torchaudio.sox_effects.apply_effects_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[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, optional) –

  When True, this function converts the native sample type to float32. Default: True.

  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, optional) – When True, the returned Tensor has dimension [channel, time]. Otherwise, the returned Tensor’s dimension is [time, channel].

• format (str or None, 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:

(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

Tutorials using apply_effects_file:

Audio Feature Augmentation

Audio Feature Augmentation

Audio Data Augmentation

Audio Data Augmentation