Add deprecation warnings (#3924)

Author: samanklesaria

src/torchaudio/__init__.py

@@ -1,15 +1,20 @@
+from torchaudio._internal.module_utils import dropping_support
+
 # Initialize extension and backend first
 from . import _extension  # noqa  # usort: skip
 from ._backend import (  # noqa  # usort: skip
     AudioMetaData,
     get_audio_backend,
     info,
     list_audio_backends,
-    load,
-    save,
+    load as _load,
+    save as _save,
     set_audio_backend,
 )
 
+load = dropping_support(_load)
+save = dropping_support(_save)
+
 from . import (  # noqa: F401
     compliance,
     datasets,

src/torchaudio/_internal/module_utils.py

@@ -59,6 +59,7 @@ def wrapped(*args, **kwargs):
 
     return decorator
 
+UNSUPPORTED = []
 
 def deprecated(direction: str, version: Optional[str] = None, remove: bool = False):
     """Decorator to add deprecation message
@@ -90,10 +91,14 @@ def wrapped(*args, **kwargs):
        {direction}
         """
 
+        UNSUPPORTED.append(wrapped)
         return wrapped
 
     return decorator
 
+dropping_support = deprecated(
+    "As TorchAudio is no longer being actively developed, this function can no longer be supported."
+    "See https://github.com/pytorch/audio/issues/3902 for more details.", version="2.9", remove=True)
 
 def fail_with_message(message):
     """Generate decorator to give users message about missing TorchAudio extension."""

src/torchaudio/functional/__init__.py

@@ -1,4 +1,6 @@
-from ._alignment import forced_align, merge_tokens, TokenSpan
+from torchaudio._internal.module_utils import dropping_support
+
+from ._alignment import forced_align as _forced_align, merge_tokens, TokenSpan
 from .filtering import (
     allpass_biquad,
     band_biquad,
@@ -23,6 +25,9 @@
     treble_biquad,
     vad,
 )
+
+forced_align = dropping_support(_forced_align)
+
 from .functional import (
     add_noise,
     amplitude_to_DB,
@@ -53,7 +58,7 @@
     preemphasis,
     psd,
     resample,
-    rnnt_loss,
+    rnnt_loss as _rnnt_loss,
     rtf_evd,
     rtf_power,
     sliding_window_cmn,
@@ -62,6 +67,8 @@
     speed,
 )
 
+rnnt_loss = dropping_support(_rnnt_loss)
+
 __all__ = [
     "amplitude_to_DB",
     "compute_deltas",

src/torchaudio/io/__init__.py

@@ -1,7 +1,14 @@
 from torio.io import CodecConfig, StreamingMediaDecoder as StreamReader, StreamingMediaEncoder as StreamWriter
+from torchaudio._internal.module_utils import dropping_support
 
 from ._effector import AudioEffector
-from ._playback import play_audio
+from ._playback import play_audio as _play_audio
+
+CodecConfig.__init__ = dropping_support(CodecConfig.__init__)
+StreamReader.__init__ = dropping_support(StreamReader.__init__)
+StreamWriter.__init__ = dropping_support(StreamWriter.__init__)
+AudioEffector.__init__ = dropping_support(AudioEffector.__init__)
+play_audio = dropping_support(_play_audio)
 
 
 __all__ = [

src/torchaudio/kaldi_io.py

@@ -6,6 +6,7 @@
 import torch
 from torch import Tensor
 from torchaudio._internal import module_utils as _mod_utils
+from torchaudio._internal.module_utils import dropping_support
 
 if _mod_utils.is_module_available("numpy"):
     import numpy as np
@@ -41,6 +42,7 @@ def _convert_method_output_to_tensor(
         yield key, torch.from_numpy(np_arr)
 
 
+@dropping_support
 @_mod_utils.requires_module("kaldi_io", "numpy")
 def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     r"""Create generator of (key,vector<int>) tuples, which reads from the ark file/stream.
@@ -64,6 +66,7 @@ def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True)
 
 
+@dropping_support
 @_mod_utils.requires_module("kaldi_io", "numpy")
 def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     r"""Create generator of (key,vector<float32/float64>) tuples, read according to Kaldi scp.
@@ -84,6 +87,7 @@ def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_scp)
 
 
+@dropping_support
 @_mod_utils.requires_module("kaldi_io", "numpy")
 def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     r"""Create generator of (key,vector<float32/float64>) tuples, which reads from the ark file/stream.
@@ -104,6 +108,7 @@ def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_ark)
 
 
+@dropping_support
 @_mod_utils.requires_module("kaldi_io", "numpy")
 def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     r"""Create generator of (key,matrix<float32/float64>) tuples, read according to Kaldi scp.
@@ -124,6 +129,7 @@ def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_scp)
 
 
+@dropping_support
 @_mod_utils.requires_module("kaldi_io", "numpy")
 def read_mat_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
     r"""Create generator of (key,matrix<float32/float64>) tuples, which reads from the ark file/stream.

src/torchaudio/models/decoder/__init__.py

@@ -1,3 +1,4 @@
+from torchaudio._internal.module_utils import dropping_support
 _CTC_DECODERS = [
     "CTCHypothesis",
     "CTCDecoder",
@@ -33,7 +34,7 @@ def __getattr__(name: str):
                 "To use CUCTC decoder, please set BUILD_CUDA_CTC_DECODER=1 when building from source."
             ) from err
 
-        item = getattr(_cuda_ctc_decoder, name)
+        item = dropping_support(getattr(_cuda_ctc_decoder, name))
         globals()[name] = item
         return item
     raise AttributeError(f"module {__name__} has no attribute {name}")

src/torchaudio/prototype/datasets/musan.py

@@ -4,6 +4,7 @@
 import torch
 from torch.utils.data import Dataset
 from torchaudio.datasets.utils import _load_waveform
+from torchaudio._internal.module_utils import dropping_support
 
 
 _SUBSETS = ["music", "noise", "speech"]
@@ -18,6 +19,7 @@ class Musan(Dataset):
         subset (str): Subset of the dataset to use. Options: [``"music"``, ``"noise"``, ``"speech"``].
     """
 
+    @dropping_support
     def __init__(self, root: Union[str, Path], subset: str):
         if subset not in _SUBSETS:
             raise ValueError(f"Invalid subset '{subset}' given. Please provide one of {_SUBSETS}")

src/torchaudio/prototype/functional/_dsp.py

@@ -4,8 +4,10 @@
 import torch
 
 from torchaudio.functional import fftconvolve
+from torchaudio._internal.module_utils import dropping_support
 
 
+@dropping_support
 def oscillator_bank(
     frequencies: torch.Tensor,
     amplitudes: torch.Tensor,
@@ -81,6 +83,7 @@ def oscillator_bank(
     return waveform
 
 
+@dropping_support
 def adsr_envelope(
     num_frames: int,
     *,
@@ -182,6 +185,7 @@ def adsr_envelope(
     return out
 
 
+@dropping_support
 def extend_pitch(
     base: torch.Tensor,
     pattern: Union[int, List[float], torch.Tensor],
@@ -249,6 +253,7 @@ def extend_pitch(
     return h_freq
 
 
+@dropping_support
 def sinc_impulse_response(cutoff: torch.Tensor, window_size: int = 513, high_pass: bool = False):
     """Create windowed-sinc impulse response for given cutoff frequencies.
 
@@ -288,6 +293,7 @@ def sinc_impulse_response(cutoff: torch.Tensor, window_size: int = 513, high_pas
     return filt
 
 
+@dropping_support
 def frequency_impulse_response(magnitudes):
     """Create filter from desired frequency response
 
@@ -319,6 +325,7 @@ def _overlap_and_add(waveform, stride):
     return buffer
 
 
+@dropping_support
 def filter_waveform(waveform: torch.Tensor, kernels: torch.Tensor, delay_compensation: int = -1):
     """Applies filters along time axis of the given waveform.
 
@@ -404,6 +411,7 @@ def filter_waveform(waveform: torch.Tensor, kernels: torch.Tensor, delay_compens
     return result
 
 
+@dropping_support
 def exp_sigmoid(
     input: torch.Tensor, exponent: float = 10.0, max_value: float = 2.0, threshold: float = 1e-7
 ) -> torch.Tensor:

src/torchaudio/prototype/functional/_rir.py

@@ -1,5 +1,6 @@
 import math
 from typing import Optional, Tuple, Union
+from torchaudio._internal.module_utils import dropping_support
 
 import torch
 import torchaudio
@@ -176,6 +177,7 @@ def _validate_inputs(
         raise ValueError(f"`mic_array` must be a 2D Tensor with shape (num_channels, 3). Found {mic_array.shape}.")
 
 
+@dropping_support
 def simulate_rir_ism(
     room: torch.Tensor,
     source: torch.Tensor,
@@ -276,6 +278,7 @@ def simulate_rir_ism(
     return rir
 
 
+@dropping_support
 def ray_tracing(
     room: torch.Tensor,
     source: torch.Tensor,

src/torchaudio/prototype/functional/functional.py

@@ -4,6 +4,7 @@
 
 import torch
 from torchaudio.functional.functional import _create_triangular_filterbank
+from torchaudio._internal.module_utils import dropping_support
 
 
 def _hz_to_bark(freqs: float, bark_scale: str = "traunmuller") -> float:
@@ -72,6 +73,7 @@ def _hz_to_octs(freqs, tuning=0.0, bins_per_octave=12):
     return torch.log2(freqs / (a440 / 16))
 
 
+@dropping_support
 def barkscale_fbanks(
     n_freqs: int,
     f_min: float,
@@ -129,6 +131,7 @@ def barkscale_fbanks(
     return fb
 
 
+@dropping_support
 def chroma_filterbank(
     sample_rate: int,
     n_freqs: int,