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Source code for torchaudio.sox_effects.sox_effects

import os
from typing import List, Optional, Tuple

import torch
import torchaudio
from torchaudio._internal import module_utils as _mod_utils
from torchaudio.utils.sox_utils import list_effects


[docs]@_mod_utils.requires_sox() def 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 :func:`shutdown_sox_effects` is not called yet. Once :func:`shutdown_sox_effects` is called, you can no longer use SoX effects and initializing again will result in error. """ torch.ops.torchaudio.sox_effects_initialize_sox_effects()
[docs]@_mod_utils.requires_sox() def 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 :py:func:`shutdown_sox_effects` is called, you can no longer use SoX effects and initializing again will result in error. """ torch.ops.torchaudio.sox_effects_shutdown_sox_effects()
[docs]@_mod_utils.requires_sox() def effect_names() -> List[str]: """Gets list of valid sox effect names Returns: List[str]: list of available effect names. Example >>> torchaudio.sox_effects.effect_names() ['allpass', 'band', 'bandpass', ... ] """ return list(list_effects().keys())
[docs]@_mod_utils.requires_sox() def 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 .. devices:: CPU .. properties:: TorchScript 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.). Args: 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: (Tensor, int): 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. 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 """ return torch.ops.torchaudio.sox_effects_apply_effects_tensor(tensor, sample_rate, effects, channels_first)
[docs]@_mod_utils.requires_sox() def 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 .. devices:: CPU .. properties:: TorchScript 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. Args: 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 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, 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: (Tensor, int): 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]`. 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 """ if not torch.jit.is_scripting(): if hasattr(path, "read"): return torchaudio._torchaudio.apply_effects_fileobj(path, effects, normalize, channels_first, format) path = os.fspath(path) return torch.ops.torchaudio.sox_effects_apply_effects_file(path, effects, normalize, channels_first, format)

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