Upload pitcher.py

src/modules/Pitcher/pitcher.py

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+"""Pitcher module"""
+
+import crepe
+from scipy.io import wavfile
+
+from modules.console_colors import ULTRASINGER_HEAD, blue_highlighted, red_highlighted
+from modules.Pitcher.pitched_data import PitchedData
+
+
+def get_pitch_with_crepe_file(
+    filename: str, model_capacity: str, step_size: int = 10, device: str = "cpu"
+) -> PitchedData:
+    """Pitch with crepe"""
+
+    print(
+        f"{ULTRASINGER_HEAD} Pitching with {blue_highlighted('crepe')} and model {blue_highlighted(model_capacity)} and {red_highlighted(device)} as worker"
+    )
+    sample_rate, audio = wavfile.read(filename)
+
+    return get_pitch_with_crepe(audio, sample_rate, model_capacity, step_size)
+
+
+def get_pitch_with_crepe(
+    audio, sample_rate: int, model_capacity: str, step_size: int = 10
+) -> PitchedData:
+    """Pitch with crepe"""
+
+    # Info: The model is trained on 16 kHz audio, so if the input audio has a different sample rate, it will be first resampled to 16 kHz using resampy inside crepe.
+
+    times, frequencies, confidence, activation = crepe.predict(
+        audio, sample_rate, model_capacity, step_size=step_size, viterbi=True
+    )
+    return PitchedData(times, frequencies, confidence)
+
+
+def get_pitched_data_with_high_confidence(
+    pitched_data: PitchedData, threshold=0.4
+) -> PitchedData:
+    """Get frequency with high confidence"""
+    new_pitched_data = PitchedData([], [], [])
+    for i, conf in enumerate(pitched_data.confidence):
+        if conf > threshold:
+            new_pitched_data.times.append(pitched_data.times[i])
+            new_pitched_data.frequencies.append(pitched_data.frequencies[i])
+            new_pitched_data.confidence.append(pitched_data.confidence[i])
+
+    return new_pitched_data
+
+
+def get_frequencies_with_high_confidence(
+    frequencies: list[float], confidences: list[float], threshold=0.4
+) -> list[float]:
+    """Get frequency with high confidence"""
+    conf_f = []
+    for i, conf in enumerate(confidences):
+        if conf > threshold:
+            conf_f.append(frequencies[i])
+    if not conf_f:
+        conf_f = frequencies
+    return conf_f
+
+
+class Pitcher:
+    """Docstring"""