add predict example

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+    <inspection_tool class="PyInterpreterInspection" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="4">
+            <item index="0" class="java.lang.String" itemvalue="json" />
+            <item index="1" class="java.lang.String" itemvalue="customtkinter" />
+            <item index="2" class="java.lang.String" itemvalue="pytest" />
+            <item index="3" class="java.lang.String" itemvalue="csv" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="SqlDialectInspection" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="SqlNoDataSourceInspection" enabled="false" level="WARNING" enabled_by_default="false" />
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/material_theme_project_new.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="MaterialThemeProjectNewConfig">
+    <option name="metadata">
+      <MTProjectMetadataState>
+        <option name="migrated" value="true" />
+        <option name="pristineConfig" value="false" />
+        <option name="userId" value="78c19164:19192ff6e6a:-7ffe" />
+      </MTProjectMetadataState>
+    </option>
+  </component>
+</project>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12 (astronomy-research-my3dsky-ec1ed317f901)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (astronomy-research-my3dsky-ec1ed317f901)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/music-classifier.iml" filepath="$PROJECT_DIR$/.idea/music-classifier.iml" />
+    </modules>
+  </component>
+</project>

.idea/music-classifier.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

predict-example.py

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+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
+from datasets import load_dataset
+import numpy as np
+import librosa
+import torch
+
+# Paths
+MODEL_DIR = "./wav2vec_trained_model"
+
+# Load the dataset
+dataset = load_dataset("lewtun/music_genres_small")
+
+# Retrieve the label names
+genre_mapping = {}
+for example in dataset["train"]:
+    genre_id = example["genre_id"]
+    genre = example["genre"]
+    if genre_id not in genre_mapping:
+        genre_mapping[genre_id] = genre
+        if len(genre_mapping) == 9:
+            break
+
+print(f"Loading model from {MODEL_DIR}...\n")
+model = Wav2Vec2ForSequenceClassification.from_pretrained("gastoooon/music-classifier")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large")
+
+# Function for preprocessing audio for prediction
+def preprocess_audio(audio_path, target_length=16000 * 180):  # 30 seconds at 16kHz
+    audio_array, sampling_rate = librosa.load(audio_path, sr=16000)
+
+    if len(audio_array) > target_length:
+        audio_array = audio_array[:target_length]
+    else:
+        padding = target_length - len(audio_array)
+        audio_array = np.pad(audio_array, (0, padding), "constant")
+
+    inputs = feature_extractor(audio_array, sampling_rate=16000, return_tensors="pt", padding=True)
+    return inputs
+
+
+# Path to your audio file
+audio_path = "./Nirvana - Come As You Are.wav"
+
+
+# Preprocess audio
+inputs = preprocess_audio(audio_path)
+
+# Predict
+with torch.no_grad():
+    logits = model(**inputs).logits
+    predicted_class = torch.argmax(logits, dim=-1).item()
+
+# Output the result
+print(f"song analized:{audio_path}")
+print(f"Predicted genre: {genre_mapping[predicted_class]}")
+