base gradio

app.py

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+import math
+import json
+import os
+import numpy as np
+import gradio as gr
+from tensorflow import keras
+from huggingface_hub import hf_hub_download
+import librosa
+
+
+# Download the model
+model_path = hf_hub_download(repo_id='ruben09/music_genre_classification', filename='music_genre_model.h5')
+
+# Load the model
+model = keras.models.load_model(model_path)
+
+def process_audio(audio_file):
+  map_labels = ["blues", "classical", "country", "disco", "hiphop", "jazz", "metal", "pop", "reggae", "rock"]
+  SR = 22050
+  TD = 30
+  SPT = SR * TD
+  num_segments = 3
+  n_fft=2048
+  hop_length=512
+
+  summed_predictions = np.zeros(len(map_labels))
+
+  sample_per_segment = int(SPT / num_segments)
+  num_spectrogram_per_segment = math.ceil(sample_per_segment / hop_length)
+
+  signal, sr = librosa.load(audio_file, sr=SR)
+
+  for d in range(num_segments):
+    start = sample_per_segment * d
+    finish = start + sample_per_segment
+
+    spectrogram = librosa.feature.mfcc(y=signal[start:finish], sr=sr, n_mfcc=13, n_fft=n_fft, hop_length=hop_length)
+    spectrogram_db = spectrogram.T
+    if len(spectrogram_db) == num_spectrogram_per_segment:
+      input_data = np.array(spectrogram_db)
+      input_data = input_data[None,..., np.newaxis]
+      input_data = np.transpose(input_data, (0, 2, 1, 3))
+      prediction = model.predict(input_data)
+      summed_predictions += prediction[0]
+
+  averaged_predictions = summed_predictions / num_segments
+
+  # Get the final prediction (the class with the highest probability)
+  final_prediction_idx = np.argmax(averaged_predictions)
+  final_class_label = map_labels[final_prediction_idx]
+  final_probability = averaged_predictions[final_prediction_idx]
+
+  # Format the result as a dictionary
+  result = {
+      "final_prediction": final_class_label,
+      "confidence": round(float(final_probability), 2),
+      "all_probabilities": {map_labels[i]: round(float(prob), 2) for i, prob in enumerate(averaged_predictions)}
+  }
+
+  return result
+
+iface = gr.Interface(
+    fn=process_audio,  # The function to process the uploaded audio
+    inputs=gr.Audio(type="filepath", label="Upload Audio (WAV, MP3, FLAC)"),  # Accept audio input
+    outputs="json",  # Return predictions as text
+    title="Audio Classification",  # Title of the interface
+    description="Upload an audio file (max 30 seconds) to get a genre classification."
+)
+
+# Launch the Gradio app
+iface.launch()

requirements.txt

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+gradio
+tensorflow
+huggingface_hub
+librosa