694 lines
25 KiB
Python
694 lines
25 KiB
Python
import pdb
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from librosa.util import normalize, pad_center, tiny
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from scipy.signal import get_window
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###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
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def window_sumsquare(
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window,
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n_frames,
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hop_length=200,
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win_length=800,
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n_fft=800,
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dtype=np.float32,
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norm=None,
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):
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"""
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# from librosa 0.6
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Compute the sum-square envelope of a window function at a given hop length.
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This is used to estimate modulation effects induced by windowing
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observations in short-time fourier transforms.
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Parameters
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----------
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window : string, tuple, number, callable, or list-like
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Window specification, as in `get_window`
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n_frames : int > 0
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The number of analysis frames
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hop_length : int > 0
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The number of samples to advance between frames
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win_length : [optional]
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The length of the window function. By default, this matches `n_fft`.
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n_fft : int > 0
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The length of each analysis frame.
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dtype : np.dtype
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The data type of the output
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Returns
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-------
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wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
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The sum-squared envelope of the window function
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"""
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if win_length is None:
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win_length = n_fft
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n = n_fft + hop_length * (n_frames - 1)
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x = np.zeros(n, dtype=dtype)
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# Compute the squared window at the desired length
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win_sq = get_window(window, win_length, fftbins=True)
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win_sq = normalize(win_sq, norm=norm) ** 2
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win_sq = pad_center(win_sq, n_fft)
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# Fill the envelope
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for i in range(n_frames):
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sample = i * hop_length
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x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
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return x
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class STFT(torch.nn.Module):
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def __init__(
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self, filter_length=1024, hop_length=512, win_length=None, window="hann"
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):
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"""
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This module implements an STFT using 1D convolution and 1D transpose convolutions.
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This is a bit tricky so there are some cases that probably won't work as working
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out the same sizes before and after in all overlap add setups is tough. Right now,
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this code should work with hop lengths that are half the filter length (50% overlap
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between frames).
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Keyword Arguments:
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filter_length {int} -- Length of filters used (default: {1024})
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hop_length {int} -- Hop length of STFT (restrict to 50% overlap between frames) (default: {512})
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win_length {[type]} -- Length of the window function applied to each frame (if not specified, it
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equals the filter length). (default: {None})
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window {str} -- Type of window to use (options are bartlett, hann, hamming, blackman, blackmanharris)
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(default: {'hann'})
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"""
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super(STFT, self).__init__()
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self.filter_length = filter_length
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self.hop_length = hop_length
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self.win_length = win_length if win_length else filter_length
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self.window = window
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self.forward_transform = None
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self.pad_amount = int(self.filter_length / 2)
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scale = self.filter_length / self.hop_length
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fourier_basis = np.fft.fft(np.eye(self.filter_length))
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cutoff = int((self.filter_length / 2 + 1))
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fourier_basis = np.vstack(
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[np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
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)
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forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
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inverse_basis = torch.FloatTensor(
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np.linalg.pinv(scale * fourier_basis).T[:, None, :]
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)
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assert filter_length >= self.win_length
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# get window and zero center pad it to filter_length
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fft_window = get_window(window, self.win_length, fftbins=True)
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fft_window = pad_center(fft_window, size=filter_length)
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fft_window = torch.from_numpy(fft_window).float()
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# window the bases
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forward_basis *= fft_window
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inverse_basis *= fft_window
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self.register_buffer("forward_basis", forward_basis.float())
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self.register_buffer("inverse_basis", inverse_basis.float())
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def transform(self, input_data):
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"""Take input data (audio) to STFT domain.
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Arguments:
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input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
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Returns:
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magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
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num_frequencies, num_frames)
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phase {tensor} -- Phase of STFT with shape (num_batch,
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num_frequencies, num_frames)
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"""
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num_batches = input_data.shape[0]
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num_samples = input_data.shape[-1]
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self.num_samples = num_samples
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# similar to librosa, reflect-pad the input
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input_data = input_data.view(num_batches, 1, num_samples)
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# print(1234,input_data.shape)
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input_data = F.pad(
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input_data.unsqueeze(1),
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(self.pad_amount, self.pad_amount, 0, 0, 0, 0),
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mode="reflect",
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).squeeze(1)
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# print(2333,input_data.shape,self.forward_basis.shape,self.hop_length)
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# pdb.set_trace()
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forward_transform = F.conv1d(
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input_data, self.forward_basis, stride=self.hop_length, padding=0
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)
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cutoff = int((self.filter_length / 2) + 1)
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real_part = forward_transform[:, :cutoff, :]
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imag_part = forward_transform[:, cutoff:, :]
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magnitude = torch.sqrt(real_part**2 + imag_part**2)
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# phase = torch.atan2(imag_part.data, real_part.data)
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return magnitude # , phase
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def inverse(self, magnitude, phase):
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"""Call the inverse STFT (iSTFT), given magnitude and phase tensors produced
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by the ```transform``` function.
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Arguments:
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magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
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num_frequencies, num_frames)
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phase {tensor} -- Phase of STFT with shape (num_batch,
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num_frequencies, num_frames)
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Returns:
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inverse_transform {tensor} -- Reconstructed audio given magnitude and phase. Of
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shape (num_batch, num_samples)
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"""
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recombine_magnitude_phase = torch.cat(
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[magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
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)
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inverse_transform = F.conv_transpose1d(
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recombine_magnitude_phase,
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self.inverse_basis,
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stride=self.hop_length,
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padding=0,
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)
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if self.window is not None:
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window_sum = window_sumsquare(
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self.window,
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magnitude.size(-1),
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hop_length=self.hop_length,
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win_length=self.win_length,
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n_fft=self.filter_length,
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dtype=np.float32,
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)
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# remove modulation effects
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approx_nonzero_indices = torch.from_numpy(
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np.where(window_sum > tiny(window_sum))[0]
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)
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window_sum = torch.from_numpy(window_sum).to(inverse_transform.device)
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inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
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approx_nonzero_indices
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]
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# scale by hop ratio
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inverse_transform *= float(self.filter_length) / self.hop_length
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inverse_transform = inverse_transform[..., self.pad_amount :]
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inverse_transform = inverse_transform[..., : self.num_samples]
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inverse_transform = inverse_transform.squeeze(1)
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return inverse_transform
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def forward(self, input_data):
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"""Take input data (audio) to STFT domain and then back to audio.
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Arguments:
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input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
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Returns:
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reconstruction {tensor} -- Reconstructed audio given magnitude and phase. Of
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shape (num_batch, num_samples)
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"""
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self.magnitude, self.phase = self.transform(input_data)
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reconstruction = self.inverse(self.magnitude, self.phase)
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return reconstruction
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from time import time as ttime
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class BiGRU(nn.Module):
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def __init__(self, input_features, hidden_features, num_layers):
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super(BiGRU, self).__init__()
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self.gru = nn.GRU(
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input_features,
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hidden_features,
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num_layers=num_layers,
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batch_first=True,
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bidirectional=True,
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)
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def forward(self, x):
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return self.gru(x)[0]
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class ConvBlockRes(nn.Module):
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def __init__(self, in_channels, out_channels, momentum=0.01):
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super(ConvBlockRes, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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nn.Conv2d(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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if in_channels != out_channels:
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self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
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self.is_shortcut = True
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else:
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self.is_shortcut = False
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def forward(self, x):
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if self.is_shortcut:
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return self.conv(x) + self.shortcut(x)
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else:
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return self.conv(x) + x
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class Encoder(nn.Module):
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def __init__(
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self,
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in_channels,
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in_size,
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n_encoders,
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kernel_size,
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n_blocks,
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out_channels=16,
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momentum=0.01,
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):
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super(Encoder, self).__init__()
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self.n_encoders = n_encoders
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self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
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self.layers = nn.ModuleList()
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self.latent_channels = []
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for i in range(self.n_encoders):
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self.layers.append(
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ResEncoderBlock(
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in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
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)
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)
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self.latent_channels.append([out_channels, in_size])
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in_channels = out_channels
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out_channels *= 2
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in_size //= 2
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self.out_size = in_size
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self.out_channel = out_channels
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def forward(self, x):
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concat_tensors = []
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x = self.bn(x)
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for i in range(self.n_encoders):
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_, x = self.layers[i](x)
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concat_tensors.append(_)
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return x, concat_tensors
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class ResEncoderBlock(nn.Module):
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def __init__(
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self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
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):
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super(ResEncoderBlock, self).__init__()
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self.n_blocks = n_blocks
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self.conv = nn.ModuleList()
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self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
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for i in range(n_blocks - 1):
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self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
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self.kernel_size = kernel_size
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if self.kernel_size is not None:
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self.pool = nn.AvgPool2d(kernel_size=kernel_size)
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def forward(self, x):
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for i in range(self.n_blocks):
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x = self.conv[i](x)
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if self.kernel_size is not None:
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return x, self.pool(x)
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else:
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return x
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class Intermediate(nn.Module): #
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def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
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super(Intermediate, self).__init__()
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self.n_inters = n_inters
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self.layers = nn.ModuleList()
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self.layers.append(
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ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
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)
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for i in range(self.n_inters - 1):
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self.layers.append(
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ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
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)
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def forward(self, x):
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for i in range(self.n_inters):
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x = self.layers[i](x)
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return x
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class ResDecoderBlock(nn.Module):
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def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
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super(ResDecoderBlock, self).__init__()
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out_padding = (0, 1) if stride == (1, 2) else (1, 1)
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self.n_blocks = n_blocks
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self.conv1 = nn.Sequential(
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nn.ConvTranspose2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=stride,
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padding=(1, 1),
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output_padding=out_padding,
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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self.conv2 = nn.ModuleList()
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self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
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for i in range(n_blocks - 1):
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self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
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def forward(self, x, concat_tensor):
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x = self.conv1(x)
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x = torch.cat((x, concat_tensor), dim=1)
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for i in range(self.n_blocks):
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x = self.conv2[i](x)
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return x
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class Decoder(nn.Module):
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def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
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super(Decoder, self).__init__()
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self.layers = nn.ModuleList()
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self.n_decoders = n_decoders
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for i in range(self.n_decoders):
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out_channels = in_channels // 2
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self.layers.append(
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ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
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)
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in_channels = out_channels
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def forward(self, x, concat_tensors):
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for i in range(self.n_decoders):
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x = self.layers[i](x, concat_tensors[-1 - i])
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return x
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class DeepUnet(nn.Module):
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def __init__(
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self,
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kernel_size,
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n_blocks,
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(DeepUnet, self).__init__()
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self.encoder = Encoder(
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in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
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)
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self.intermediate = Intermediate(
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self.encoder.out_channel // 2,
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self.encoder.out_channel,
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inter_layers,
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n_blocks,
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)
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self.decoder = Decoder(
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self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
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)
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def forward(self, x):
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x, concat_tensors = self.encoder(x)
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x = self.intermediate(x)
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x = self.decoder(x, concat_tensors)
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return x
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class E2E(nn.Module):
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def __init__(
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self,
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n_blocks,
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n_gru,
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kernel_size,
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(E2E, self).__init__()
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self.unet = DeepUnet(
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kernel_size,
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n_blocks,
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en_de_layers,
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inter_layers,
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in_channels,
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en_out_channels,
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)
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self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
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if n_gru:
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self.fc = nn.Sequential(
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BiGRU(3 * 128, 256, n_gru),
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nn.Linear(512, 360),
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nn.Dropout(0.25),
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nn.Sigmoid(),
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)
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else:
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self.fc = nn.Sequential(
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nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
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)
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def forward(self, mel):
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# print(mel.shape)
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mel = mel.transpose(-1, -2).unsqueeze(1)
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x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
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x = self.fc(x)
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# print(x.shape)
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return x
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from librosa.filters import mel
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class MelSpectrogram(torch.nn.Module):
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def __init__(
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self,
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is_half,
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n_mel_channels,
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sampling_rate,
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win_length,
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hop_length,
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n_fft=None,
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mel_fmin=0,
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mel_fmax=None,
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clamp=1e-5,
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):
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super().__init__()
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n_fft = win_length if n_fft is None else n_fft
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self.hann_window = {}
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mel_basis = mel(
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sr=sampling_rate,
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n_fft=n_fft,
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n_mels=n_mel_channels,
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fmin=mel_fmin,
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fmax=mel_fmax,
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htk=True,
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)
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mel_basis = torch.from_numpy(mel_basis).float()
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self.register_buffer("mel_basis", mel_basis)
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self.n_fft = win_length if n_fft is None else n_fft
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self.hop_length = hop_length
|
||
self.win_length = win_length
|
||
self.sampling_rate = sampling_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)
|
||
n_fft_new = int(np.round(self.n_fft * factor))
|
||
win_length_new = int(np.round(self.win_length * factor))
|
||
hop_length_new = int(np.round(self.hop_length * speed))
|
||
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(
|
||
# "cpu"if(audio.device.type=="privateuseone") else audio.device
|
||
audio.device
|
||
)
|
||
# fft = torch.stft(#doesn't support pytorch_dml
|
||
# # audio.cpu() if(audio.device.type=="privateuseone")else audio,
|
||
# audio,
|
||
# n_fft=n_fft_new,
|
||
# hop_length=hop_length_new,
|
||
# 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))
|
||
# print(1111111111)
|
||
# print(222222222222222,audio.device,self.is_half)
|
||
if hasattr(self, "stft") == False:
|
||
# print(n_fft_new,hop_length_new,win_length_new,audio.shape)
|
||
self.stft = STFT(
|
||
filter_length=n_fft_new,
|
||
hop_length=hop_length_new,
|
||
win_length=win_length_new,
|
||
window="hann",
|
||
).to(audio.device)
|
||
magnitude = self.stft.transform(audio) # phase
|
||
# if (audio.device.type == "privateuseone"):
|
||
# magnitude=magnitude.to(audio.device)
|
||
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 == True:
|
||
mel_output = mel_output.half()
|
||
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
|
||
# print(log_mel_spec.device.type)
|
||
return log_mel_spec
|
||
|
||
|
||
class RMVPE:
|
||
def __init__(self, model_path, is_half, device=None):
|
||
self.resample_kernel = {}
|
||
self.resample_kernel = {}
|
||
self.is_half = is_half
|
||
if device is None:
|
||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
self.device = device
|
||
self.mel_extractor = MelSpectrogram(
|
||
is_half, 128, 16000, 1024, 160, None, 30, 8000
|
||
).to(device)
|
||
if "privateuseone" in str(device):
|
||
import onnxruntime as ort
|
||
|
||
ort_session = ort.InferenceSession(
|
||
"rmvpe.onnx", providers=["DmlExecutionProvider"]
|
||
)
|
||
self.model = ort_session
|
||
else:
|
||
model = E2E(4, 1, (2, 2))
|
||
ckpt = torch.load(model_path, map_location="cpu")
|
||
model.load_state_dict(ckpt)
|
||
model.eval()
|
||
if is_half == True:
|
||
model = model.half()
|
||
self.model = model
|
||
self.model = self.model.to(device)
|
||
cents_mapping = 20 * np.arange(360) + 1997.3794084376191
|
||
self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
|
||
|
||
def mel2hidden(self, mel):
|
||
with torch.no_grad():
|
||
n_frames = mel.shape[-1]
|
||
mel = F.pad(
|
||
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
|
||
)
|
||
if "privateuseone" in str(self.device):
|
||
onnx_input_name = self.model.get_inputs()[0].name
|
||
onnx_outputs_names = self.model.get_outputs()[0].name
|
||
hidden = self.model.run(
|
||
[onnx_outputs_names],
|
||
input_feed={onnx_input_name: mel.cpu().numpy()},
|
||
)[0]
|
||
else:
|
||
hidden = self.model(mel)
|
||
return hidden[:, :n_frames]
|
||
|
||
def decode(self, hidden, thred=0.03):
|
||
cents_pred = self.to_local_average_cents(hidden, thred=thred)
|
||
f0 = 10 * (2 ** (cents_pred / 1200))
|
||
f0[f0 == 10] = 0
|
||
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
|
||
return f0
|
||
|
||
def infer_from_audio(self, audio, thred=0.03):
|
||
# torch.cuda.synchronize()
|
||
t0 = ttime()
|
||
mel = self.mel_extractor(
|
||
torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True
|
||
)
|
||
# print(123123123,mel.device.type)
|
||
# torch.cuda.synchronize()
|
||
t1 = ttime()
|
||
hidden = self.mel2hidden(mel)
|
||
# torch.cuda.synchronize()
|
||
t2 = ttime()
|
||
# print(234234,hidden.device.type)
|
||
if "privateuseone" not in str(self.device):
|
||
hidden = hidden.squeeze(0).cpu().numpy()
|
||
else:
|
||
hidden = hidden[0]
|
||
if self.is_half == True:
|
||
hidden = hidden.astype("float32")
|
||
|
||
f0 = self.decode(hidden, thred=thred)
|
||
# torch.cuda.synchronize()
|
||
t3 = ttime()
|
||
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
|
||
return f0
|
||
|
||
def to_local_average_cents(self, salience, thred=0.05):
|
||
# t0 = ttime()
|
||
center = np.argmax(salience, axis=1) # 帧长#index
|
||
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
|
||
# t1 = ttime()
|
||
center += 4
|
||
todo_salience = []
|
||
todo_cents_mapping = []
|
||
starts = center - 4
|
||
ends = center + 5
|
||
for idx in range(salience.shape[0]):
|
||
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
|
||
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
|
||
# t2 = ttime()
|
||
todo_salience = np.array(todo_salience) # 帧长，9
|
||
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9
|
||
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
|
||
weight_sum = np.sum(todo_salience, 1) # 帧长
|
||
devided = product_sum / weight_sum # 帧长
|
||
# t3 = ttime()
|
||
maxx = np.max(salience, axis=1) # 帧长
|
||
devided[maxx <= thred] = 0
|
||
# t4 = ttime()
|
||
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||
return devided
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import librosa
|
||
import soundfile as sf
|
||
|
||
audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
|
||
if len(audio.shape) > 1:
|
||
audio = librosa.to_mono(audio.transpose(1, 0))
|
||
audio_bak = audio.copy()
|
||
if sampling_rate != 16000:
|
||
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
||
model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
|
||
thred = 0.03 # 0.01
|
||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
rmvpe = RMVPE(model_path, is_half=False, device=device)
|
||
t0 = ttime()
|
||
f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||
t1 = ttime()
|
||
print(f0.shape, t1 - t0)
|