| import os | |
| import sys | |
| import torch | |
| import numpy as np | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from librosa.filters import mel | |
| sys.path.append(os.getcwd()) | |
| from main.library import opencl | |
| class MelSpectrogram(nn.Module): | |
| def __init__(self, is_half, n_mel_channels, sample_rate, win_length, hop_length, n_fft=None, mel_fmin=0, mel_fmax=None, clamp=1e-5): | |
| super().__init__() | |
| n_fft = win_length if n_fft is None else n_fft | |
| self.hann_window = {} | |
| mel_basis = mel(sr=sample_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=mel_fmin, fmax=mel_fmax, htk=True) | |
| mel_basis = torch.from_numpy(mel_basis).float() | |
| self.register_buffer("mel_basis", mel_basis) | |
| self.n_fft = win_length if n_fft is None else n_fft | |
| self.hop_length = hop_length | |
| self.win_length = win_length | |
| self.sample_rate = sample_rate | |
| self.n_mel_channels = n_mel_channels | |
| self.clamp = clamp | |
| self.is_half = is_half | |
| def forward(self, audio, keyshift=0, speed=1, center=True): | |
| factor = 2 ** (keyshift / 12) | |
| win_length_new = int(np.round(self.win_length * factor)) | |
| keyshift_key = str(keyshift) + "_" + str(audio.device) | |
| if keyshift_key not in self.hann_window: self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(audio.device) | |
| n_fft = int(np.round(self.n_fft * factor)) | |
| hop_length = int(np.round(self.hop_length * speed)) | |
| if str(audio.device).startswith("ocl"): | |
| stft = opencl.STFT(filter_length=n_fft, hop_length=hop_length, win_length=win_length_new).to(audio.device) | |
| magnitude = stft.transform(audio, 1e-9) | |
| else: | |
| fft = torch.stft(audio, n_fft=n_fft, hop_length=hop_length, win_length=win_length_new, window=self.hann_window[keyshift_key], center=center, return_complex=True) | |
| magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2)) | |
| if keyshift != 0: | |
| size = self.n_fft // 2 + 1 | |
| resize = magnitude.size(1) | |
| if resize < size: magnitude = F.pad(magnitude, (0, 0, 0, size - resize)) | |
| magnitude = magnitude[:, :size, :] * self.win_length / win_length_new | |
| mel_output = torch.matmul(self.mel_basis, magnitude) | |
| if self.is_half: mel_output = mel_output.half() | |
| return torch.log(torch.clamp(mel_output, min=self.clamp)) |