| """Canonical waveform preprocessing, extracted without numerical changes.""" |
| import numpy as np |
| import torch |
| from scipy import signal |
|
|
| def prepare_staqa_waveform( |
| wav: np.ndarray, |
| sample_rate: int, |
| *, |
| target_sample_rate: int = 32_000, |
| target_length: int = 320_000, |
| normalize: bool = True, |
| ) -> torch.Tensor: |
| """Return the canonical two-channel, fixed-length StAQA waveform.""" |
|
|
| if ( |
| isinstance(sample_rate, bool) |
| or not isinstance(sample_rate, int) |
| or sample_rate <= 0 |
| ): |
| raise ValueError("sample_rate must be a positive integer") |
| if target_sample_rate <= 0: |
| raise ValueError("target_sample_rate must be positive") |
| if target_length <= 0: |
| raise ValueError("target_length must be positive") |
|
|
| waveform = np.asarray(wav).T.astype(np.float32) |
| if waveform.ndim != 2: |
| raise ValueError( |
| "StAQA waveform must be [samples, channels], " |
| f"got shape {tuple(np.asarray(wav).shape)}" |
| ) |
| if waveform.shape[0] == 1: |
| waveform = np.repeat(waveform, 2, axis=0) |
| elif waveform.shape[0] > 2: |
| waveform = waveform[:2] |
|
|
| if int(sample_rate) != int(target_sample_rate): |
| waveform = signal.resample_poly( |
| waveform, |
| int(target_sample_rate), |
| int(sample_rate), |
| axis=-1, |
| ).astype(np.float32) |
|
|
| if normalize: |
| for channel in range(waveform.shape[0]): |
| rms = float(np.sqrt(np.mean(waveform[channel] ** 2))) |
| if rms > 0: |
| waveform[channel] = waveform[channel] * 10 ** ( |
| (-14.0 - 20 * np.log10(rms)) / 20 |
| ) |
|
|
| if waveform.shape[1] >= target_length: |
| waveform = waveform[:, :target_length] |
| else: |
| pad = np.zeros( |
| (waveform.shape[0], target_length - waveform.shape[1]), |
| dtype=np.float32, |
| ) |
| waveform = np.concatenate([waveform, pad], axis=1) |
| return torch.from_numpy(waveform) |
|
|
