'Audio-Spectrogram-Transformer' example added (#134)

* 'Audio-Spectrogram-Transformer'

* File format correct

* test_compile.py corrected

examples/Audio-Spectrogram-Transformer.py

@@ -0,0 +1,57 @@
+#
+# Copyright © 2024 Intel Corporation
+# SPDX-License-Identifier: Apache 2.0
+#
+
+
+import sys
+import subprocess
+import pkg_resources
+
+required = {"librosa", "soundfile", "datasets", "intel-npu-acceleration-library"}
+installed = {pkg.key for pkg in pkg_resources.working_set}
+missing = required - installed
+
+if missing:
+    # implement pip as a subprocess:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", *missing])
+from transformers import AutoFeatureExtractor, ASTForAudioClassification
+from datasets import load_dataset
+import torch
+import intel_npu_acceleration_library
+
+dataset = load_dataset(
+    "hf-internal-testing/librispeech_asr_demo",
+    "clean",
+    split="validation",
+    trust_remote_code=True,
+)
+dataset = dataset.sort("id")
+sampling_rate = dataset.features["audio"].sampling_rate
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(
+    "MIT/ast-finetuned-audioset-10-10-0.4593"
+)
+model = ASTForAudioClassification.from_pretrained(
+    "MIT/ast-finetuned-audioset-10-10-0.4593"
+)
+print("Compile model for the NPU")
+model = intel_npu_acceleration_library.compile(model)
+
+# audio file is decoded on the fly
+inputs = feature_extractor(
+    dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt"
+)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class_ids = torch.argmax(logits, dim=-1).item()
+predicted_label = model.config.id2label[predicted_class_ids]
+predicted_label
+
+# compute loss - target_label is e.g. "down"
+target_label = model.config.id2label[0]
+inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
+loss = model(**inputs).loss
+print(round(loss.item(), 2))

test/python/test_compile.py

@@ -95,7 +95,7 @@ def test_torch_compile():
         assert str(e.value) == "Windows not yet supported for torch.compile"
     else:
         compiled_model = torch.compile(model, backend="npu")
-        y = compiled_model(x).detach()
+        y = compiled_model(x.to(torch.float32)).detach()
         assert 1 - r2_score(y_ref.numpy(), y.numpy()) < 0.01