Move TorchAudio-Squim models to Beta

docs/source/_templates/autosummary/model_class.rst

@@ -46,6 +46,14 @@
             "hdemucs_medium",
             "hdemucs_high",
         ],
+        "torchaudio.models.SquimObjective": [
+            "squim_objective_model",
+            "squim_objective_base",
+        ],
+        "torchaudio.models.SquimSubjective": [
+            "squim_subjective_model",
+            "squim_subjective_base",
+        ],
     }
 -%}
 {%- set prototype_factory = {

docs/source/_templates/autosummary/prototype_model_class.rst

@@ -13,14 +13,6 @@
     }
 -%}
 {%- set factory={
-        "torchaudio.prototype.models.SquimObjective": [
-            "squim_objective_model",
-            "squim_objective_base",
-        ],
-        "torchaudio.prototype.models.SquimSubjective": [
-            "squim_subjective_model",
-            "squim_subjective_base",
-        ],
         "torchaudio.prototype.models.ConformerWav2Vec2PretrainModel": [
             "conformer_wav2vec2_pretrain_model",
             "conformer_wav2vec2_pretrain_base",

docs/source/models.rst

@@ -28,6 +28,8 @@ For such models, factory functions are provided.
    HuBERTPretrainModel
    RNNT
    RNNTBeamSearch
+   SquimObjective
+   SquimSubjective
    Tacotron2
    Wav2Letter
    Wav2Vec2Model

docs/source/pipelines.rst

@@ -217,3 +217,53 @@ Pretrained Models
    CONVTASNET_BASE_LIBRI2MIX
    HDEMUCS_HIGH_MUSDB_PLUS
    HDEMUCS_HIGH_MUSDB
+
+Squim Objective
+---------------
+
+Interface
+~~~~~~~~~
+
+:py:class:`SquimObjectiveBundle` defines speech quality and intelligibility measurement (SQUIM) pipeline that can predict **objecive** metric scores given the input waveform.
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: autosummary/bundle_class.rst
+
+   SquimObjectiveBundle
+
+Pretrained Models
+~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: autosummary/bundle_data.rst
+
+   SQUIM_OBJECTIVE
+
+Squim Subjective
+----------------
+
+Interface
+~~~~~~~~~
+
+:py:class:`SquimSubjectiveBundle` defines speech quality and intelligibility measurement (SQUIM) pipeline that can predict **subjective** metric scores given the input waveform.
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: autosummary/bundle_class.rst
+
+   SquimSubjectiveBundle
+
+Pretrained Models
+~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: autosummary/bundle_data.rst
+
+   SQUIM_SUBJECTIVE

docs/source/prototype.models.rst

@@ -23,8 +23,6 @@ For such models, factory functions are provided.
   ConformerWav2Vec2PretrainModel
   ConvEmformer
   HiFiGANVocoder
-  SquimObjective
-  SquimSubjective
 
 Prototype Factory Functions of Beta Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/prototype.pipelines.rst

@@ -45,53 +45,3 @@ Pretrained Models
    :template: autosummary/bundle_data.rst
 
    HIFIGAN_VOCODER_V3_LJSPEECH
-
-Squim Objective
----------------
-
-Interface
-~~~~~~~~~
-
-:py:class:`SquimObjectiveBundle` defines speech quality and intelligibility measurement (SQUIM) pipeline that can predict **objecive** metric scores given the input waveform.
-
-.. autosummary::
-   :toctree: generated
-   :nosignatures:
-   :template: autosummary/bundle_class.rst
-
-   SquimObjectiveBundle
-
-Pretrained Models
-~~~~~~~~~~~~~~~~~
-
-.. autosummary::
-   :toctree: generated
-   :nosignatures:
-   :template: autosummary/bundle_data.rst
-
-   SQUIM_OBJECTIVE
-
-Squim Subjective
-----------------
-
-Interface
-~~~~~~~~~
-
-:py:class:`SquimSubjectiveBundle` defines speech quality and intelligibility measurement (SQUIM) pipeline that can predict **subjective** metric scores given the input waveform.
-
-.. autosummary::
-   :toctree: generated
-   :nosignatures:
-   :template: autosummary/bundle_class.rst
-
-   SquimSubjectiveBundle
-
-Pretrained Models
-~~~~~~~~~~~~~~~~~
-
-.. autosummary::
-   :toctree: generated
-   :nosignatures:
-   :template: autosummary/bundle_data.rst
-
-   SQUIM_SUBJECTIVE

examples/tutorials/squim_tutorial.py

@@ -80,7 +80,7 @@
 try:
     from pesq import pesq
     from pystoi import stoi
-    from torchaudio.prototype.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE
+    from torchaudio.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE
 except ImportError:
     import google.colab  # noqa: F401
 

test/integration_tests/prototype/squim_pipeline_test.py → test/integration_tests/squim_pipeline_test.py

@@ -1,6 +1,6 @@
 import pytest
 import torchaudio
-from torchaudio.prototype.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE
+from torchaudio.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE
 
 
 @pytest.mark.parametrize(

test/torchaudio_unittest/prototype/squim_test.py → test/torchaudio_unittest/models/squim/squim_test.py

@@ -1,6 +1,6 @@
 import torch
 from parameterized import parameterized
-from torchaudio.prototype.models import squim_objective_base, squim_subjective_base
+from torchaudio.models import squim_objective_base, squim_subjective_base
 from torchaudio_unittest.common_utils import skipIfNoCuda, torch_script, TorchaudioTestCase
 
 

torchaudio/models/__init__.py

@@ -5,6 +5,14 @@
 from .emformer import Emformer
 from .rnnt import emformer_rnnt_base, emformer_rnnt_model, RNNT
 from .rnnt_decoder import Hypothesis, RNNTBeamSearch
+from .squim import (
+    squim_objective_base,
+    squim_objective_model,
+    squim_subjective_base,
+    squim_subjective_model,
+    SquimObjective,
+    SquimSubjective,
+)
 from .tacotron2 import Tacotron2
 from .wav2letter import Wav2Letter
 from .wav2vec2 import (
@@ -68,4 +76,10 @@
     "hdemucs_low",
     "hdemucs_medium",
     "hdemucs_high",
+    "squim_objective_base",
+    "squim_objective_model",
+    "squim_subjective_base",
+    "squim_subjective_model",
+    "SquimObjective",
+    "SquimSubjective",
 ]

torchaudio/pipelines/__init__.py

@@ -4,6 +4,7 @@
     HDEMUCS_HIGH_MUSDB_PLUS,
     SourceSeparationBundle,
 )
+from ._squim_pipeline import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE, SquimObjectiveBundle, SquimSubjectiveBundle
 from ._tts import (
     TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
     TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH,
@@ -90,4 +91,8 @@
     "CONVTASNET_BASE_LIBRI2MIX",
     "HDEMUCS_HIGH_MUSDB_PLUS",
     "HDEMUCS_HIGH_MUSDB",
+    "SQUIM_OBJECTIVE",
+    "SQUIM_SUBJECTIVE",
+    "SquimObjectiveBundle",
+    "SquimSubjectiveBundle",
 ]

torchaudio/prototype/pipelines/squim_pipeline.py → torchaudio/pipelines/_squim_pipeline.py

@@ -2,13 +2,13 @@
 
 from torchaudio._internal import load_state_dict_from_url
 
-from torchaudio.prototype.models import squim_objective_base, squim_subjective_base, SquimObjective, SquimSubjective
+from torchaudio.models import squim_objective_base, squim_subjective_base, SquimObjective, SquimSubjective
 
 
 @dataclass
 class SquimObjectiveBundle:
     """Data class that bundles associated information to use pretrained
-    :py:class:`~torchaudio.prototype.models.SquimObjective` model.
+    :py:class:`~torchaudio.models.SquimObjective` model.
 
     This class provides interfaces for instantiating the pretrained model along with
     the information necessary to retrieve pretrained weights and additional data
@@ -24,8 +24,7 @@ class SquimObjectiveBundle:
     Example: Estimate the objective metric scores for the input waveform.
         >>> import torch
         >>> import torchaudio
-        >>> # Since SquimObjective bundle is in prototypes, it needs to be exported explicitly
-        >>> from torchaudio.prototype.pipelines import SQUIM_OBJECTIVE as bundle
+        >>> from torchaudio.pipelines import SQUIM_OBJECTIVE as bundle
         >>>
         >>> # Load the SquimObjective bundle
         >>> model = bundle.get_model()
@@ -59,7 +58,7 @@ def get_model(self, *, dl_kwargs=None) -> SquimObjective:
             dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`.
 
         Returns:
-            Variation of :py:class:`~torchaudio.prototype.models.SquimObjective`.
+            Variation of :py:class:`~torchaudio.models.SquimObjective`.
         """
         model = squim_objective_base()
         model.load_state_dict(self._get_state_dict(dl_kwargs))
@@ -82,7 +81,7 @@ def sample_rate(self):
 SQUIM_OBJECTIVE.__doc__ = """SquimObjective pipeline trained using approach described in
     :cite:`kumar2023torchaudio` on the *DNS 2020 Dataset* :cite:`reddy2020interspeech`.
 
-    The underlying model is constructed by :py:func:`torchaudio.prototype.models.squim_objective_base`.
+    The underlying model is constructed by :py:func:`torchaudio.models.squim_objective_base`.
     The weights are under `Creative Commons Attribution 4.0 International License
     <https://github.com/microsoft/DNS-Challenge/blob/interspeech2020/master/LICENSE>`__.
 
@@ -93,7 +92,7 @@ def sample_rate(self):
 @dataclass
 class SquimSubjectiveBundle:
     """Data class that bundles associated information to use pretrained
-    :py:class:`~torchaudio.prototype.models.SquimSubjective` model.
+    :py:class:`~torchaudio.models.SquimSubjective` model.
 
     This class provides interfaces for instantiating the pretrained model along with
     the information necessary to retrieve pretrained weights and additional data
@@ -109,8 +108,7 @@ class SquimSubjectiveBundle:
     Example: Estimate the subjective metric scores for the input waveform.
         >>> import torch
         >>> import torchaudio
-        >>> # Since SquimSubjective bundle is in prototypes, it needs to be exported explicitly
-        >>> from torchaudio.prototype.pipelines import SQUIM_SUBJECTIVE as bundle
+        >>> from torchaudio.pipelines import SQUIM_SUBJECTIVE as bundle
         >>>
         >>> # Load the SquimSubjective bundle
         >>> model = bundle.get_model()
@@ -146,7 +144,7 @@ def get_model(self, *, dl_kwargs=None) -> SquimSubjective:
             dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`.
 
         Returns:
-            Variation of :py:class:`~torchaudio.prototype.models.SquimObjective`.
+            Variation of :py:class:`~torchaudio.models.SquimObjective`.
         """
         model = squim_subjective_base()
         model.load_state_dict(self._get_state_dict(dl_kwargs))
@@ -170,7 +168,7 @@ def sample_rate(self):
     as described in :cite:`manocha2022speech` and :cite:`kumar2023torchaudio`
     on the *BVCC* :cite:`cooper2021voices` and *DAPS* :cite:`mysore2014can` datasets.
 
-    The underlying model is constructed by :py:func:`torchaudio.prototype.models.squim_subjective_base`.
+    The underlying model is constructed by :py:func:`torchaudio.models.squim_subjective_base`.
     The weights are under `Creative Commons Attribution Non Commercial 4.0 International
     <https://zenodo.org/record/4660670#.ZBtWPOxuerN>`__.
 

torchaudio/prototype/models/__init__.py

@@ -11,14 +11,6 @@
 from .hifi_gan import hifigan_vocoder, hifigan_vocoder_v1, hifigan_vocoder_v2, hifigan_vocoder_v3, HiFiGANVocoder
 from .rnnt import conformer_rnnt_base, conformer_rnnt_biasing, conformer_rnnt_biasing_base, conformer_rnnt_model
 from .rnnt_decoder import Hypothesis, RNNTBeamSearchBiasing
-from .squim import (
-    squim_objective_base,
-    squim_objective_model,
-    squim_subjective_base,
-    squim_subjective_model,
-    SquimObjective,
-    SquimSubjective,
-)
 
 __all__ = [
     "conformer_rnnt_base",
@@ -42,10 +34,4 @@
     "hifigan_vocoder_v2",
     "hifigan_vocoder_v3",
     "hifigan_vocoder",
-    "squim_objective_base",
-    "squim_objective_model",
-    "squim_subjective_base",
-    "squim_subjective_model",
-    "SquimObjective",
-    "SquimSubjective",
 ]

torchaudio/prototype/pipelines/__init__.py

@@ -1,14 +1,9 @@
 from .hifigan_pipeline import HIFIGAN_VOCODER_V3_LJSPEECH, HiFiGANVocoderBundle
 from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
-from .squim_pipeline import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE, SquimObjectiveBundle, SquimSubjectiveBundle
 
 __all__ = [
     "EMFORMER_RNNT_BASE_MUSTC",
     "EMFORMER_RNNT_BASE_TEDLIUM3",
     "HIFIGAN_VOCODER_V3_LJSPEECH",
     "HiFiGANVocoderBundle",
-    "SQUIM_OBJECTIVE",
-    "SQUIM_SUBJECTIVE",
-    "SquimObjectiveBundle",
-    "SquimSubjectiveBundle",
 ]