| """ | |
| Create pseudo data | |
| Authors | |
| * Leo 2022 | |
| """ | |
| import random | |
| import shutil | |
| import tempfile | |
| from pathlib import Path | |
| from typing import Any, List | |
| import torch | |
| import torchaudio | |
| from torch.nn.utils.rnn import pad_sequence | |
| SAMPLE_RATE = 16000 | |
| __all__ = [ | |
| "pseudo_audio", | |
| "get_pseudo_wavs", | |
| ] | |
| class pseudo_audio: | |
| """ | |
| This context manager returns filepaths (List[str]) and num_samples (List[int]) on entering | |
| """ | |
| def __init__(self, secs: List[float], sample_rate: int = SAMPLE_RATE): | |
| self.tempdir = Path(tempfile.TemporaryDirectory().name) | |
| self.tempdir.mkdir(parents=True, exist_ok=True) | |
| self.num_samples = [] | |
| for n, sec in enumerate(secs): | |
| wav = torch.randn(1, round(sample_rate * sec)) | |
| torchaudio.save( | |
| str(self.tempdir / f"audio_{n}.wav"), wav, sample_rate=sample_rate | |
| ) | |
| self.num_samples.append(wav.size(-1)) | |
| self.filepaths = [ | |
| str(self.tempdir / f"audio_{i}.wav") for i in range(len(secs)) | |
| ] | |
| def __enter__(self): | |
| return self.filepaths, self.num_samples | |
| def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: | |
| shutil.rmtree(self.tempdir) | |
| def get_pseudo_wavs( | |
| seed: int = 0, | |
| n: int = 2, | |
| min_secs: int = 1, | |
| max_secs: int = 3, | |
| sample_rate: int = SAMPLE_RATE, | |
| device: str = "cpu", | |
| padded: bool = False, | |
| ): | |
| random.seed(seed) | |
| torch.manual_seed(seed) | |
| wavs = [] | |
| wavs_len = [] | |
| for _ in range(n): | |
| wav_length = random.randint(min_secs * sample_rate, max_secs * sample_rate) | |
| wav = torch.randn(wav_length, requires_grad=True).to(device) | |
| wavs_len.append(wav_length) | |
| wavs.append(wav) | |
| if not padded: | |
| return wavs | |
| else: | |
| return pad_sequence(wavs, batch_first=True), torch.LongTensor(wavs_len) | |