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] [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 (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 pathfile-like
: Object withread(size: int) -> bytes
method, which returns byte string of at mostsize
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 tofloat32
. 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 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:
(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 AugmentationAudio Data Augmentation