| 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.min_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) |
|
|
