| 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()) | |
| from main.library import opencl | |
| 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"): | |
| stft = opencl.STFT(filter_length=n_fft, hop_length=hop_length_new, win_length=win_size_new).to(y.device) | |
| spec = 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 = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-9) | |
| 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 torch.log(torch.clamp(torch.matmul(mel_basis[mel_basis_key], spec), min=self.clip_val) * 1) |