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import numpy as np
import librosa
from scipy import signal
from .slicer2 import Slicer
class Preprocessor:
def __init__(
self, sr: int, max_slice_length: float = 3.0, min_slice_length: float = 0.5
):
self.slicer = Slicer(
sr=sr,
threshold=-42,
min_length=1500,
min_interval=400,
hop_size=15,
max_sil_kept=500,
)
self.sr = sr
self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
self.max_slice_length = max_slice_length
self.max_slice_length = min_slice_length
self.overlap = 0.3
self.tail = self.max_slice_length + self.overlap
self.max = 0.9
self.alpha = 0.75
def norm(self, samples: np.ndarray) -> np.ndarray:
sample_max = np.abs(samples).max()
normalized = samples / sample_max * self.max
normalized = (normalized * self.alpha) + (samples * (1 - self.alpha))
return normalized
def preprocess_audio(self, y: np.ndarray) -> list[np.ndarray]:
y = signal.filtfilt(self.bh, self.ah, y)
audios = []
for audio in self.slicer.slice(y):
i = 0
while True:
start = int(self.sr * (self.max_slice_length - self.overlap) * i)
i += 1
if len(audio[start:]) > self.tail * self.sr:
slice = audio[start : start + int(self.max_slice_length * self.sr)]
audios.append(self.norm(slice))
else:
slice = audio[start:]
if len(slice) > self.min_slice_length * self.sr:
audios.append(self.norm(slice))
break
return audios
def preprocess_file(self, file_path: str) -> list[np.ndarray]:
y, _ = librosa.load(file_path, sr=self.sr)
return self.preprocess_audio(y)
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