Add Wav2Vec2 base model (#4513)

Summary:
Pull Request resolved: #4513

As titled.

Reviewed By: zonglinpengmeta

Differential Revision: D60619295

fbshipit-source-id: 00fd48029bc2413cf2a4a1453c80bbf65d29c57f

examples/cadence/models/wav2vec2.py

@@ -0,0 +1,65 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Example script for exporting simple models to flatbuffer
+
+import logging
+
+from executorch.backends.cadence.aot.ops_registrations import *  # noqa
+
+import torch
+
+from executorch.backends.cadence.aot.export_example import export_model
+from torchaudio.models.wav2vec2.model import wav2vec2_model, Wav2Vec2Model
+
+FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+
+
+def main() -> None:
+    # The wrapper is needed to avoid issues with the optional second arguments
+    # of Wav2Vec2Models.
+    class Wav2Vec2ModelWrapper(torch.nn.Module):
+        def __init__(self, model: Wav2Vec2Model):
+            super().__init__()
+            self.model = model
+
+        def forward(self, x):
+            out, _ = self.model(x)
+            return out
+
+    _model = wav2vec2_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=768,
+        encoder_projection_dropout=0.1,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=12,
+        encoder_num_heads=12,
+        encoder_attention_dropout=0.1,
+        encoder_ff_interm_features=3072,
+        encoder_ff_interm_dropout=0.0,
+        encoder_dropout=0.1,
+        encoder_layer_norm_first=False,
+        encoder_layer_drop=0.1,
+        aux_num_out=None,
+    )
+    _model.eval()
+
+    model = Wav2Vec2ModelWrapper(_model)
+    model.eval()
+
+    # test input
+    audio_len = 1680
+    example_inputs = (torch.rand(1, audio_len),)
+
+    export_model(model, example_inputs)
+
+
+if __name__ == "__main__":
+    main()