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

samples/requirements.txt

@@ -0,0 +1,80 @@
+absl-py==2.1.0
+asttokens==3.0.0
+astunparse==1.6.3
+audioread==3.0.1
+certifi==2024.12.14
+cffi==1.17.1
+charset-normalizer==3.4.0
+contourpy==1.3.1
+cycler==0.12.1
+decorator==5.1.1
+executing==2.1.0
+flatbuffers==24.12.23
+fonttools==4.55.3
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.68.1
+h5py==3.12.1
+idna==3.10
+iniconfig==2.0.0
+ipython==8.31.0
+jedi==0.19.2
+joblib==1.4.2
+keras==3.7.0
+kiwisolver==1.4.8
+lazy_loader==0.4
+libclang==18.1.1
+librosa==0.10.2.post1
+llvmlite==0.43.0
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.0
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+ml-dtypes==0.4.1
+msgpack==1.1.0
+namex==0.0.8
+numba==0.60.0
+numpy==2.0.2
+opt_einsum==3.4.0
+optree==0.13.1
+packaging==24.2
+parso==0.8.4
+pexpect==4.9.0
+pillow==11.0.0
+platformdirs==4.3.6
+pluggy==1.5.0
+pooch==1.8.2
+prompt_toolkit==3.0.48
+protobuf==5.29.2
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pycparser==2.22
+Pygments==2.18.0
+pyparsing==3.2.0
+pytest==8.3.4
+python-dateutil==2.9.0.post0
+requests==2.32.3
+rich==13.9.4
+scikit-learn==1.6.0
+scipy==1.14.1
+setuptools==75.6.0
+six==1.17.0
+soundfile==0.12.1
+soxr==0.5.0.post1
+stack-data==0.6.3
+tensorboard==2.18.0
+tensorboard-data-server==0.7.2
+tensorflow==2.18.0
+tensorflow-hub==0.16.1
+termcolor==2.5.0
+tf_keras==2.18.0
+threadpoolctl==3.5.0
+traitlets==5.14.3
+typing_extensions==4.12.2
+urllib3==2.3.0
+wcwidth==0.2.13
+Werkzeug==3.1.3
+wheel==0.45.1
+wrapt==1.17.0

src/preprocessing/classify.py

@@ -0,0 +1,58 @@
+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}')
+
+
+