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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
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Merge branch 'clean' of https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI into clean
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@ -1,14 +1,23 @@
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import torch, numpy as np,pdb
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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 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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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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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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@ -41,18 +50,20 @@ def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
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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 = 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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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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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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@ -79,12 +90,15 @@ class STFT(torch.nn.Module):
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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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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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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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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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@ -94,8 +108,8 @@ class STFT(torch.nn.Module):
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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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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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@ -117,23 +131,25 @@ class STFT(torch.nn.Module):
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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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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,
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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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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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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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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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@ -150,30 +166,39 @@ class STFT(torch.nn.Module):
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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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[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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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, 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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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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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[approx_nonzero_indices]
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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[..., 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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@ -191,7 +216,11 @@ class STFT(torch.nn.Module):
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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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@ -509,14 +538,14 @@ class MelSpectrogram(torch.nn.Module):
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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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# 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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window="hann",
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).to(audio.device)
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magnitude = self.stft.transform(audio)#phase
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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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@ -544,10 +573,13 @@ 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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if ("privateuseone" in str(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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ort_session = ort.InferenceSession(
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"rmvpe.onnx", providers=["DmlExecutionProvider"]
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)
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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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@ -566,10 +598,13 @@ 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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if("privateuseone" in str(self.device) ):
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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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hidden = self.model.run(
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[onnx_outputs_names],
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input_feed={onnx_input_name: mel.cpu().numpy()},
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)[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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@ -583,25 +618,27 @@ class RMVPE:
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def infer_from_audio(self, audio, thred=0.03):
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# torch.cuda.synchronize()
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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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t0 = ttime()
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mel = self.mel_extractor(
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torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True
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)
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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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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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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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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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@ -632,8 +669,9 @@ class RMVPE:
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return devided
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if __name__ == '__main__':
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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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@ -642,13 +680,13 @@ if __name__ == '__main__':
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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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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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t1 = ttime()
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print(f0.shape, t1 - t0)
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@ -31,4 +31,3 @@ def load_hubert(config):
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else:
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hubert_model = hubert_model.float()
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return hubert_model.eval()
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