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72c09ec0b7* indextts2 * update lfs for audio files --------- Co-authored-by: wangyining02 <wangyining02@bilibili.com>
102 lines
2.6 KiB
Python
102 lines
2.6 KiB
Python
import torch
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import pyworld as pw
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import numpy as np
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import soundfile as sf
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import os
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from torchaudio.functional import pitch_shift
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import librosa
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from librosa.filters import mel as librosa_mel_fn
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import torch.nn as nn
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import torch.nn.functional as F
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def dynamic_range_compression(x, C=1, clip_val=1e-5):
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return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
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def dynamic_range_decompression(x, C=1):
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return np.exp(x) / C
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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return torch.log(torch.clamp(x, min=clip_val) * C)
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def dynamic_range_decompression_torch(x, C=1):
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return torch.exp(x) / C
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def spectral_normalize_torch(magnitudes):
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output = dynamic_range_compression_torch(magnitudes)
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return output
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def spectral_de_normalize_torch(magnitudes):
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output = dynamic_range_decompression_torch(magnitudes)
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return output
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class MelSpectrogram(nn.Module):
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def __init__(
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self,
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n_fft,
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num_mels,
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sampling_rate,
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hop_size,
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win_size,
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fmin,
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fmax,
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center=False,
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):
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super(MelSpectrogram, self).__init__()
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self.n_fft = n_fft
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self.hop_size = hop_size
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self.win_size = win_size
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self.sampling_rate = sampling_rate
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self.num_mels = num_mels
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self.fmin = fmin
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self.fmax = fmax
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self.center = center
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mel_basis = {}
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hann_window = {}
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mel = librosa_mel_fn(
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sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
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)
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mel_basis = torch.from_numpy(mel).float()
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hann_window = torch.hann_window(win_size)
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self.register_buffer("mel_basis", mel_basis)
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self.register_buffer("hann_window", hann_window)
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def forward(self, y):
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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(
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int((self.n_fft - self.hop_size) / 2),
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int((self.n_fft - self.hop_size) / 2),
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),
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mode="reflect",
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)
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y = y.squeeze(1)
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spec = torch.stft(
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y,
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self.n_fft,
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hop_length=self.hop_size,
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win_length=self.win_size,
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window=self.hann_window,
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center=self.center,
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pad_mode="reflect",
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normalized=False,
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onesided=True,
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return_complex=True,
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)
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spec = torch.view_as_real(spec)
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
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spec = torch.matmul(self.mel_basis, spec)
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spec = spectral_normalize_torch(spec)
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return spec
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