Add Speaker Diarization and Verification heads (#14723)

* Models

* Squashed commit of the following:

commit 72278e1e931a16d0879acc77f65762f3364833d0
Author: anton-l <aglozhkov@gmail.com>
Date:   Fri Dec 10 21:45:08 2021 +0300

* Add unispeech heads

* Add sd/sv automodels

* Docs cleanup

* Fix docstrings

* rename xvector classes

* examples

* Tests cleanup

* Style

* Better checkpoints for tests

* leftover docs

* apply review suggestions

* Style + init tests

* Update unispeech-sat tdnn downsampling

docs/source/model_doc/auto.rst

@@ -181,6 +181,13 @@ AutoModelForAudioClassification
     :members:
 
 
+AutoModelForAudioFrameClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForAudioFrameClassification
+    :members:
+
+
 AutoModelForCTC
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -195,6 +202,13 @@ AutoModelForSpeechSeq2Seq
     :members:
 
 
+AutoModelForAudioXVector
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForAudioXVector
+    :members:
+
+
 AutoModelForObjectDetection
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/unispeech_sat.rst

@@ -85,6 +85,20 @@ UniSpeechSatForSequenceClassification
     :members: forward
 
 
+UniSpeechSatForAudioFrameClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.UniSpeechSatForAudioFrameClassification
+    :members: forward
+
+
+UniSpeechSatForXVector
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.UniSpeechSatForXVector
+    :members: forward
+
+
 UniSpeechSatForPreTraining
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/wav2vec2.rst

@@ -114,6 +114,20 @@ Wav2Vec2ForSequenceClassification
     :members: forward
 
 
+Wav2Vec2ForAudioFrameClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2ForAudioFrameClassification
+    :members: forward
+
+
+Wav2Vec2ForXVector
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2ForXVector
+    :members: forward
+
+
 Wav2Vec2ForPreTraining
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -649,6 +649,8 @@ if is_torch_available():
             "MODEL_WITH_LM_HEAD_MAPPING",
             "AutoModel",
             "AutoModelForAudioClassification",
+            "AutoModelForAudioFrameClassification",
+            "AutoModelForAudioXVector",
             "AutoModelForCausalLM",
             "AutoModelForCTC",
             "AutoModelForImageClassification",
@@ -1325,9 +1327,11 @@ if is_torch_available():
     _import_structure["models.unispeech_sat"].extend(
         [
             "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "UniSpeechSatForAudioFrameClassification",
             "UniSpeechSatForCTC",
             "UniSpeechSatForPreTraining",
             "UniSpeechSatForSequenceClassification",
+            "UniSpeechSatForXVector",
             "UniSpeechSatModel",
             "UniSpeechSatPreTrainedModel",
         ]
@@ -1358,10 +1362,12 @@ if is_torch_available():
     _import_structure["models.wav2vec2"].extend(
         [
             "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "Wav2Vec2ForAudioFrameClassification",
             "Wav2Vec2ForCTC",
             "Wav2Vec2ForMaskedLM",
             "Wav2Vec2ForPreTraining",
             "Wav2Vec2ForSequenceClassification",
+            "Wav2Vec2ForXVector",
             "Wav2Vec2Model",
             "Wav2Vec2PreTrainedModel",
         ]
@@ -2603,6 +2609,8 @@ if TYPE_CHECKING:
             MODEL_WITH_LM_HEAD_MAPPING,
             AutoModel,
             AutoModelForAudioClassification,
+            AutoModelForAudioFrameClassification,
+            AutoModelForAudioXVector,
             AutoModelForCausalLM,
             AutoModelForCTC,
             AutoModelForImageClassification,
@@ -3164,9 +3172,11 @@ if TYPE_CHECKING:
         )
         from .models.unispeech_sat import (
             UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            UniSpeechSatForAudioFrameClassification,
             UniSpeechSatForCTC,
             UniSpeechSatForPreTraining,
             UniSpeechSatForSequenceClassification,
+            UniSpeechSatForXVector,
             UniSpeechSatModel,
             UniSpeechSatPreTrainedModel,
         )
@@ -3191,10 +3201,12 @@ if TYPE_CHECKING:
         )
         from .models.wav2vec2 import (
             WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Wav2Vec2ForAudioFrameClassification,
             Wav2Vec2ForCTC,
             Wav2Vec2ForMaskedLM,
             Wav2Vec2ForPreTraining,
             Wav2Vec2ForSequenceClassification,
+            Wav2Vec2ForXVector,
             Wav2Vec2Model,
             Wav2Vec2PreTrainedModel,
         )

src/transformers/file_utils.py

@@ -1117,6 +1117,54 @@ PT_SPEECH_SEQ_CLASS_SAMPLE = r"""
 """
 
 
+PT_SPEECH_FRAME_CLASS_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {processor_class}, {model_class}
+        >>> from datasets import load_dataset
+        >>> import torch
+
+        >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
+        >>> sampling_rate = dataset.features["audio"].sampling_rate
+
+        >>> feature_extractor = {processor_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> # audio file is decoded on the fly
+        >>> inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt")
+        >>> logits = model(**inputs).logits
+        >>> probabilities = torch.sigmoid(logits[0])
+        >>> # labels is a one-hot array of shape (num_frames, num_speakers)
+        >>> labels = (probabilities > 0.5).long()
+"""
+
+
+PT_SPEECH_XVECTOR_SAMPLE = r"""
+    Example::
+
+        >>> from transformers import {processor_class}, {model_class}
+        >>> from datasets import load_dataset
+        >>> import torch
+
+        >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
+        >>> sampling_rate = dataset.features["audio"].sampling_rate
+
+        >>> feature_extractor = {processor_class}.from_pretrained('{checkpoint}')
+        >>> model = {model_class}.from_pretrained('{checkpoint}')
+
+        >>> # audio file is decoded on the fly
+        >>> inputs = feature_extractor(dataset[:2]["audio"]["array"], return_tensors="pt")
+        >>> embeddings = model(**inputs).embeddings
+        >>> embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()
+
+        >>> # the resulting embeddings can be used for cosine similarity-based retrieval
+        >>> cosine_sim = torch.nn.CosineSimilarity(dim=-1)
+        >>> similarity = cosine_sim(embeddings[0], embeddings[1])
+        >>> threshold = 0.7  # the optimal threshold is dataset-dependent
+        >>> if similarity < threshold:
+        ...     print("Speakers are not the same!")
+"""
+
 PT_SAMPLE_DOCSTRINGS = {
     "SequenceClassification": PT_SEQUENCE_CLASSIFICATION_SAMPLE,
     "QuestionAnswering": PT_QUESTION_ANSWERING_SAMPLE,
@@ -1128,6 +1176,8 @@ PT_SAMPLE_DOCSTRINGS = {
     "SpeechBaseModel": PT_SPEECH_BASE_MODEL_SAMPLE,
     "CTC": PT_SPEECH_CTC_SAMPLE,
     "AudioClassification": PT_SPEECH_SEQ_CLASS_SAMPLE,
+    "AudioFrameClassification": PT_SPEECH_FRAME_CLASS_SAMPLE,
+    "AudioXVector": PT_SPEECH_XVECTOR_SAMPLE,
 }
 
 
@@ -1419,6 +1469,10 @@ def add_code_sample_docstrings(
             code_sample = sample_docstrings["LMHead"]
         elif "CTC" in model_class:
             code_sample = sample_docstrings["CTC"]
+        elif "AudioFrameClassification" in model_class:
+            code_sample = sample_docstrings["AudioFrameClassification"]
+        elif "XVector" in model_class and modality == "audio":
+            code_sample = sample_docstrings["AudioXVector"]
         elif "Model" in model_class and modality == "audio":
             code_sample = sample_docstrings["SpeechBaseModel"]
         elif "Model" in model_class or "Encoder" in model_class:

src/transformers/models/auto/__init__.py

@@ -53,6 +53,8 @@ if is_torch_available():
         "MODEL_WITH_LM_HEAD_MAPPING",
         "AutoModel",
         "AutoModelForAudioClassification",
+        "AutoModelForAudioFrameClassification",
+        "AutoModelForAudioXVector",
         "AutoModelForCausalLM",
         "AutoModelForCTC",
         "AutoModelForImageClassification",
@@ -161,6 +163,8 @@ if TYPE_CHECKING:
             MODEL_WITH_LM_HEAD_MAPPING,
             AutoModel,
             AutoModelForAudioClassification,
+            AutoModelForAudioFrameClassification,
+            AutoModelForAudioXVector,
             AutoModelForCausalLM,
             AutoModelForCTC,
             AutoModelForImageClassification,

src/transformers/models/auto/modeling_auto.py

@@ -538,6 +538,22 @@ MODEL_FOR_CTC_MAPPING_NAMES = OrderedDict(
     ]
 )
 
+MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
+    [
+        # Model for Audio Classification mapping
+        ("wav2vec2", "Wav2Vec2ForAudioFrameClassification"),
+        ("unispeech-sat", "UniSpeechSatForAudioFrameClassification"),
+    ]
+)
+
+MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = OrderedDict(
+    [
+        # Model for Audio Classification mapping
+        ("wav2vec2", "Wav2Vec2ForXVector"),
+        ("unispeech-sat", "UniSpeechSatForXVector"),
+    ]
+)
+
 MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES)
 MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_PRETRAINING_MAPPING_NAMES)
 MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_WITH_LM_HEAD_MAPPING_NAMES)
@@ -578,6 +594,10 @@ MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = _LazyAutoMapping(
 )
 MODEL_FOR_CTC_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CTC_MAPPING_NAMES)
 MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES)
+MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING = _LazyAutoMapping(
+    CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES
+)
+MODEL_FOR_AUDIO_XVECTOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES)
 
 
 class AutoModel(_BaseAutoModelClass):
@@ -726,6 +746,22 @@ AutoModelForSpeechSeq2Seq = auto_class_update(
 )
 
 
+class AutoModelForAudioFrameClassification(_BaseAutoModelClass):
+    _model_mapping = MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING
+
+
+AutoModelForAudioFrameClassification = auto_class_update(
+    AutoModelForAudioFrameClassification, head_doc="audio frame (token) classification"
+)
+
+
+class AutoModelForAudioXVector(_BaseAutoModelClass):
+    _model_mapping = MODEL_FOR_AUDIO_XVECTOR_MAPPING
+
+
+AutoModelForAudioXVector = auto_class_update(AutoModelForAudioXVector, head_doc="audio retrieval via x-vector")
+
+
 class AutoModelWithLMHead(_AutoModelWithLMHead):
     @classmethod
     def from_config(cls, config):

src/transformers/models/hubert/modeling_hubert.py

@@ -42,9 +42,9 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "HubertConfig"
 _CHECKPOINT_FOR_DOC = "facebook/hubert-large-ls960-ft"
 _PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
+_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _SEQ_CLASS_CHECKPOINT = "superb/hubert-base-superb-ks"
-_SEQ_CLASS_PROCESSOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _HIDDEN_STATES_START_POSITION = 1
 
@@ -1182,7 +1182,7 @@ class HubertForSequenceClassification(HubertPreTrainedModel):
 
     @add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
-        processor_class=_SEQ_CLASS_PROCESSOR_FOR_DOC,
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_SEQ_CLASS_CHECKPOINT,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,

src/transformers/models/sew/modeling_sew.py

@@ -38,9 +38,9 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "SEWConfig"
 _CHECKPOINT_FOR_DOC = "asapp/sew-tiny-100k"
 _PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
+_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _SEQ_CLASS_CHECKPOINT = "asapp/sew-tiny-100k"
-_SEQ_CLASS_PROCESSOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _HIDDEN_STATES_START_POSITION = 1
 
@@ -1067,7 +1067,7 @@ class SEWForSequenceClassification(SEWPreTrainedModel):
 
     @add_start_docstrings_to_model_forward(SEW_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
-        processor_class=_SEQ_CLASS_PROCESSOR_FOR_DOC,
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_SEQ_CLASS_CHECKPOINT,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,

src/transformers/models/sew_d/modeling_sew_d.py

@@ -39,9 +39,9 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "SEWDConfig"
 _CHECKPOINT_FOR_DOC = "asapp/sew-d-tiny-100k"
 _PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
+_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _SEQ_CLASS_CHECKPOINT = "asapp/sew-d-tiny-100k"
-_SEQ_CLASS_PROCESSOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _HIDDEN_STATES_START_POSITION = 1
 
@@ -1598,7 +1598,7 @@ class SEWDForSequenceClassification(SEWDPreTrainedModel):
 
     @add_start_docstrings_to_model_forward(SEWD_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
-        processor_class=_SEQ_CLASS_PROCESSOR_FOR_DOC,
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_SEQ_CLASS_CHECKPOINT,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,

src/transformers/models/unispeech/modeling_unispeech.py

@@ -44,9 +44,9 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "UniSpeechConfig"
 _PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
 _CHECKPOINT_FOR_DOC = "microsoft/unispeech-large-1500h-cv"
+_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _SEQ_CLASS_CHECKPOINT = "microsoft/unispeech-large-1500h-cv"
-_SEQ_CLASS_PROCESSOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _HIDDEN_STATES_START_POSITION = 2
 
@@ -1481,7 +1481,7 @@ class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
 
     @add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
-        processor_class=_SEQ_CLASS_PROCESSOR_FOR_DOC,
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_SEQ_CLASS_CHECKPOINT,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,

src/transformers/models/unispeech_sat/__init__.py

@@ -27,9 +27,11 @@ _import_structure = {
 if is_torch_available():
     _import_structure["modeling_unispeech_sat"] = [
         "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "UniSpeechSatForAudioFrameClassification",
         "UniSpeechSatForCTC",
         "UniSpeechSatForPreTraining",
         "UniSpeechSatForSequenceClassification",
+        "UniSpeechSatForXVector",
         "UniSpeechSatModel",
         "UniSpeechSatPreTrainedModel",
     ]
@@ -40,9 +42,11 @@ if TYPE_CHECKING:
     if is_torch_available():
         from .modeling_unispeech_sat import (
             UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            UniSpeechSatForAudioFrameClassification,
             UniSpeechSatForCTC,
             UniSpeechSatForPreTraining,
             UniSpeechSatForSequenceClassification,
+            UniSpeechSatForXVector,
             UniSpeechSatModel,
             UniSpeechSatPreTrainedModel,
         )

src/transformers/models/unispeech_sat/configuration_unispeech_sat.py

@@ -153,6 +153,17 @@ class UniSpeechSatConfig(PretrainedConfig):
             instance of :class:`~transformers.UniSpeechSatForSequenceClassification`.
         classifier_proj_size (:obj:`int`, `optional`, defaults to 256):
             Dimensionality of the projection before token mean-pooling for classification.
+        tdnn_dim (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(512, 512, 512, 512, 1500)`):
+            A tuple of integers defining the number of output channels of each 1D convolutional layer in the `TDNN`
+            module of the `XVector` model. The length of `tdnn_dim` defines the number of `TDNN` layers.
+        tdnn_kernel (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 3, 3, 1, 1)`):
+            A tuple of integers defining the kernel size of each 1D convolutional layer in the `TDNN` module of the
+            `XVector` model. The length of `tdnn_kernel` has to match the length of `tdnn_dim`.
+        tdnn_dilation (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(1, 2, 3, 1, 1)`):
+            A tuple of integers defining the dilation factor of each 1D convolutional layer in `TDNN` module of the
+            `XVector` model. The length of `tdnn_dilation` has to match the length of `tdnn_dim`.
+        xvector_output_dim (:obj:`int`, `optional`, defaults to 512):
+            Dimensionality of the `XVector` embedding vectors.
 
     Example::
 
@@ -213,6 +224,10 @@ class UniSpeechSatConfig(PretrainedConfig):
         ctc_zero_infinity=False,
         use_weighted_layer_sum=False,
         classifier_proj_size=256,
+        tdnn_dim=(512, 512, 512, 512, 1500),
+        tdnn_kernel=(5, 3, 3, 1, 1),
+        tdnn_dilation=(1, 2, 3, 1, 1),
+        xvector_output_dim=512,
         pad_token_id=0,
         bos_token_id=1,
         eos_token_id=2,
@@ -246,7 +261,6 @@ class UniSpeechSatConfig(PretrainedConfig):
         self.num_clusters = num_clusters
         self.do_stable_layer_norm = do_stable_layer_norm
         self.use_weighted_layer_sum = use_weighted_layer_sum
-        self.classifier_proj_size = classifier_proj_size
 
         if (
             (len(self.conv_stride) != self.num_feat_extract_layers)
@@ -282,3 +296,12 @@ class UniSpeechSatConfig(PretrainedConfig):
         # ctc loss
         self.ctc_loss_reduction = ctc_loss_reduction
         self.ctc_zero_infinity = ctc_zero_infinity
+
+        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
+        self.classifier_proj_size = classifier_proj_size
+
+        # XVector-specific parameters. Feel free to ignore for other classes.
+        self.tdnn_dim = list(tdnn_dim)
+        self.tdnn_kernel = list(tdnn_kernel)
+        self.tdnn_dilation = list(tdnn_dilation)
+        self.xvector_output_dim = xvector_output_dim

src/transformers/models/unispeech_sat/convert_unispeech_original_s3prl_checkpoint_to_pytorch.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert Hubert checkpoint."""
+
+
+import argparse
+
+import torch
+
+from transformers import (
+    UniSpeechSatConfig,
+    UniSpeechSatForAudioFrameClassification,
+    UniSpeechSatForSequenceClassification,
+    UniSpeechSatForXVector,
+    Wav2Vec2FeatureExtractor,
+    logging,
+)
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def convert_classification(base_model_name, hf_config, downstream_dict):
+    model = UniSpeechSatForSequenceClassification.from_pretrained(base_model_name, config=hf_config)
+    model.projector.weight.data = downstream_dict["projector.weight"]
+    model.projector.bias.data = downstream_dict["projector.bias"]
+    model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"]
+    model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"]
+    return model
+
+
+def convert_diarization(base_model_name, hf_config, downstream_dict):
+    model = UniSpeechSatForAudioFrameClassification.from_pretrained(base_model_name, config=hf_config)
+    model.classifier.weight.data = downstream_dict["model.linear.weight"]
+    model.classifier.bias.data = downstream_dict["model.linear.bias"]
+    return model
+
+
+def convert_xvector(base_model_name, hf_config, downstream_dict):
+    model = UniSpeechSatForXVector.from_pretrained(base_model_name, config=hf_config)
+    model.projector.weight.data = downstream_dict["connector.weight"]
+    model.projector.bias.data = downstream_dict["connector.bias"]
+    for i, kernel_size in enumerate(hf_config.tdnn_kernel):
+        model.tdnn[i].kernel.weight.data = downstream_dict[
+            f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
+        ]
+        model.tdnn[i].kernel.bias.data = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
+
+    model.feature_extractor.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
+    model.feature_extractor.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
+    model.classifier.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
+    model.classifier.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
+    model.objective.weight.data = downstream_dict["objective.W"]
+    return model
+
+
+@torch.no_grad()
+def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, model_dump_path):
+    """
+    Copy/paste/tweak model's weights to transformers design.
+    """
+    checkpoint = torch.load(checkpoint_path, map_location="cpu")
+
+    downstream_dict = checkpoint["Downstream"]
+
+    hf_config = UniSpeechSatConfig.from_pretrained(config_path)
+    hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
+        base_model_name, return_attention_mask=True, do_normalize=False
+    )
+
+    arch = hf_config.architectures[0]
+    if arch.endswith("ForSequenceClassification"):
+        hf_model = convert_classification(base_model_name, hf_config, downstream_dict)
+    elif arch.endswith("ForAudioFrameClassification"):
+        hf_model = convert_diarization(base_model_name, hf_config, downstream_dict)
+    elif arch.endswith("ForXVector"):
+        hf_model = convert_xvector(base_model_name, hf_config, downstream_dict)
+    else:
+        raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}")
+
+    if hf_config.use_weighted_layer_sum:
+        hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"]
+
+    hf_feature_extractor.save_pretrained(model_dump_path)
+    hf_model.save_pretrained(model_dump_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
+    )
+    parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
+    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
+    parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
+    args = parser.parse_args()
+    convert_s3prl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -33,7 +33,7 @@ from ...file_utils import (
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
+from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput, TokenClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
 from .configuration_unispeech_sat import UniSpeechSatConfig
@@ -45,9 +45,11 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "UniSpeechSatConfig"
 _PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
 _CHECKPOINT_FOR_DOC = "microsoft/unispeech-sat-base-plus"
+_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _SEQ_CLASS_CHECKPOINT = "microsoft/unispeech-sat-base-plus"
-_SEQ_CLASS_PROCESSOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
+_FRAME_CLASS_CHECKPOINT = "microsoft/unispeech-sat-base-plus-sd"
+_XVECTOR_CHECKPOINT = "microsoft/unispeech-sat-base-plus-sv"
 
 _HIDDEN_STATES_START_POSITION = 2
 
@@ -123,6 +125,38 @@ class UniSpeechSatForPreTrainingOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class XVectorOutput(ModelOutput):
+    """
+    Output type of :class:`~transformers.Wav2Vec2ForXVector`.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.xvector_output_dim)`):
+            Classification hidden states before AMSoftmax.
+        embeddings (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.xvector_output_dim)`):
+            Utterance embeddings used for vector similarity-based retrieval.
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    embeddings: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
 # Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
 def _compute_mask_indices(
     shape: Tuple[int, int],
@@ -1472,7 +1506,7 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
 
     @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
-        processor_class=_SEQ_CLASS_PROCESSOR_FOR_DOC,
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_SEQ_CLASS_CHECKPOINT,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
@@ -1538,3 +1572,285 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    UniSpeech-SAT Model with a frame classification head on top for tasks like Speaker Diarization.
+    """,
+    UNISPEECH_SAT_START_DOCSTRING,
+)
+# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat, WAV_2_VEC_2->UNISPEECH_SAT
+class UniSpeechSatForAudioFrameClassification(UniSpeechSatPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.unispeech_sat = UniSpeechSatModel(config)
+        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
+        if config.use_weighted_layer_sum:
+            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.unispeech_sat.feature_extractor._freeze_parameters()
+
+    def freeze_base_model(self):
+        """
+        Calling this function will disable the gradient computation for the base model so that its parameters will not
+        be updated during training. Only the classification head will be updated.
+        """
+        for param in self.unispeech_sat.parameters():
+            param.requires_grad = False
+
+    @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
+        checkpoint=_FRAME_CLASS_CHECKPOINT,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        modality="audio",
+    )
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+        outputs = self.unispeech_sat(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.config.use_weighted_layer_sum:
+            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
+            hidden_states = torch.stack(hidden_states, dim=1)
+            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+        else:
+            hidden_states = outputs[0]
+
+        logits = self.classifier(hidden_states)
+
+        if not return_dict:
+            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
+            return output
+
+        return TokenClassifierOutput(
+            loss=None,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# Copied from transformers.models.wav2vec2.modeling_wav2vec2.AMSoftmaxLoss
+class AMSoftmaxLoss(nn.Module):
+    def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
+        super(AMSoftmaxLoss, self).__init__()
+        self.scale = scale
+        self.margin = margin
+        self.num_labels = num_labels
+        self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True)
+        self.loss = nn.CrossEntropyLoss()
+
+    def forward(self, hidden_states, labels):
+        labels = labels.flatten()
+        weight = nn.functional.normalize(self.weight, dim=0)
+        hidden_states = nn.functional.normalize(hidden_states, dim=1)
+        cos_theta = torch.mm(hidden_states, weight)
+        psi = cos_theta - self.margin
+
+        onehot = nn.functional.one_hot(labels, self.num_labels)
+        logits = self.scale * torch.where(onehot.bool(), psi, cos_theta)
+        loss = self.loss(logits, labels)
+
+        return loss
+
+
+# Copied from transformers.models.wav2vec2.modeling_wav2vec2.TDNNLayer
+class TDNNLayer(nn.Module):
+    def __init__(self, config, layer_id=0):
+        super().__init__()
+        self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
+        self.out_conv_dim = config.tdnn_dim[layer_id]
+        self.kernel_size = config.tdnn_kernel[layer_id]
+        self.dilation = config.tdnn_dilation[layer_id]
+
+        self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
+        self.activation = nn.ReLU()
+
+    def forward(self, hidden_states):
+        hidden_states = hidden_states.unsqueeze(1)
+        hidden_states = nn.functional.unfold(
+            hidden_states,
+            (self.kernel_size, self.in_conv_dim),
+            stride=(1, self.in_conv_dim),
+            dilation=(self.dilation, 1),
+        )
+        hidden_states = hidden_states.transpose(1, 2)
+        hidden_states = self.kernel(hidden_states)
+
+        hidden_states = self.activation(hidden_states)
+        return hidden_states
+
+
+@add_start_docstrings(
+    """
+    UniSpeech-SAT Model with an XVector feature extraction head on top for tasks like Speaker Verification.
+    """,
+    UNISPEECH_SAT_START_DOCSTRING,
+)
+# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat, WAV_2_VEC_2->UNISPEECH_SAT
+class UniSpeechSatForXVector(UniSpeechSatPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.unispeech_sat = UniSpeechSatModel(config)
+        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
+        if config.use_weighted_layer_sum:
+            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
+        self.projector = nn.Linear(config.hidden_size, config.tdnn_dim[0])
+
+        tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))]
+        self.tdnn = nn.ModuleList(tdnn_layers)
+
+        self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim)
+        self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim)
+
+        self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels)
+
+        self.init_weights()
+
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.unispeech_sat.feature_extractor._freeze_parameters()
+
+    def freeze_base_model(self):
+        """
+        Calling this function will disable the gradient computation for the base model so that its parameters will not
+        be updated during training. Only the classification head will be updated.
+        """
+        for param in self.unispeech_sat.parameters():
+            param.requires_grad = False
+
+    def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
+        """
+        Computes the output length of the TDNN layers
+        """
+
+        def _conv_out_length(input_length, kernel_size, stride):
+            # 1D convolutional layer output length formula taken
+            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
+            return (input_length - kernel_size) // stride + 1
+
+        for kernel_size in self.config.tdnn_kernel:
+            input_lengths = _conv_out_length(input_lengths, kernel_size, 1)
+
+        return input_lengths
+
+    @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
+        checkpoint=_XVECTOR_CHECKPOINT,
+        output_type=XVectorOutput,
+        config_class=_CONFIG_FOR_DOC,
+        modality="audio",
+    )
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+        outputs = self.unispeech_sat(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.config.use_weighted_layer_sum:
+            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
+            hidden_states = torch.stack(hidden_states, dim=1)
+            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+        else:
+            hidden_states = outputs[0]
+
+        hidden_states = self.projector(hidden_states)
+
+        for tdnn_layer in self.tdnn:
+            hidden_states = tdnn_layer(hidden_states)
+
+        # Statistic Pooling
+        if attention_mask is None:
+            mean_features = hidden_states.mean(dim=1)
+            std_features = hidden_states.std(dim=1)
+        else:
+            feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1))
+            tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths)
+            mean_features = []
+            std_features = []
+            for i, length in enumerate(tdnn_output_lengths):
+                mean_features.append(hidden_states[i, :length].mean(dim=0))
+                std_features.append(hidden_states[i, :length].std(dim=0))
+            mean_features = torch.stack(mean_features)
+            std_features = torch.stack(std_features)
+        statistic_pooling = torch.cat([mean_features, std_features], dim=-1)
+
+        output_embeddings = self.feature_extractor(statistic_pooling)
+        logits = self.classifier(output_embeddings)
+
+        loss = None
+        if labels is not None:
+            loss = self.objective(logits, labels)
+
+        if not return_dict:
+            output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:]
+            return ((loss,) + output) if loss is not None else output
+
+        return XVectorOutput(
+            loss=loss,
+            logits=logits,
+            embeddings=output_embeddings,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/models/wav2vec2/__init__.py

@@ -31,10 +31,12 @@ _import_structure = {
 if is_torch_available():
     _import_structure["modeling_wav2vec2"] = [
         "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "Wav2Vec2ForAudioFrameClassification",
         "Wav2Vec2ForCTC",
         "Wav2Vec2ForMaskedLM",
         "Wav2Vec2ForPreTraining",
         "Wav2Vec2ForSequenceClassification",
+        "Wav2Vec2ForXVector",
         "Wav2Vec2Model",
         "Wav2Vec2PreTrainedModel",
     ]
@@ -65,10 +67,12 @@ if TYPE_CHECKING:
     if is_torch_available():
         from .modeling_wav2vec2 import (
             WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Wav2Vec2ForAudioFrameClassification,
             Wav2Vec2ForCTC,
             Wav2Vec2ForMaskedLM,
             Wav2Vec2ForPreTraining,
             Wav2Vec2ForSequenceClassification,
+            Wav2Vec2ForXVector,
             Wav2Vec2Model,
             Wav2Vec2PreTrainedModel,
         )

src/transformers/models/wav2vec2/configuration_wav2vec2.py

@@ -80,10 +80,10 @@ class Wav2Vec2Config(PretrainedConfig):
             feature extractor. The length of `conv_dim` defines the number of 1D convolutional layers.
         conv_stride (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 2, 2, 2, 2, 2, 2)`):
             A tuple of integers defining the stride of each 1D convolutional layer in the feature extractor. The length
-            of `conv_stride` defines the number of convolutional layers and has to match the the length of `conv_dim`.
+            of `conv_stride` defines the number of convolutional layers and has to match the length of `conv_dim`.
         conv_kernel (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(10, 3, 3, 3, 3, 3, 3)`):
             A tuple of integers defining the kernel size of each 1D convolutional layer in the feature extractor. The
-            length of `conv_kernel` defines the number of convolutional layers and has to match the the length of
+            length of `conv_kernel` defines the number of convolutional layers and has to match the length of
             `conv_dim`.
         conv_bias (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether the 1D convolutional layers have a bias.
@@ -153,6 +153,17 @@ class Wav2Vec2Config(PretrainedConfig):
             instance of :class:`~transformers.Wav2Vec2ForSequenceClassification`.
         classifier_proj_size (:obj:`int`, `optional`, defaults to 256):
             Dimensionality of the projection before token mean-pooling for classification.
+        tdnn_dim (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(512, 512, 512, 512, 1500)`):
+            A tuple of integers defining the number of output channels of each 1D convolutional layer in the `TDNN`
+            module of the `XVector` model. The length of `tdnn_dim` defines the number of `TDNN` layers.
+        tdnn_kernel (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 3, 3, 1, 1)`):
+            A tuple of integers defining the kernel size of each 1D convolutional layer in the `TDNN` module of the
+            `XVector` model. The length of `tdnn_kernel` has to match the length of `tdnn_dim`.
+        tdnn_dilation (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(1, 2, 3, 1, 1)`):
+            A tuple of integers defining the dilation factor of each 1D convolutional layer in `TDNN` module of the
+            `XVector` model. The length of `tdnn_dilation` has to match the length of `tdnn_dim`.
+        xvector_output_dim (:obj:`int`, `optional`, defaults to 512):
+            Dimensionality of the `XVector` embedding vectors.
         add_adapter (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether a convolutional network should be stacked on top of the Wav2Vec2 Encoder. Can be very useful for
             warm-starting Wav2Vec2 for SpeechEncoderDecoder models.
@@ -226,6 +237,10 @@ class Wav2Vec2Config(PretrainedConfig):
         ctc_zero_infinity=False,
         use_weighted_layer_sum=False,
         classifier_proj_size=256,
+        tdnn_dim=(512, 512, 512, 512, 1500),
+        tdnn_kernel=(5, 3, 3, 1, 1),
+        tdnn_dilation=(1, 2, 3, 1, 1),
+        xvector_output_dim=512,
         pad_token_id=0,
         bos_token_id=1,
         eos_token_id=2,
@@ -262,7 +277,6 @@ class Wav2Vec2Config(PretrainedConfig):
         self.vocab_size = vocab_size
         self.do_stable_layer_norm = do_stable_layer_norm
         self.use_weighted_layer_sum = use_weighted_layer_sum
-        self.classifier_proj_size = classifier_proj_size
 
         if (
             (len(self.conv_stride) != self.num_feat_extract_layers)
@@ -305,3 +319,12 @@ class Wav2Vec2Config(PretrainedConfig):
         self.adapter_stride = adapter_stride
         self.num_adapter_layers = num_adapter_layers
         self.output_hidden_size = output_hidden_size or hidden_size
+
+        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
+        self.classifier_proj_size = classifier_proj_size
+
+        # XVector-specific parameters. Feel free to ignore for other classes.
+        self.tdnn_dim = list(tdnn_dim)
+        self.tdnn_kernel = list(tdnn_kernel)
+        self.tdnn_dilation = list(tdnn_dilation)
+        self.xvector_output_dim = xvector_output_dim

src/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py

@@ -19,13 +19,52 @@ import argparse
 
 import torch
 
-from transformers import Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification, logging
+from transformers import (
+    Wav2Vec2Config,
+    Wav2Vec2FeatureExtractor,
+    Wav2Vec2ForAudioFrameClassification,
+    Wav2Vec2ForSequenceClassification,
+    Wav2Vec2ForXVector,
+    logging,
+)
 
 
 logging.set_verbosity_info()
 logger = logging.get_logger(__name__)
 
-SUPPORTED_MODELS = ["UtteranceLevel"]
+
+def convert_classification(base_model_name, hf_config, downstream_dict):
+    model = Wav2Vec2ForSequenceClassification.from_pretrained(base_model_name, config=hf_config)
+    model.projector.weight.data = downstream_dict["projector.weight"]
+    model.projector.bias.data = downstream_dict["projector.bias"]
+    model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"]
+    model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"]
+    return model
+
+
+def convert_diarization(base_model_name, hf_config, downstream_dict):
+    model = Wav2Vec2ForAudioFrameClassification.from_pretrained(base_model_name, config=hf_config)
+    model.classifier.weight.data = downstream_dict["model.linear.weight"]
+    model.classifier.bias.data = downstream_dict["model.linear.bias"]
+    return model
+
+
+def convert_xvector(base_model_name, hf_config, downstream_dict):
+    model = Wav2Vec2ForXVector.from_pretrained(base_model_name, config=hf_config)
+    model.projector.weight.data = downstream_dict["connector.weight"]
+    model.projector.bias.data = downstream_dict["connector.bias"]
+    for i, kernel_size in enumerate(hf_config.tdnn_kernel):
+        model.tdnn[i].kernel.weight.data = downstream_dict[
+            f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
+        ]
+        model.tdnn[i].kernel.bias.data = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
+
+    model.feature_extractor.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
+    model.feature_extractor.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
+    model.classifier.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
+    model.classifier.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
+    model.objective.weight.data = downstream_dict["objective.W"]
+    return model
 
 
 @torch.no_grad()
@@ -34,25 +73,27 @@ def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, mode
     Copy/paste/tweak model's weights to transformers design.
     """
     checkpoint = torch.load(checkpoint_path, map_location="cpu")
-    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
-        raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}")
 
     downstream_dict = checkpoint["Downstream"]
 
-    hf_congfig = Wav2Vec2Config.from_pretrained(config_path)
-    hf_model = Wav2Vec2ForSequenceClassification.from_pretrained(base_model_name, config=hf_congfig)
+    hf_config = Wav2Vec2Config.from_pretrained(config_path)
     hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
         base_model_name, return_attention_mask=True, do_normalize=False
     )
 
-    if hf_congfig.use_weighted_layer_sum:
+    arch = hf_config.architectures[0]
+    if arch.endswith("ForSequenceClassification"):
+        hf_model = convert_classification(base_model_name, hf_config, downstream_dict)
+    elif arch.endswith("ForAudioFrameClassification"):
+        hf_model = convert_diarization(base_model_name, hf_config, downstream_dict)
+    elif arch.endswith("ForXVector"):
+        hf_model = convert_xvector(base_model_name, hf_config, downstream_dict)
+    else:
+        raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}")
+
+    if hf_config.use_weighted_layer_sum:
         hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"]
 
-    hf_model.projector.weight.data = downstream_dict["projector.weight"]
-    hf_model.projector.bias.data = downstream_dict["projector.bias"]
-    hf_model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"]
-    hf_model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"]
-
     hf_feature_extractor.save_pretrained(model_dump_path)
     hf_model.save_pretrained(model_dump_path)
 

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -34,7 +34,13 @@ from ...file_utils import (
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from ...modeling_outputs import BaseModelOutput, CausalLMOutput, MaskedLMOutput, SequenceClassifierOutput
+from ...modeling_outputs import (
+    BaseModelOutput,
+    CausalLMOutput,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
 from .configuration_wav2vec2 import Wav2Vec2Config
@@ -45,9 +51,11 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "Wav2Vec2Config"
 _CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h"
 _PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
+_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
 
 _SEQ_CLASS_CHECKPOINT = "superb/wav2vec2-base-superb-ks"
-_SEQ_CLASS_PROCESSOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
+_FRAME_CLASS_CHECKPOINT = "superb/wav2vec2-base-superb-sd"
+_XVECTOR_CHECKPOINT = "superb/wav2vec2-base-superb-sv"
 
 _HIDDEN_STATES_START_POSITION = 2
 
@@ -93,7 +101,7 @@ class Wav2Vec2BaseModelOutput(ModelOutput):
 @dataclass
 class Wav2Vec2ForPreTrainingOutput(ModelOutput):
     """
-    Output type of :class:`~transformers.Wav2Vec2ForPreTrainingOutput`, with potential hidden states and attentions.
+    Output type of :class:`~transformers.Wav2Vec2ForPreTraining`, with potential hidden states and attentions.
 
     Args:
         loss (`optional`, returned when :obj:`sample_negative_indices` are passed, ``torch.FloatTensor`` of shape :obj:`(1,)`):
@@ -132,6 +140,38 @@ class Wav2Vec2ForPreTrainingOutput(ModelOutput):
     diversity_loss: Optional[torch.FloatTensor] = None
 
 
+@dataclass
+class XVectorOutput(ModelOutput):
+    """
+    Output type of :class:`~transformers.Wav2Vec2ForXVector`.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.xvector_output_dim)`):
+            Classification hidden states before AMSoftmax.
+        embeddings (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.xvector_output_dim)`):
+            Utterance embeddings used for vector similarity-based retrieval.
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    embeddings: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
 def _compute_mask_indices(
     shape: Tuple[int, int],
     mask_prob: float,
@@ -1707,7 +1747,7 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
 
     @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
-        processor_class=_SEQ_CLASS_PROCESSOR_FOR_DOC,
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
         checkpoint=_SEQ_CLASS_CHECKPOINT,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
@@ -1773,3 +1813,281 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    Wav2Vec2 Model with a frame classification head on top for tasks like Speaker Diarization.
+    """,
+    WAV_2_VEC_2_START_DOCSTRING,
+)
+class Wav2Vec2ForAudioFrameClassification(Wav2Vec2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.wav2vec2 = Wav2Vec2Model(config)
+        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
+        if config.use_weighted_layer_sum:
+            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.wav2vec2.feature_extractor._freeze_parameters()
+
+    def freeze_base_model(self):
+        """
+        Calling this function will disable the gradient computation for the base model so that its parameters will not
+        be updated during training. Only the classification head will be updated.
+        """
+        for param in self.wav2vec2.parameters():
+            param.requires_grad = False
+
+    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
+        checkpoint=_FRAME_CLASS_CHECKPOINT,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        modality="audio",
+    )
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+        outputs = self.wav2vec2(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.config.use_weighted_layer_sum:
+            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
+            hidden_states = torch.stack(hidden_states, dim=1)
+            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+        else:
+            hidden_states = outputs[0]
+
+        logits = self.classifier(hidden_states)
+
+        if not return_dict:
+            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
+            return output
+
+        return TokenClassifierOutput(
+            loss=None,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class AMSoftmaxLoss(nn.Module):
+    def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
+        super(AMSoftmaxLoss, self).__init__()
+        self.scale = scale
+        self.margin = margin
+        self.num_labels = num_labels
+        self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True)
+        self.loss = nn.CrossEntropyLoss()
+
+    def forward(self, hidden_states, labels):
+        labels = labels.flatten()
+        weight = nn.functional.normalize(self.weight, dim=0)
+        hidden_states = nn.functional.normalize(hidden_states, dim=1)
+        cos_theta = torch.mm(hidden_states, weight)
+        psi = cos_theta - self.margin
+
+        onehot = nn.functional.one_hot(labels, self.num_labels)
+        logits = self.scale * torch.where(onehot.bool(), psi, cos_theta)
+        loss = self.loss(logits, labels)
+
+        return loss
+
+
+class TDNNLayer(nn.Module):
+    def __init__(self, config, layer_id=0):
+        super().__init__()
+        self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
+        self.out_conv_dim = config.tdnn_dim[layer_id]
+        self.kernel_size = config.tdnn_kernel[layer_id]
+        self.dilation = config.tdnn_dilation[layer_id]
+
+        self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
+        self.activation = nn.ReLU()
+
+    def forward(self, hidden_states):
+        hidden_states = hidden_states.unsqueeze(1)
+        hidden_states = nn.functional.unfold(
+            hidden_states,
+            (self.kernel_size, self.in_conv_dim),
+            stride=(1, self.in_conv_dim),
+            dilation=(self.dilation, 1),
+        )
+        hidden_states = hidden_states.transpose(1, 2)
+        hidden_states = self.kernel(hidden_states)
+
+        hidden_states = self.activation(hidden_states)
+        return hidden_states
+
+
+@add_start_docstrings(
+    """
+    Wav2Vec2 Model with an XVector feature extraction head on top for tasks like Speaker Verification.
+    """,
+    WAV_2_VEC_2_START_DOCSTRING,
+)
+class Wav2Vec2ForXVector(Wav2Vec2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.wav2vec2 = Wav2Vec2Model(config)
+        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
+        if config.use_weighted_layer_sum:
+            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
+        self.projector = nn.Linear(config.hidden_size, config.tdnn_dim[0])
+
+        tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))]
+        self.tdnn = nn.ModuleList(tdnn_layers)
+
+        self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim)
+        self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim)
+
+        self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels)
+
+        self.init_weights()
+
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.wav2vec2.feature_extractor._freeze_parameters()
+
+    def freeze_base_model(self):
+        """
+        Calling this function will disable the gradient computation for the base model so that its parameters will not
+        be updated during training. Only the classification head will be updated.
+        """
+        for param in self.wav2vec2.parameters():
+            param.requires_grad = False
+
+    def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
+        """
+        Computes the output length of the TDNN layers
+        """
+
+        def _conv_out_length(input_length, kernel_size, stride):
+            # 1D convolutional layer output length formula taken
+            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
+            return (input_length - kernel_size) // stride + 1
+
+        for kernel_size in self.config.tdnn_kernel:
+            input_lengths = _conv_out_length(input_lengths, kernel_size, 1)
+
+        return input_lengths
+
+    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
+        checkpoint=_XVECTOR_CHECKPOINT,
+        output_type=XVectorOutput,
+        config_class=_CONFIG_FOR_DOC,
+        modality="audio",
+    )
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+        outputs = self.wav2vec2(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.config.use_weighted_layer_sum:
+            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
+            hidden_states = torch.stack(hidden_states, dim=1)
+            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+        else:
+            hidden_states = outputs[0]
+
+        hidden_states = self.projector(hidden_states)
+
+        for tdnn_layer in self.tdnn:
+            hidden_states = tdnn_layer(hidden_states)
+
+        # Statistic Pooling
+        if attention_mask is None:
+            mean_features = hidden_states.mean(dim=1)
+            std_features = hidden_states.std(dim=1)
+        else:
+            feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1))
+            tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths)
+            mean_features = []
+            std_features = []
+            for i, length in enumerate(tdnn_output_lengths):
+                mean_features.append(hidden_states[i, :length].mean(dim=0))
+                std_features.append(hidden_states[i, :length].std(dim=0))
+            mean_features = torch.stack(mean_features)
+            std_features = torch.stack(std_features)
+        statistic_pooling = torch.cat([mean_features, std_features], dim=-1)
+
+        output_embeddings = self.feature_extractor(statistic_pooling)
+        logits = self.classifier(output_embeddings)
+
+        loss = None
+        if labels is not None:
+            loss = self.objective(logits, labels)
+
+        if not return_dict:
+            output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:]
+            return ((loss,) + output) if loss is not None else output
+
+        return XVectorOutput(
+            loss=loss,
+            logits=logits,
+            embeddings=output_embeddings,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -422,6 +422,30 @@ class AutoModelForAudioClassification:
         requires_backends(self, ["torch"])
 
 
+class AutoModelForAudioFrameClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    def forward(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class AutoModelForAudioXVector:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    def forward(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class AutoModelForCausalLM:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
@@ -4896,6 +4920,11 @@ class UniSpeechPreTrainedModel:
 UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class UniSpeechSatForAudioFrameClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class UniSpeechSatForCTC:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
@@ -4918,6 +4947,11 @@ class UniSpeechSatForSequenceClassification:
         requires_backends(self, ["torch"])
 
 
+class UniSpeechSatForXVector:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class UniSpeechSatModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
@@ -5072,6 +5106,11 @@ class ViTPreTrainedModel:
 WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class Wav2Vec2ForAudioFrameClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class Wav2Vec2ForCTC:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
@@ -5106,6 +5145,11 @@ class Wav2Vec2ForSequenceClassification:
         requires_backends(self, ["torch"])
 
 
+class Wav2Vec2ForXVector:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class Wav2Vec2Model:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])