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mel_processing.py
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import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
import warnings
# warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings(action="ignore")
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 = {}
'''
输入信号y的短时傅里叶变换（short-time Fourier transform，STFT）的幅度谱（magnitude spectrum）1。
STFT是一种将信号分解为时间-频率域的方法，它可以用来分析信号的频率特征和变化情况
spec的每一行对应一个时间窗口，每一列对应一个频率分量，spec[i][j]的值表示在第i个时间窗口和第j个频率分量上的信号幅度
这里的spec不是梅尔频谱（Mel-spectrogram），梅尔频谱是一种基于人耳听觉特性的频谱表示，它将线性频率刻度转换为非线性的梅尔刻度。
梅尔刻度更加贴近人类对声音的感知，它可以用来提取语音信号的特征。如果你想得到梅尔频谱，你可以使用torch库中的torchaudio.transforms.MelSpectrogram函数。
原始图到STFT的变换是可逆的，也就是说，你可以从STFT的结果恢复出原始图。这个过程叫做逆短时傅里叶变换（Inverse Short-Time Fourier Transform，ISTFT）
你可以使用torch库中的torchaudio.functional.istft函数来实现ISTFT.
原始图到梅尔频谱（Mel-spectrogram）的变换不是可逆的，也就是说，你不能从梅尔频谱恢复出原始图。这是因为梅尔频谱是对STFT的结果进行了非线性的变换，丢失了一些信息。
梅尔频谱更适合用来分析语音信号的特征，而不是重建信号。
'''
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
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,
return_complex=False,
)
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) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
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,
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,
return_complex=False,
)
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