[+] Deploy to pypi

sgs/api.py

@@ -0,0 +1,68 @@
+import json
+from pathlib import Path
+from typing import Literal
+
+import pkg_resources
+from parselmouth import Sound
+from scipy.stats import gaussian_kde
+
+from .calculations import calculate_freq_statistics, calculate_freq_info, calculate_tilt
+
+Feature = Literal['pitch', 'f1', 'f2', 'f3', 'tilt']
+Gender = Literal['f', 'm']
+
+
+_kde_functions: dict[Feature, dict[Gender, gaussian_kde]] = {}
+
+
+def load_kde() -> dict[Feature, dict[Gender, gaussian_kde]]:
+    """
+    Load statistical results into kernel density functions
+
+    :return: Kernel density functions for F and M for pitch, f1, f2, f3, tilt
+    """
+    if _kde_functions:
+        return _kde_functions
+
+    data_file = pkg_resources.resource_filename(__name__, 'data/vox1_data.json')
+    data: dict[Feature, dict[Gender, list[float]]] = json.loads(Path(data_file).read_text())
+
+    # Lowercase keys
+    data = {k.lower(): data[k] for k in data}
+
+    # Fit KDE functions
+    for feature in data:
+        _kde_functions[feature] = {}
+        for gender in data[feature]:
+            kde = gaussian_kde(data[feature][gender], 'scott')
+            _kde_functions[feature][gender] = kde
+
+    return _kde_functions
+
+
+def calculate_feature_means(audio: Sound) -> dict[Feature, float]:
+    s = calculate_freq_statistics(calculate_freq_info(audio))
+    return {'pitch': s.pitch.mean, 'f1': s.f1.mean, 'f2': s.f2.mean, 'f3': s.f3.mean, 'tilt': calculate_tilt(audio)}
+
+
+def _calculate_fem_prob(feature: Feature, value: float) -> float:
+    """
+    Calculate probability of a feature sounding feminine
+
+    :return: Ratio between 0 and 1
+    """
+    f = load_kde()[feature]['f'].evaluate([value])[0]
+    m = load_kde()[feature]['m'].evaluate([value])[0]
+    return f / (f + m)
+
+
+def calculate_feature_classification(audio: Sound):
+    """
+    Run statistical classification based on kernel density estimation.
+
+    :param audio: Audio
+    :return: Statistical results {'means': {'pitch': ..., 'f1': ...}, 'fem_prob': {'pitch': ..., 'f1': ...}}
+    """
+    means = calculate_feature_means(audio)
+    fem_prob = {feature: _calculate_fem_prob(feature, means[feature]) for feature in means}
+    return {'means': means, 'fem_prob': fem_prob}