refactor code, use package libraries for freqsplit/preprocessing/classify

- add additional params: sr=None and mono=False for freqsplit/input/file_reader/read_audio()
- remove loading audio using librosa in freqsplit/preprocessing/classify/classify_audio()
- add error handling, if the sr is not 16Khz
- raise RuntimeError, if YAMNet model fails
- update tests/test_preprocessing

src/freqsplit/input/file_reader.py

@@ -1,12 +1,14 @@
 import os
 import librosa
 
-def read_audio(file_path):
+def read_audio(file_path, sr=None, mono=False):
     """
     Reads an audio file and returns the audio time series and sampling rate.
 
     Args:
         file_path (str): Path to the audio file.
+        sr (int): Sample rate at which the audio is to be loaded
+        mono (bool): True to loaded audio with single channels, else False.
 
     Returns:
         tuple: audio_time_series (numpy.ndarray), sampling_rate (int)
@@ -15,7 +17,7 @@ def read_audio(file_path):
     if not os.path.exists(file_path):
         raise FileNotFoundError(f"File not found: {file_path}")
     try:
-        audio, sr = librosa.load(file_path, sr=None) # Load with original sampling rate.
+        audio, sr = librosa.load(file_path, sr=sr, mono=mono) # Load with original sampling rate.
         return audio, sr
     except Exception as e:
         raise RuntimeError(f"Error reading the audio file: {e}")

src/freqsplit/preprocessing/classify.py

@@ -3,8 +3,6 @@ import tensorflow_hub as hub
 import librosa
 import numpy as np
 import csv
-import os
-
 
 # Force TensorFlow to use only CPU
 tf.config.set_visible_devices([], 'GPU')
@@ -22,25 +20,33 @@ def class_names_from_csv(class_map_scv_text):
     return class_names
 
 # Main function to process audio and classify
-def classify_audio(file_path):
+def classify_audio(waveform, sr):
     """
     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.).
+    - waveform (numpy.ndarray): waveform of 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
+    
+    # Check if the sampling rate is 16000Hz
+    try:
+        if(sr!=16000):
+            raise RuntimeError
+    except Exception:
+        raise RuntimeError(f"The audio is not sampled at 16000Hz, failed to classify audio.")
     
     # Normalize the waveform to [-1.0, 1.0] (librosa already returns normalized values)
     waveform = waveform / np.max(np.abs(waveform))
 
     # Execute the YAMNet model
+    try:
         scores, embeddings, spectrogram = model(waveform)
+    except Exception as e:
+        raise RuntimeError(f"Error: Failed to classify audio: {e}")
         
     # Extract the class names from the model
     class_map_path = model.class_map_path().numpy()

tests/test_preprocessing.py

@@ -24,8 +24,9 @@ def test_trim_audio():
 
 def test_classify():
     file_path = "tests/test_audio/cafe_crowd_talk.wav"
+    waveform, sr = read_audio(file_path, 16000, mono=True)
     expected_class = "Speech"
-    predicted_class = classify_audio(file_path)
+    predicted_class = classify_audio(waveform, sr)
 
     assert predicted_class == expected_class , f"Expected {expected_class}, but got {predicted_class}"