Merge pull request #4 from joelmathewthomas/feature/preprocessing-classify

Implement audio classification using YAMNet in preprocessing pipeline

.python-version

@@ -0,0 +1 @@
+3.12.7

requirements.txt

@@ -1,28 +1,78 @@
+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
+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-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 +1,48 @@
+import tensorflow as tf
+import tensorflow_hub as hub
+import librosa
+import numpy as np
+import csv
+
+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
+
+# Main function to process audio and classify
+def classify_audio(file_path):
+    """
+    Given an audio file, this function loads the audio, resamples it, 
+    normalizes it, and runs it through the YAMNet model to classify the sound.
+
+    Args:
+    - file_path (str): Path to the audio file (WAV, MP3, etc.).
+
+    Returns:
+    - str: Predicted class label of the audio.
+    """
+    # Load audio using librosa (this handles both loading, resampling, and conversion to mono)
+    waveform, sample_rate = librosa.load(file_path, sr=16000, mono=True)  # Ensuring 16k sample rate and mono
+
+    # Normalize the waveform to [-1.0, 1.0] (librosa already returns normalized values)
+    waveform = waveform / np.max(np.abs(waveform))
+
+    # Execute the YAMNet model
+    scores, embeddings, spectrogram = model(waveform)
+    
+    # Extract the class names from the model
+    class_map_path = model.class_map_path().numpy()
+    class_names = class_names_from_csv(class_map_path)
+
+    # Find the class with the highest score
+    scores_np = scores.numpy()
+    inferred_class = class_names[scores_np.mean(axis=0).argmax()]
+
+    return inferred_class

tests/test_preprocessing.py

@@ -2,6 +2,7 @@ import pytest
 import librosa
 from src.preprocessing.normalize import normalize_audio
 from src.preprocessing.trim import trim_audio
+from src.preprocessing.classify import classify_audio
 from src.input.file_reader import read_audio
 
 def test_normalize_audio():
@@ -17,4 +18,11 @@ def test_trim_audio():
     audio, sr = read_audio(file_path)
     trimmed_audio = trim_audio(audio, sr)
 
-    assert len(trimmed_audio) <= len(audio)
+    assert len(trimmed_audio) <= len(audio)
+
+def test_classify():
+    file_path = "samples/cafe_crowd_talk.wav"
+    expected_class = "Speech"
+    predicted_class = classify_audio(file_path)
+
+    assert predicted_class == expected_class , f"Expected {expected_class}, but got {predicted_class}"