 return float32, and sample values are normalized to [-1.0, 1.0]. If input file is integer WAV, giving False 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 always float32 type. If normalize=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) If channels_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([[ 5.1151e-03,  1.8073e-02,  2.2188e-02,  ...,  1.0431e-07,
-1.4761e-07,  1.8114e-07],
[-2.6924e-03,  2.1860e-03,  1.0650e-02,  ...,  6.4122e-07,
-5.6159e-07,  4.8103e-07]])
>>> 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)
>>>     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() or apply_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

SoxEffect

### 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() or apply_effects_tensor().

Parameters
• 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, 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", )  # 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.

Parameters
• ename (str) – which is the name of effect

• eargs (List[str] or str, optional) – which is a list of effect options. (Default: None)

clear_chain() → None[source]

Clear effects chain in python

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]

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