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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"""
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