File size: 11,253 Bytes
864affd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
import os
from typing import List, Optional, Tuple

import torch
import torchaudio
from torchaudio._internal.module_utils import deprecated
from torchaudio.utils.sox_utils import list_effects


sox_ext = torchaudio._extension.lazy_import_sox_ext()


@deprecated("Please remove the call. This function is called automatically.")
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.

    """
    pass


@deprecated("Please remove the call. This function is called automatically.")
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.

    """
    pass


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())


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 sox_ext.apply_effects_tensor(tensor, sample_rate, effects, channels_first)


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):

            Source of audio data.

        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:

        (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"):
            raise RuntimeError(
                "apply_effects_file function does not support file-like object. "
                "Please use torchaudio.io.AudioEffector."
            )
        path = os.fspath(path)
    return sox_ext.apply_effects_file(path, effects, normalize, channels_first, format)