chore: import upstream snapshot with attribution

tests/collections/asr/decoding/utils.py

@@ -0,0 +1,59 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+import copy
+from contextlib import contextmanager
+
+import librosa
+import torch
+
+from nemo.collections.asr.models import ASRModel
+
+
+@contextmanager
+def preserve_decoding_cfg_and_cpu_device(model: ASRModel):
+    """
+    Context manager to preserve the decoding strategy and device of the model.
+    This is useful for tests that modify the model's decoding strategy or device
+    to avoid side effects or costly model reloading.
+    """
+    backup_decoding_cfg = copy.deepcopy(model.cfg.decoding)
+
+    try:
+        yield
+    finally:
+        model.to(device="cpu")
+        if model.cfg.decoding != backup_decoding_cfg:
+            model.change_decoding_strategy(backup_decoding_cfg)
+
+
+def load_audio(file_path, target_sr=16000) -> tuple[torch.Tensor, int]:
+    audio, sr = librosa.load(file_path, sr=target_sr)
+    return torch.tensor(audio, dtype=torch.float32), sr
+
+
+@contextmanager
+def avoid_sync_operations(device: torch.device):
+    try:
+        if device.type == "cuda":
+            torch.cuda.set_sync_debug_mode(2)  # fail if a blocking operation
+        yield
+    finally:
+        if device.type == "cuda":
+            torch.cuda.set_sync_debug_mode(0)  # default, blocking operations are allowed
+
+
+def make_preprocessor_deterministic(model: ASRModel):
+    model.preprocessor.featurizer.dither = 0.0
+    model.preprocessor.featurizer.pad_to = 0