[PT FE] Add tests for Speech-Transformer (#20847)

* Add tests for Speech-Transformer

* Update tests/model_hub_tests/torch_tests/test_speech-transformer.py

* Update tests/model_hub_tests/torch_tests/test_speech-transformer.py

tests/model_hub_tests/torch_tests/test_speech-transformer.py

@@ -0,0 +1,72 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import sys
+import tempfile
+import torch
+import pytest
+import subprocess
+
+from models_hub_common.test_convert_model import TestConvertModel
+from openvino import convert_model
+
+
+# To make tests reproducible we seed the random generator
+torch.manual_seed(0)
+
+
+class TestSpeechTransformerConvertModel(TestConvertModel):
+    def setup_class(self):
+        self.repo_dir = tempfile.TemporaryDirectory()
+        os.system(
+            f"git clone https://github.com/mvafin/Speech-Transformer.git {self.repo_dir.name}")
+        subprocess.check_call(["git", "checkout", "071eebb7549b66bae2cb93e3391fe99749389456"], cwd=self.repo_dir.name)
+        checkpoint_url = "https://github.com/foamliu/Speech-Transformer/releases/download/v1.0/speech-transformer-cn.pt"
+        subprocess.check_call(["wget", checkpoint_url], cwd=self.repo_dir.name)
+
+    def load_model(self, model_name, model_link):
+        sys.path.append(self.repo_dir.name)
+        from transformer.transformer import Transformer
+
+        filename = os.path.join(self.repo_dir.name, 'speech-transformer-cn.pt')
+        m = Transformer()
+        m.load_state_dict(torch.load(
+            filename, map_location=torch.device('cpu')))
+
+        self.example = (torch.randn(32, 209, 320),
+                        torch.stack(sorted(torch.randint(55, 250, [32]), reverse=True)),
+                        torch.randint(-1, 4232, [32, 20]))
+        self.input = (torch.randn(32, 209, 320),
+                      torch.stack(sorted(torch.randint(55, 400, [32]), reverse=True)),
+                      torch.randint(-1, 4232, [32, 25]))
+        return m
+
+    def get_inputs_info(self, model_obj):
+        return None
+
+    def prepare_inputs(self, inputs_info):
+        return [i.numpy() for i in self.input]
+
+    def convert_model(self, model_obj):
+        m = convert_model(model_obj, example_input=self.example)
+        return m
+
+    def infer_fw_model(self, model_obj, inputs):
+        fw_outputs = model_obj(*[torch.from_numpy(i) for i in inputs])
+        if isinstance(fw_outputs, dict):
+            for k in fw_outputs.keys():
+                fw_outputs[k] = fw_outputs[k].numpy(force=True)
+        elif isinstance(fw_outputs, (list, tuple)):
+            fw_outputs = [o.numpy(force=True) for o in fw_outputs]
+        else:
+            fw_outputs = [fw_outputs.numpy(force=True)]
+        return fw_outputs
+
+    def teardown_class(self):
+        # remove all downloaded files from cache
+        self.repo_dir.cleanup()
+
+    @pytest.mark.nightly
+    def test_convert_model(self, ie_device):
+        self.run("speech-transformer", None, ie_device)