| from __future__ import annotations
|
|
|
| from pathlib import Path
|
|
|
| import torch
|
| import numpy as np
|
| import numpy.typing as npt
|
| from omegaconf import DictConfig
|
|
|
| from osuT5.dataset.data_utils import load_audio_file
|
|
|
|
|
| class Preprocessor(object):
|
| def __init__(self, args: DictConfig):
|
| """Preprocess audio data into sequences."""
|
| self.frame_seq_len = args.data.src_seq_len - 1
|
| self.frame_size = args.data.hop_length
|
| self.sample_rate = args.data.sample_rate
|
| self.samples_per_sequence = self.frame_seq_len * self.frame_size
|
| self.sequence_stride = int(self.samples_per_sequence * args.data.sequence_stride)
|
|
|
| def load(self, path: Path) -> npt.ArrayLike:
|
| """Load an audio file as audio frames. Convert stereo to mono, normalize.
|
|
|
| Args:
|
| path: Path to audio file.
|
|
|
| Returns:
|
| samples: Audio time-series.
|
| """
|
| return load_audio_file(path, self.sample_rate)
|
|
|
| def segment(self, samples: npt.ArrayLike) -> torch.Tensor:
|
| """Segment audio samples into sequences. Sequences are flattened frames.
|
|
|
| Args:
|
| samples: Audio time-series.
|
|
|
| Returns:
|
| sequences: A list of sequences of shape (batch size, samples per sequence).
|
| """
|
| samples = np.pad(
|
| samples,
|
| [0, self.sequence_stride - (len(samples) - self.samples_per_sequence) % self.sequence_stride],
|
| )
|
| sequences = self.window(samples, self.samples_per_sequence, self.sequence_stride)
|
| sequences = torch.from_numpy(sequences).to(torch.float32)
|
| return sequences
|
|
|
| @staticmethod
|
| def window(a, w, o, copy=False):
|
| sh = (a.size - w + 1, w)
|
| st = a.strides * 2
|
| view = np.lib.stride_tricks.as_strided(a, strides=st, shape=sh)[0::o]
|
| if copy:
|
| return view.copy()
|
| else:
|
| return view
|
|
|
