| import os |
| import sys |
| import torch |
|
|
| import numpy as np |
| import torch.nn.functional as F |
|
|
| from librosa.filters import mel |
|
|
| sys.path.append(os.getcwd()) |
|
|
| class STFT: |
| def __init__( |
| self, |
| sr=22050, |
| n_mels=80, |
| n_fft=1024, |
| win_size=1024, |
| hop_length=256, |
| fmin=20, |
| fmax=11025, |
| clip_val=1e-5 |
| ): |
| self.target_sr = sr |
| self.n_mels = n_mels |
| self.n_fft = n_fft |
| self.win_size = win_size |
| self.hop_length = hop_length |
| self.fmin = fmin |
| self.fmax = fmax |
| self.clip_val = clip_val |
| self.mel_basis = {} |
| self.hann_window = {} |
|
|
| def get_mel(self, y, keyshift=0, speed=1, center=False, train=False): |
| n_fft = self.n_fft |
| win_size = self.win_size |
| hop_length = self.hop_length |
|
|
| fmax = self.fmax |
| factor = 2 ** (keyshift / 12) |
|
|
| win_size_new = int(np.round(win_size * factor)) |
| hop_length_new = int(np.round(hop_length * speed)) |
|
|
| mel_basis = self.mel_basis if not train else {} |
| hann_window = self.hann_window if not train else {} |
| mel_basis_key = str(fmax) + "_" + str(y.device) |
|
|
| if mel_basis_key not in mel_basis: |
| mel_basis[mel_basis_key] = torch.from_numpy( |
| mel( |
| sr=self.target_sr, |
| n_fft=n_fft, |
| n_mels=self.n_mels, |
| fmin=self.fmin, |
| fmax=fmax |
| ) |
| ).float().to(y.device) |
|
|
| keyshift_key = str(keyshift) + "_" + str(y.device) |
| if keyshift_key not in hann_window: hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device) |
|
|
| pad_left = (win_size_new - hop_length_new) // 2 |
| pad_right = max((win_size_new - hop_length_new + 1) // 2, win_size_new - y.size(-1) - pad_left) |
|
|
| pad = F.pad(y.unsqueeze(1), (pad_left, pad_right), mode="reflect" if pad_right < y.size(-1) else "constant").squeeze(1) |
| n_fft = int(np.round(n_fft * factor)) |
|
|
| if str(y.device).startswith(("ocl", "privateuseone")): |
| if not hasattr(self, "stft"): |
| from main.library.backends.utils import STFT as _STFT |
|
|
| self.stft = _STFT( |
| filter_length=n_fft, |
| hop_length=hop_length_new, |
| win_length=win_size_new |
| ).to(y.device) |
|
|
| spec = self.stft.transform(pad, 1e-9) |
| else: |
| spec = torch.stft( |
| pad, |
| n_fft, |
| hop_length=hop_length_new, |
| win_length=win_size_new, |
| window=hann_window[keyshift_key], |
| center=center, |
| pad_mode="reflect", |
| normalized=False, |
| onesided=True, |
| return_complex=True |
| ) |
|
|
| spec = (spec.real.pow(2) + spec.imag.pow(2) + 1e-9).sqrt() |
|
|
| if keyshift != 0: |
| size = n_fft // 2 + 1 |
| resize = spec.size(1) |
| spec = (F.pad(spec, (0, 0, 0, size - resize)) if resize < size else spec[:, :size, :]) * win_size / win_size_new |
|
|
| return ((mel_basis[mel_basis_key] @ spec).clamp(min=self.clip_val) * 1).log() |
