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lib/rmvpe.py
315
lib/rmvpe.py
@ -1,8 +1,197 @@
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import torch, numpy as np
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import torch, numpy as np,pdb
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import torch.nn as nn
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import torch.nn.functional as F
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import torch,pdb
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import numpy as np
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import torch.nn.functional as F
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from scipy.signal import get_window
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from librosa.util import pad_center, tiny,normalize
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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(window, n_frames, hop_length=200, win_length=800,
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n_fft=800, dtype=np.float32, norm=None):
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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__(self, filter_length=1024, hop_length=512, win_length=None,
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window='hann'):
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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([np.real(fourier_basis[:cutoff, :]),np.imag(fourier_basis[:cutoff, :])])
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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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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(input_data.unsqueeze(1),(self.pad_amount, self.pad_amount, 0, 0,0,0),mode='reflect').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,
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self.forward_basis,
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stride=self.hop_length,
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padding=0)
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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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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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if self.window is not None:
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window_sum = window_sumsquare(
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self.window, magnitude.size(-1), hop_length=self.hop_length,
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win_length=self.win_length, n_fft=self.filter_length,
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dtype=np.float32)
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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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window_sum = torch.from_numpy(window_sum).to(inverse_transform.device)
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inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
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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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@ -250,9 +439,11 @@ class E2E(nn.Module):
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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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@ -301,18 +492,33 @@ class MelSpectrogram(torch.nn.Module):
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keyshift_key = str(keyshift) + "_" + str(audio.device)
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if keyshift_key not in self.hann_window:
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self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
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# "cpu"if(audio.device.type=="privateuseone") else audio.device
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audio.device
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)
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fft = torch.stft(
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audio,
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n_fft=n_fft_new,
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hop_length=hop_length_new,
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win_length=win_length_new,
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window=self.hann_window[keyshift_key],
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center=center,
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return_complex=True,
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)
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magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
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# fft = torch.stft(#doesn't support pytorch_dml
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# # audio.cpu() if(audio.device.type=="privateuseone")else audio,
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# audio,
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# n_fft=n_fft_new,
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# hop_length=hop_length_new,
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# win_length=win_length_new,
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# window=self.hann_window[keyshift_key],
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# center=center,
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# return_complex=True,
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# )
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# magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
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# print(1111111111)
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# print(222222222222222,audio.device,self.is_half)
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if hasattr(self, "stft") == False:
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# print(n_fft_new,hop_length_new,win_length_new,audio.shape)
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self.stft=STFT(
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filter_length=n_fft_new,
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hop_length=hop_length_new,
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win_length=win_length_new,
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window='hann'
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).to(audio.device)
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magnitude = self.stft.transform(audio)#phase
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# if (audio.device.type == "privateuseone"):
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# magnitude=magnitude.to(audio.device)
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if keyshift != 0:
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size = self.n_fft // 2 + 1
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resize = magnitude.size(1)
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@ -323,19 +529,13 @@ class MelSpectrogram(torch.nn.Module):
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if self.is_half == True:
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mel_output = mel_output.half()
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log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
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# print(log_mel_spec.device.type)
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return log_mel_spec
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class RMVPE:
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def __init__(self, model_path, is_half, device=None):
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self.resample_kernel = {}
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model = E2E(4, 1, (2, 2))
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ckpt = torch.load(model_path, map_location="cpu")
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model.load_state_dict(ckpt)
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model.eval()
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if is_half == True:
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model = model.half()
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self.model = model
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self.resample_kernel = {}
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self.is_half = is_half
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if device is None:
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@ -344,7 +544,19 @@ class RMVPE:
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self.mel_extractor = MelSpectrogram(
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is_half, 128, 16000, 1024, 160, None, 30, 8000
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).to(device)
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self.model = self.model.to(device)
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if ("privateuseone" in str(device)):
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import onnxruntime as ort
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ort_session = ort.InferenceSession("rmvpe.onnx", providers=["DmlExecutionProvider"])
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self.model=ort_session
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else:
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model = E2E(4, 1, (2, 2))
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ckpt = torch.load(model_path, map_location="cpu")
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model.load_state_dict(ckpt)
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model.eval()
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if is_half == True:
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model = model.half()
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self.model = model
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self.model = self.model.to(device)
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cents_mapping = 20 * np.arange(360) + 1997.3794084376191
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self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
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@ -354,7 +566,12 @@ class RMVPE:
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mel = F.pad(
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mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
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)
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hidden = self.model(mel)
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if("privateuseone" in str(self.device) ):
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onnx_input_name = self.model.get_inputs()[0].name
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onnx_outputs_names = self.model.get_outputs()[0].name
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hidden = self.model.run([onnx_outputs_names], input_feed={onnx_input_name: mel.cpu().numpy()})[0]
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else:
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hidden = self.model(mel)
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return hidden[:, :n_frames]
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def decode(self, hidden, thred=0.03):
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@ -365,21 +582,26 @@ class RMVPE:
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return f0
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def infer_from_audio(self, audio, thred=0.03):
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audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
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# torch.cuda.synchronize()
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# t0=ttime()
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mel = self.mel_extractor(audio, center=True)
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t0=ttime()
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mel = self.mel_extractor(torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True)
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# print(123123123,mel.device.type)
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# torch.cuda.synchronize()
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# t1=ttime()
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t1=ttime()
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hidden = self.mel2hidden(mel)
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# torch.cuda.synchronize()
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# t2=ttime()
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hidden = hidden.squeeze(0).cpu().numpy()
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t2=ttime()
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# print(234234,hidden.device.type)
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if("privateuseone" not in str(self.device)):
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hidden = hidden.squeeze(0).cpu().numpy()
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else:
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hidden=hidden[0]
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if self.is_half == True:
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hidden = hidden.astype("float32")
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f0 = self.decode(hidden, thred=thred)
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# torch.cuda.synchronize()
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# t3=ttime()
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t3=ttime()
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# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
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return f0
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@ -410,22 +632,23 @@ class RMVPE:
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return devided
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# if __name__ == '__main__':
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# audio, sampling_rate = sf.read("卢本伟语录~1.wav")
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# if len(audio.shape) > 1:
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# audio = librosa.to_mono(audio.transpose(1, 0))
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# audio_bak = audio.copy()
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# if sampling_rate != 16000:
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# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
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# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
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# thred = 0.03 # 0.01
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# device = 'cuda' if torch.cuda.is_available() else 'cpu'
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# rmvpe = RMVPE(model_path,is_half=False, device=device)
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# t0=ttime()
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# t1=ttime()
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# print(f0.shape,t1-t0)
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if __name__ == '__main__':
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import soundfile as sf, librosa
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audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
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if len(audio.shape) > 1:
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audio = librosa.to_mono(audio.transpose(1, 0))
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audio_bak = audio.copy()
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if sampling_rate != 16000:
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
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model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
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thred = 0.03 # 0.01
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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rmvpe = RMVPE(model_path,is_half=False, device=device)
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t0=ttime()
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f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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t1=ttime()
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print(f0.shape,t1-t0)
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