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import math | |
import os | |
import random | |
import torch | |
from torch import nn | |
import torch.nn.functional as F | |
import torch.utils.data | |
import numpy as np | |
import librosa | |
import librosa.util as librosa_util | |
from librosa.util import normalize, pad_center, tiny | |
from scipy.signal import get_window | |
from scipy.io.wavfile import read | |
from librosa.filters import mel as librosa_mel_fn | |
MAX_WAV_VALUE = 32768.0 | |
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): | |
""" | |
PARAMS | |
------ | |
C: compression factor | |
""" | |
return torch.log(torch.clamp(x, min=clip_val) * C) | |
def dynamic_range_decompression_torch(x, C=1): | |
""" | |
PARAMS | |
------ | |
C: compression factor used to compress | |
""" | |
return torch.exp(x) / C | |
def spectral_normalize_torch(magnitudes): | |
output = dynamic_range_compression_torch(magnitudes) | |
return output | |
def spectral_de_normalize_torch(magnitudes): | |
output = dynamic_range_decompression_torch(magnitudes) | |
return output | |
mel_basis = {} | |
hann_window = {} | |
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): | |
if torch.min(y) < -1.: | |
print('min value is ', torch.min(y)) | |
if torch.max(y) > 1.: | |
print('max value is ', torch.max(y)) | |
global hann_window | |
dtype_device = str(y.dtype) + '_' + str(y.device) | |
wnsize_dtype_device = str(win_size) + '_' + dtype_device | |
if wnsize_dtype_device not in hann_window: | |
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) | |
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') | |
y = y.squeeze(1) | |
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], | |
center=center, pad_mode='reflect', normalized=False, onesided=True) | |
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) | |
return spec | |
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): | |
global mel_basis | |
dtype_device = str(spec.dtype) + '_' + str(spec.device) | |
fmax_dtype_device = str(fmax) + '_' + dtype_device | |
if fmax_dtype_device not in mel_basis: | |
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) | |
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) | |
spec = torch.matmul(mel_basis[fmax_dtype_device], spec) | |
spec = spectral_normalize_torch(spec) | |
return spec | |
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): | |
if torch.min(y) < -1.: | |
print('min value is ', torch.min(y)) | |
if torch.max(y) > 1.: | |
print('max value is ', torch.max(y)) | |
global mel_basis, hann_window | |
dtype_device = str(y.dtype) + '_' + str(y.device) | |
fmax_dtype_device = str(fmax) + '_' + dtype_device | |
wnsize_dtype_device = str(win_size) + '_' + dtype_device | |
if fmax_dtype_device not in mel_basis: | |
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) | |
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) | |
if wnsize_dtype_device not in hann_window: | |
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) | |
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') | |
y = y.squeeze(1) | |
spec = torch.stft(y.float(), n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], | |
center=center, pad_mode='reflect', normalized=False, onesided=True) | |
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) | |
spec = torch.matmul(mel_basis[fmax_dtype_device], spec) | |
spec = spectral_normalize_torch(spec) | |
return spec | |