torchaudio.sox_effects.apply_effects_tensor

torchaudio.sox_effects.apply_effects_tensor(tensor: Tensor, sample_rate: int, effects: List[List[str]], channels_first: bool = True) → Tuple[Tensor, int]

Apply sox effects to given Tensor

Note

This function only works on CPU Tensors. 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 CPU Tensor.

• sample_rate (int) – Sample rate

• effects (List[List[str]]) – List of effects.

• channels_first (bool, optional) – 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:

(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

Tutorials using apply_effects_tensor:

Audio Data Augmentation

Audio Data Augmentation