torchaudio.sox_effects

Resource initialization / shutdown

torchaudio.sox_effects.init_sox_effects()

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()

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]

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]

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 command 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)
>>> 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, format: Optional[str] = None) → Tuple[torch.Tensor, int]

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 (path-like object or file-like object) –

  Source of audio data. When the function is not compiled by TorchScript, (e.g. torch.jit.script), the following types are accepted:

  • path-like: file path

  • file-like: Object with read(size: int) -> bytes method, which returns byte string of at most size length.

  When the function is compiled by TorchScript, only str type is allowed.

  Note: This argument is intentionally annotated as str only for TorchScript compiler compatibility.

• 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].

• format (str, optional) – Override the format detection with the given format. Providing the argument might help when libsox can not infer the format from header or extension,

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
...
...     def __getitem__(self, index):
...         speed = 0.5 + 1.5 * random.randn()
...         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