Integrate YAMNet for audio classification

- Added sample audio files: cafe_crowd_talk.wav, miaow_16k.wav, and speech_whistling2.wav
- Implemented YAMNet-based audio classification in classify.py
- Updated requirements.txt to include TensorFlow and dependencies for YAMNet

src/preprocessing/classify.py

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+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+import csv
+
+import matplotlib.pyplot as plt
+from IPython.display import Audio
+from scipy.io import wavfile
+from scipy import signal
+
+model = hub.load('https://tfhub.dev/google/yamnet/1')
+
+#Find the name of the class with the top score when mean-aggregated across frames.
+def class_names_from_csv(class_map_scv_text):
+    """Returns list of class names corresponding to score vector."""
+    class_names = []
+    with tf.io.gfile.GFile(class_map_scv_text) as csvfile:
+        reader = csv.DictReader(csvfile)
+        for row in reader:
+            class_names.append(row['display_name'])
+        
+    return class_names
+
+class_map_path = model.class_map_path().numpy()
+class_names = class_names_from_csv(class_map_path)
+
+# Resample audio to 16K
+def ensure_sample_rate(original_sample_rate, waveform, desired_sample_rate=16000):
+    """Resample waveform if required."""
+    if original_sample_rate != desired_sample_rate:
+        desired_length = int(round(float(len(waveform)) / original_sample_rate * desired_sample_rate))
+        waveform = signal.resample(waveform, desired_length)
+    return desired_sample_rate, waveform
+
+# wav_file_name = 'speech_whistling2.wav'
+wav_file_name = 'cafe_crowd_talk.wav'
+sample_rate, wav_data = wavfile.read(wav_file_name, 'rb')
+sample_rate, wav_data = ensure_sample_rate(sample_rate, wav_data)
+
+# Show some basic information about the audio.
+duration = len(wav_data)/sample_rate
+print(f'Sample rate: {sample_rate} Hz')
+print(f'Total duration: {duration:.2f}s')
+print(f'Size of the input: {len(wav_data)}')
+
+# The wav_data needs to be normalized to values in [-1.0, 1.0]
+waveform = wav_data / tf.int16.max
+
+# Execute the Model
+# Check the output.
+scores, embeddings, spectogram = model(waveform)
+scores_np = scores.numpy()
+spectogram_np = spectogram.numpy()
+infered_class = class_names[scores_np.mean(axis=0).argmax()]
+print(f'The main sound is : {infered_class}')
+
+
+