File format correct

examples/Audio-Spectrogram-Transformer.py

@@ -8,39 +8,50 @@
 import subprocess
 import pkg_resources
 
-required  = {'librosa', 'soundfile','datasets','intel-npu-acceleration-library'} 
+required = {"librosa", "soundfile", "datasets", "intel-npu-acceleration-library"}
 installed = {pkg.key for pkg in pkg_resources.working_set}
-missing   = required - installed
+missing = required - installed
 
 if missing:
     # implement pip as a subprocess:
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', *missing])
+    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 = 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")
+
+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")
-
+inputs = feature_extractor(
+    dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt"
+)
+
 with torch.no_grad():
-   logits = model(**inputs).logits
- 
+    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))
+print(round(loss.item(), 2))