Refactor backend and not rely on global variables on switching (#698)

* Refactor backend switching

1. Do not rely on global variables for backend switch
   So that load/save/info/load_wav functions will be torchscript-able
2. Add no_backend module to for the case there is no backend module available
   [bonus] This allows the whole codebase importable on systems that do not have torchaudio C++ extension nor soundfile.

Author: mthrok

test/test_backend.py

@@ -0,0 +1,40 @@
+import unittest
+
+import torchaudio
+from torchaudio._internal.module_utils import is_module_available
+
+
+class BackendSwitch:
+    """Test set/get_audio_backend works"""
+    backend = None
+    backend_module = None
+
+    def test_switch(self):
+        torchaudio.set_audio_backend(self.backend)
+        if self.backend is None:
+            assert torchaudio.get_audio_backend() is None
+        else:
+            assert torchaudio.get_audio_backend() == self.backend
+        assert torchaudio.load == self.backend_module.load
+        assert torchaudio.load_wav == self.backend_module.load_wav
+        assert torchaudio.save == self.backend_module.save
+        assert torchaudio.info == self.backend_module.info
+
+
+class TestBackendSwitch_NoBackend(BackendSwitch, unittest.TestCase):
+    backend = None
+    backend_module = torchaudio.backend.no_backend
+
+
+@unittest.skipIf(
+    not is_module_available('torchaudio._torchaudio'),
+    'torchaudio C++ extension not available')
+class TestBackendSwitch_SoX(BackendSwitch, unittest.TestCase):
+    backend = 'sox'
+    backend_module = torchaudio.backend.sox_backend
+
+
+@unittest.skipIf(not is_module_available('soundfile'), '"soundfile" not available')
+class TestBackendSwitch_soundfile(BackendSwitch, unittest.TestCase):
+    backend = 'soundfile'
+    backend_module = torchaudio.backend.soundfile_backend

test/test_datasets.py

@@ -29,11 +29,13 @@ def test_librispeech(self):
         data[0]
 
     @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available")
+    @common_utils.AudioBackendScope('sox')
     def test_commonvoice(self):
         data = COMMONVOICE(self.path, url="tatar")
         data[0]
 
     @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available")
+    @common_utils.AudioBackendScope('sox')
     def test_commonvoice_diskcache(self):
         data = COMMONVOICE(self.path, url="tatar")
         data = diskcache_iterator(data)
@@ -43,6 +45,7 @@ def test_commonvoice_diskcache(self):
         data[0]
 
     @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available")
+    @common_utils.AudioBackendScope('sox')
     def test_commonvoice_bg(self):
         data = COMMONVOICE(self.path, url="tatar")
         data = bg_iterator(data, 5)

torchaudio/__init__.py

@@ -1,7 +1,3 @@
-from pathlib import Path
-from typing import Any, Callable, Optional, Tuple, Union
-
-from torch import Tensor
 from torchaudio._internal import module_utils as _mod_utils
 from torchaudio import (
     compliance,
@@ -11,7 +7,6 @@
     transforms
 )
 from torchaudio.backend import (
-    _get_audio_backend_module,
     list_audio_backends,
     get_audio_backend,
     set_audio_backend,
@@ -57,116 +52,3 @@ def shutdown_sox():
     This function is deprecated. See ``torchaudio.sox_effects.shutdown_sox_effects``
     """
     _shutdown_sox_effects()
-
-
-def load(filepath: Union[str, Path],
-         out: Optional[Tensor] = None,
-         normalization: Union[bool, float, Callable] = True,
-         channels_first: bool = True,
-         num_frames: int = 0,
-         offset: int = 0,
-         signalinfo: Optional[SignalInfo] = None,
-         encodinginfo: Optional[EncodingInfo] = None,
-         filetype: Optional[str] = None) -> Tuple[Tensor, int]:
-    r"""Loads an audio file from disk into a tensor
-
-    Args:
-        filepath (str or Path): Path to audio file
-        out (Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``)
-        normalization (bool, float, or callable, optional): If boolean `True`, then output is divided by `1 << 31`
-            (assumes signed 32-bit audio), and normalizes to `[-1, 1]`.
-            If `float`, then output is divided by that number
-            If `Callable`, then the output is passed as a parameter
-            to the given function, then the output is divided by
-            the result. (Default: ``True``)
-        channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``)
-        num_frames (int, optional): Number of frames to load.  0 to load everything after the offset.
-            (Default: ``0``)
-        offset (int, optional): Number of frames from the start of the file to begin data loading.
-            (Default: ``0``)
-        signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the
-            audio type cannot be automatically determined. (Default: ``None``)
-        encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the
-            audio type cannot be automatically determined. (Default: ``None``)
-        filetype (str, optional): A filetype or extension to be set if sox cannot determine it
-            automatically. (Default: ``None``)
-
-    Returns:
-        (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number
-        of audio frames and C is the number of channels. An integer which is the sample rate of the
-        audio (as listed in the metadata of the file)
-
-    Example
-        >>> data, sample_rate = torchaudio.load('foo.mp3')
-        >>> print(data.size())
-        torch.Size([2, 278756])
-        >>> print(sample_rate)
-        44100
-        >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max())
-        >>> print(data_vol_normalized.abs().max())
-        1.
-
-    """
-    return _get_audio_backend_module().load(
-        filepath,
-        out=out,
-        normalization=normalization,
-        channels_first=channels_first,
-        num_frames=num_frames,
-        offset=offset,
-        signalinfo=signalinfo,
-        encodinginfo=encodinginfo,
-        filetype=filetype,
-    )
-
-
-def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]:
-    r""" Loads a wave file. It assumes that the wav file uses 16 bit per sample that needs normalization by shifting
-    the input right by 16 bits.
-
-    Args:
-        filepath (str or Path): Path to audio file
-
-    Returns:
-        (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number
-        of audio frames and C is the number of channels. An integer which is the sample rate of the
-        audio (as listed in the metadata of the file)
-    """
-    kwargs['normalization'] = 1 << 16
-    return load(filepath, **kwargs)
-
-
-def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None:
-    r"""Convenience function for `save_encinfo`.
-
-    Args:
-        filepath (str): Path to audio file
-        src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is
-            the number of audio frames, C is the number of channels
-        sample_rate (int): An integer which is the sample rate of the
-            audio (as listed in the metadata of the file)
-        precision (int, optional): Bit precision (Default: ``16``)
-        channels_first (bool, optional): Set channels first or length first in result. (
-            Default: ``True``)
-    """
-
-    return _get_audio_backend_module().save(
-        filepath, src, sample_rate, precision=precision, channels_first=channels_first
-    )
-
-
-def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]:
-    r"""Gets metadata from an audio file without loading the signal.
-
-     Args:
-        filepath (str): Path to audio file
-
-     Returns:
-        (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal
-        info as a python object. An ei (sox_encodinginfo_t) encoding info
-
-     Example
-         >>> si, ei = torchaudio.info('foo.wav')
-         >>> rate, channels, encoding = si.rate, si.channels, ei.encoding
-    """
-    return _get_audio_backend_module().info(filepath)

torchaudio/backend/__init__.py

@@ -1,6 +1,5 @@
 from . import utils
 from .utils import (
-    _get_audio_backend_module,
     list_audio_backends,
     get_audio_backend,
     set_audio_backend,

torchaudio/backend/common.py

@@ -1,4 +1,4 @@
-from typing import Any, Optional, Tuple
+from typing import Any, Optional
 
 
 class SignalInfo:
@@ -29,3 +29,115 @@ def __init__(self,
         self.reverse_nibbles = reverse_nibbles
         self.reverse_bits = reverse_bits
         self.opposite_endian = opposite_endian
+
+
+_LOAD_DOCSTRING = r"""Loads an audio file from disk into a tensor
+
+Args:
+    filepath: Path to audio file
+
+    out: An optional output tensor to use instead of creating one. (Default: ``None``)
+
+    normalization: Optional normalization.
+        If boolean `True`, then output is divided by `1 << 31`.
+        Assuming the input is signed 32-bit audio, this normalizes to `[-1, 1]`.
+        If `float`, then output is divided by that number.
+        If `Callable`, then the output is passed as a paramete to the given function,
+        then the output is divided by the result. (Default: ``True``)
+
+    channels_first: Set channels first or length first in result. (Default: ``True``)
+
+    num_frames: Number of frames to load.  0 to load everything after the offset.
+        (Default: ``0``)
+
+    offset: Number of frames from the start of the file to begin data loading.
+        (Default: ``0``)
+
+    signalinfo: A sox_signalinfo_t type, which could be helpful if the
+        audio type cannot be automatically determined. (Default: ``None``)
+
+    encodinginfo: A sox_encodinginfo_t type, which could be set if the
+        audio type cannot be automatically determined. (Default: ``None``)
+
+    filetype: A filetype or extension to be set if sox cannot determine it
+        automatically. (Default: ``None``)
+
+Returns:
+    (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where
+        L is the number of audio frames and
+        C is the number of channels.
+        An integer which is the sample rate of the audio (as listed in the metadata of the file)
+
+Example
+    >>> data, sample_rate = torchaudio.load('foo.mp3')
+    >>> print(data.size())
+    torch.Size([2, 278756])
+    >>> print(sample_rate)
+    44100
+    >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max())
+    >>> print(data_vol_normalized.abs().max())
+    1.
+"""
+
+
+_LOAD_WAV_DOCSTRING = r""" Loads a wave file.
+
+It assumes that the wav file uses 16 bit per sample that needs normalization by
+shifting the input right by 16 bits.
+
+Args:
+    filepath: Path to audio file
+
+Returns:
+    (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number
+        of audio frames and C is the number of channels. An integer which is the sample rate of the
+        audio (as listed in the metadata of the file)
+"""
+
+_SAVE_DOCSTRING = r"""Saves a Tensor on file as an audio file
+
+Args:
+    filepath: Path to audio file
+    src: An input 2D tensor of shape `[C x L]` or `[L x C]` where L is
+        the number of audio frames, C is the number of channels
+    sample_rate: An integer which is the sample rate of the
+        audio (as listed in the metadata of the file)
+    precision Bit precision (Default: ``16``)
+    channels_first (bool, optional): Set channels first or length first in result. (
+        Default: ``True``)
+"""
+
+
+_INFO_DOCSTRING = r"""Gets metadata from an audio file without loading the signal.
+
+Args:
+    filepath: Path to audio file
+
+Returns:
+    (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal
+        info as a python object. An ei (sox_encodinginfo_t) encoding info
+
+Example
+    >>> si, ei = torchaudio.info('foo.wav')
+    >>> rate, channels, encoding = si.rate, si.channels, ei.encoding
+"""
+
+
+def _impl_load(func):
+    setattr(func, '__doc__', _LOAD_DOCSTRING)
+    return func
+
+
+def _impl_load_wav(func):
+    setattr(func, '__doc__', _LOAD_WAV_DOCSTRING)
+    return func
+
+
+def _impl_save(func):
+    setattr(func, '__doc__', _SAVE_DOCSTRING)
+    return func
+
+
+def _impl_info(func):
+    setattr(func, '__doc__', _INFO_DOCSTRING)
+    return func

torchaudio/backend/no_backend.py

@@ -0,0 +1,35 @@
+from pathlib import Path
+from typing import Any, Callable, Optional, Tuple, Union
+
+from torch import Tensor
+
+from . import common
+from .common import SignalInfo, EncodingInfo
+
+
+@common._impl_load
+def load(filepath: Union[str, Path],
+         out: Optional[Tensor] = None,
+         normalization: Union[bool, float, Callable] = True,
+         channels_first: bool = True,
+         num_frames: int = 0,
+         offset: int = 0,
+         signalinfo: Optional[SignalInfo] = None,
+         encodinginfo: Optional[EncodingInfo] = None,
+         filetype: Optional[str] = None) -> Tuple[Tensor, int]:
+    raise RuntimeError('No audio I/O backend is available.')
+
+
+@common._impl_load_wav
+def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]:
+    raise RuntimeError('No audio I/O backend is available.')
+
+
+@common._impl_save
+def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None:
+    raise RuntimeError('No audio I/O backend is available.')
+
+
+@common._impl_info
+def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]:
+    raise RuntimeError('No audio I/O backend is available.')

torchaudio/backend/soundfile_backend.py

@@ -8,6 +8,7 @@
     module_utils as _mod_utils,
     misc_ops as _misc_ops,
 )
+from . import common
 from .common import SignalInfo, EncodingInfo
 
 if _mod_utils.is_module_available('soundfile'):
@@ -24,6 +25,7 @@
 
 
 @_mod_utils.requires_module('soundfile')
+@common._impl_load
 def load(filepath: str,
          out: Optional[Tensor] = None,
          normalization: Optional[bool] = True,
@@ -71,6 +73,14 @@ def load(filepath: str,
 
 
 @_mod_utils.requires_module('soundfile')
+@common._impl_load_wav
+def load_wav(filepath, **kwargs):
+    kwargs['normalization'] = 1 << 16
+    return load(filepath, **kwargs)
+
+
+@_mod_utils.requires_module('soundfile')
+@common._impl_save
 def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None:
     r"""See torchaudio.save"""
 
@@ -104,6 +114,7 @@ def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, chan
 
 
 @_mod_utils.requires_module('soundfile')
+@common._impl_info
 def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]:
     r"""See torchaudio.info"""