Added Utilities

Author: isaac-rnd

data/datasets.txt

@@ -0,0 +1,85 @@
+import soundfile
+import numpy as np
+import librosa
+import glob
+import os
+from sklearn.model_selection import train_test_split
+
+# all emotions on RAVDESS dataset
+int2emotion = {
+    "01": "neutral",
+    "02": "calm",
+    "03": "happy",
+    "04": "sad",
+    "05": "angry",
+    "06": "fearful",
+    "07": "disgust",
+    "08": "surprised"
+}
+
+# we allow only these emotions
+AVAILABLE_EMOTIONS = {
+    "angry",
+    "sad",
+    "neutral",
+    "happy"
+}
+
+def extract_feature(file_name, **kwargs):
+    """
+    Extract feature from audio file `file_name`
+        Features supported:
+            - MFCC (mfcc)
+            - Chroma (chroma)
+            - MEL Spectrogram Frequency (mel)
+            - Contrast (contrast)
+            - Tonnetz (tonnetz)
+        e.g:
+        `features = extract_feature(path, mel=True, mfcc=True)`
+    """
+    mfcc = kwargs.get("mfcc")
+    chroma = kwargs.get("chroma")
+    mel = kwargs.get("mel")
+    contrast = kwargs.get("contrast")
+    tonnetz = kwargs.get("tonnetz")
+    with soundfile.SoundFile(file_name) as sound_file:
+        X = sound_file.read(dtype="float32")
+        sample_rate = sound_file.samplerate
+        if chroma or contrast:
+            stft = np.abs(librosa.stft(X))
+        result = np.array([])
+        if mfcc:
+            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
+            result = np.hstack((result, mfccs))
+        if chroma:
+            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
+            result = np.hstack((result, chroma))
+        if mel:
+            mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)
+            result = np.hstack((result, mel))
+        if contrast:
+            contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)
+            result = np.hstack((result, contrast))
+        if tonnetz:
+            tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)
+            result = np.hstack((result, tonnetz))
+    return result
+
+
+def load_data(test_size=0.2):
+    X, y = [], []
+    for file in glob.glob("data/Actor_*/*.wav"):
+        # get the base name of the audio file
+        basename = os.path.basename(file)
+        # get the emotion label
+        emotion = int2emotion[basename.split("-")[2]]
+        # we allow only AVAILABLE_EMOTIONS we set
+        if emotion not in AVAILABLE_EMOTIONS:
+            continue
+        # extract speech features
+        features = extract_feature(file, mfcc=True, chroma=True, mel=True)
+        # add to data
+        X.append(features)
+        y.append(emotion)
+    # split the data to training and testing and return it
+    return train_test_split(np.array(X), y, test_size=test_size, random_state=7)