140 lines
5.3 KiB
Python
140 lines
5.3 KiB
Python
import math
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import numpy as np
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import torch
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import torch.utils.data
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from librosa.filters import mel as librosa_mel_fn
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from scipy.io.wavfile import read
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import torch
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import torch.nn as nn
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MAX_WAV_VALUE = 32768.0
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def load_wav(full_path):
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sampling_rate, data = read(full_path)
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return data, sampling_rate
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def dynamic_range_compression(x, C=1, clip_val=1e-5):
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return np.log10(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.log10(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 MelNet(nn.Module):
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def __init__(self, hparams, device='cpu') -> None:
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super().__init__()
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self.n_fft = hparams['fft_size']
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self.num_mels = hparams['audio_num_mel_bins']
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self.sampling_rate = hparams['audio_sample_rate']
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self.hop_size = hparams['hop_size']
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self.win_size = hparams['win_size']
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self.fmin = hparams['fmin']
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self.fmax = hparams['fmax']
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self.device = device
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mel = librosa_mel_fn(sr=self.sampling_rate, n_fft=self.n_fft, n_mels=self.num_mels, fmin=self.fmin,
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fmax=self.fmax)
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self.mel_basis = torch.from_numpy(mel).float().to(self.device)
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self.hann_window = torch.hann_window(self.win_size).to(self.device)
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def to(self, device, **kwagrs):
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super().to(device=device, **kwagrs)
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self.mel_basis = self.mel_basis.to(device)
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self.hann_window = self.hann_window.to(device)
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self.device = device
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def forward(self, y, center=False, complex=False):
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if isinstance(y, np.ndarray):
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y = torch.FloatTensor(y)
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if len(y.shape) == 1:
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y = y.unsqueeze(0)
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y = y.clamp(min=-1., max=1.).to(self.device)
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pad_length = math.ceil(y.shape[1] / self.hop_size) * self.hop_size - y.shape[1]
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y = torch.nn.functional.pad(y.unsqueeze(1),
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[int((self.n_fft - self.hop_size) / 2),
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int((self.n_fft - self.hop_size) / 2 + pad_length)],
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mode='reflect')
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y = y.squeeze(1)
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spec = torch.stft(y, self.n_fft, hop_length=self.hop_size, win_length=self.win_size, window=self.hann_window,
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center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
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if not complex:
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spec = torch.view_as_real(spec)
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) # [B, n_fft, T]
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spec = torch.matmul(self.mel_basis, spec)
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spec = spectral_normalize_torch(spec)
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spec = spec.transpose(1, 2) # [B, T, n_fft]
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else:
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B, C, T, _ = spec.shape
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spec = spec.transpose(1, 2) # [B, T, n_fft, 2]
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return spec
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## below can be used in one gpu, but not ddp
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mel_basis = {}
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hann_window = {}
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def mel_spectrogram(y, hparams, center=False, complex=False): # y should be a tensor with shape (b,wav_len)
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# hop_size: 512 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate)
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# win_size: 2048 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate)
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# fmin: 55 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
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# fmax: 10000 # To be increased/reduced depending on data.
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# fft_size: 2048 # Extra window size is filled with 0 paddings to match this parameter
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# n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax,
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n_fft = hparams['fft_size']
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num_mels = hparams['audio_num_mel_bins']
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sampling_rate = hparams['audio_sample_rate']
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hop_size = hparams['hop_size']
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win_size = hparams['win_size']
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fmin = hparams['fmin']
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fmax = hparams['fmax']
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if isinstance(y, np.ndarray):
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y = torch.FloatTensor(y)
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if len(y.shape) == 1:
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y = y.unsqueeze(0)
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y = y.clamp(min=-1., max=1.)
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global mel_basis, hann_window
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if fmax not in mel_basis:
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mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
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mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
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hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
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y = torch.nn.functional.pad(y.unsqueeze(1), [int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)],
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mode='reflect')
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y = y.squeeze(1)
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spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
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center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=complex)
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if not complex:
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
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spec = torch.matmul(mel_basis[str(fmax) + '_' + str(y.device)], spec)
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spec = spectral_normalize_torch(spec)
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else:
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B, C, T, _ = spec.shape
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spec = spec.transpose(1, 2) # [B, T, n_fft, 2]
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return spec
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