from io import BytesIO import os from typing import List, Optional, Tuple import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from librosa.util import normalize, pad_center, tiny from scipy.signal import get_window import logging logger = logging.getLogger(__name__) class STFT(torch.nn.Module): def __init__( self, filter_length=1024, hop_length=512, win_length=None, window="hann" ): """ This module implements an STFT using 1D convolution and 1D transpose convolutions. This is a bit tricky so there are some cases that probably won't work as working out the same sizes before and after in all overlap add setups is tough. Right now, this code should work with hop lengths that are half the filter length (50% overlap between frames). Keyword Arguments: filter_length {int} -- Length of filters used (default: {1024}) hop_length {int} -- Hop length of STFT (restrict to 50% overlap between frames) (default: {512}) win_length {[type]} -- Length of the window function applied to each frame (if not specified, it equals the filter length). (default: {None}) window {str} -- Type of window to use (options are bartlett, hann, hamming, blackman, blackmanharris) (default: {'hann'}) """ super(STFT, self).__init__() self.filter_length = filter_length self.hop_length = hop_length self.win_length = win_length if win_length else filter_length self.window = window self.forward_transform = None self.pad_amount = int(self.filter_length / 2) fourier_basis = np.fft.fft(np.eye(self.filter_length)) cutoff = int((self.filter_length / 2 + 1)) fourier_basis = np.vstack( [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])] ) forward_basis = torch.FloatTensor(fourier_basis) inverse_basis = torch.FloatTensor(np.linalg.pinv(fourier_basis)) assert filter_length >= self.win_length # get window and zero center pad it to filter_length fft_window = get_window(window, self.win_length, fftbins=True) fft_window = pad_center(fft_window, size=filter_length) fft_window = torch.from_numpy(fft_window).float() # window the bases forward_basis *= fft_window inverse_basis = (inverse_basis.T * fft_window).T self.register_buffer("forward_basis", forward_basis.float()) self.register_buffer("inverse_basis", inverse_basis.float()) self.register_buffer("fft_window", fft_window.float()) def transform(self, input_data, return_phase=False): """Take input data (audio) to STFT domain. Arguments: input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples) Returns: magnitude {tensor} -- Magnitude of STFT with shape (num_batch, num_frequencies, num_frames) phase {tensor} -- Phase of STFT with shape (num_batch, num_frequencies, num_frames) """ input_data = F.pad( input_data, (self.pad_amount, self.pad_amount), mode="reflect", ) forward_transform = input_data.unfold( 1, self.filter_length, self.hop_length ).permute(0, 2, 1) forward_transform = torch.matmul(self.forward_basis, forward_transform) cutoff = int((self.filter_length / 2) + 1) real_part = forward_transform[:, :cutoff, :] imag_part = forward_transform[:, cutoff:, :] magnitude = torch.sqrt(real_part**2 + imag_part**2) if return_phase: phase = torch.atan2(imag_part.data, real_part.data) return magnitude, phase else: return magnitude def inverse(self, magnitude, phase): """Call the inverse STFT (iSTFT), given magnitude and phase tensors produced by the ```transform``` function. Arguments: magnitude {tensor} -- Magnitude of STFT with shape (num_batch, num_frequencies, num_frames) phase {tensor} -- Phase of STFT with shape (num_batch, num_frequencies, num_frames) Returns: inverse_transform {tensor} -- Reconstructed audio given magnitude and phase. Of shape (num_batch, num_samples) """ cat = torch.cat( [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 ) fold = torch.nn.Fold( output_size=(1, (cat.size(-1) - 1) * self.hop_length + self.filter_length), kernel_size=(1, self.filter_length), stride=(1, self.hop_length), ) inverse_transform = torch.matmul(self.inverse_basis, cat) inverse_transform = fold(inverse_transform)[ :, 0, 0, self.pad_amount : -self.pad_amount ] window_square_sum = ( self.fft_window.pow(2).repeat(cat.size(-1), 1).T.unsqueeze(0) ) window_square_sum = fold(window_square_sum)[ :, 0, 0, self.pad_amount : -self.pad_amount ] inverse_transform /= window_square_sum return inverse_transform def forward(self, input_data): """Take input data (audio) to STFT domain and then back to audio. Arguments: input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples) Returns: reconstruction {tensor} -- Reconstructed audio given magnitude and phase. Of shape (num_batch, num_samples) """ self.magnitude, self.phase = self.transform(input_data, return_phase=True) reconstruction = self.inverse(self.magnitude, self.phase) return reconstruction from time import time as ttime class BiGRU(nn.Module): def __init__(self, input_features, hidden_features, num_layers): super(BiGRU, self).__init__() self.gru = nn.GRU( input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True, ) def forward(self, x): return self.gru(x)[0] class ConvBlockRes(nn.Module): def __init__(self, in_channels, out_channels, momentum=0.01): super(ConvBlockRes, self).__init__() self.conv = nn.Sequential( nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False, ), nn.BatchNorm2d(out_channels, momentum=momentum), nn.ReLU(), nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False, ), nn.BatchNorm2d(out_channels, momentum=momentum), nn.ReLU(), ) # self.shortcut:Optional[nn.Module] = None if in_channels != out_channels: self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1)) def forward(self, x: torch.Tensor): if not hasattr(self, "shortcut"): return self.conv(x) + x else: return self.conv(x) + self.shortcut(x) class Encoder(nn.Module): def __init__( self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01, ): super(Encoder, self).__init__() self.n_encoders = n_encoders self.bn = nn.BatchNorm2d(in_channels, momentum=momentum) self.layers = nn.ModuleList() self.latent_channels = [] for i in range(self.n_encoders): self.layers.append( ResEncoderBlock( in_channels, out_channels, kernel_size, n_blocks, momentum=momentum ) ) self.latent_channels.append([out_channels, in_size]) in_channels = out_channels out_channels *= 2 in_size //= 2 self.out_size = in_size self.out_channel = out_channels def forward(self, x: torch.Tensor): concat_tensors: List[torch.Tensor] = [] x = self.bn(x) for i, layer in enumerate(self.layers): t, x = layer(x) concat_tensors.append(t) return x, concat_tensors class ResEncoderBlock(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01 ): super(ResEncoderBlock, self).__init__() self.n_blocks = n_blocks self.conv = nn.ModuleList() self.conv.append(ConvBlockRes(in_channels, out_channels, momentum)) for i in range(n_blocks - 1): self.conv.append(ConvBlockRes(out_channels, out_channels, momentum)) self.kernel_size = kernel_size if self.kernel_size is not None: self.pool = nn.AvgPool2d(kernel_size=kernel_size) def forward(self, x): for i, conv in enumerate(self.conv): x = conv(x) if self.kernel_size is not None: return x, self.pool(x) else: return x class Intermediate(nn.Module): # def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01): super(Intermediate, self).__init__() self.n_inters = n_inters self.layers = nn.ModuleList() self.layers.append( ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum) ) for i in range(self.n_inters - 1): self.layers.append( ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum) ) def forward(self, x): for i, layer in enumerate(self.layers): x = layer(x) return x class ResDecoderBlock(nn.Module): def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01): super(ResDecoderBlock, self).__init__() out_padding = (0, 1) if stride == (1, 2) else (1, 1) self.n_blocks = n_blocks self.conv1 = nn.Sequential( nn.ConvTranspose2d( in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), stride=stride, padding=(1, 1), output_padding=out_padding, bias=False, ), nn.BatchNorm2d(out_channels, momentum=momentum), nn.ReLU(), ) self.conv2 = nn.ModuleList() self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum)) for i in range(n_blocks - 1): self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum)) def forward(self, x, concat_tensor): x = self.conv1(x) x = torch.cat((x, concat_tensor), dim=1) for i, conv2 in enumerate(self.conv2): x = conv2(x) return x class Decoder(nn.Module): def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01): super(Decoder, self).__init__() self.layers = nn.ModuleList() self.n_decoders = n_decoders for i in range(self.n_decoders): out_channels = in_channels // 2 self.layers.append( ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum) ) in_channels = out_channels def forward(self, x: torch.Tensor, concat_tensors: List[torch.Tensor]): for i, layer in enumerate(self.layers): x = layer(x, concat_tensors[-1 - i]) return x class DeepUnet(nn.Module): def __init__( self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16, ): super(DeepUnet, self).__init__() self.encoder = Encoder( in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels ) self.intermediate = Intermediate( self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks, ) self.decoder = Decoder( self.encoder.out_channel, en_de_layers, kernel_size, n_blocks ) def forward(self, x: torch.Tensor) -> torch.Tensor: x, concat_tensors = self.encoder(x) x = self.intermediate(x) x = self.decoder(x, concat_tensors) return x class E2E(nn.Module): def __init__( self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16, ): super(E2E, self).__init__() self.unet = DeepUnet( kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels, ) self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1)) if n_gru: self.fc = nn.Sequential( BiGRU(3 * 128, 256, n_gru), nn.Linear(512, 360), nn.Dropout(0.25), nn.Sigmoid(), ) else: self.fc = nn.Sequential( nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid() ) def forward(self, mel): # print(mel.shape) mel = mel.transpose(-1, -2).unsqueeze(1) x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2) x = self.fc(x) # print(x.shape) return x from librosa.filters import mel class MelSpectrogram(torch.nn.Module): def __init__( self, is_half, n_mel_channels, sampling_rate, win_length, hop_length, n_fft=None, mel_fmin=0, mel_fmax=None, clamp=1e-5, ): super().__init__() n_fft = win_length if n_fft is None else n_fft self.hann_window = {} mel_basis = mel( sr=sampling_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=mel_fmin, fmax=mel_fmax, htk=True, ) mel_basis = torch.from_numpy(mel_basis).float() self.register_buffer("mel_basis", mel_basis) self.n_fft = win_length if n_fft is None else n_fft 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( audio.device ) if "privateuseone" in str(audio.device): if not hasattr(self, "stft"): 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) else: fft = torch.stft( 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)) 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)) return log_mel_spec class RMVPE: def __init__(self, model_path: str, is_half, device=None, use_jit=False): self.resample_kernel = {} self.resample_kernel = {} self.is_half = is_half if device is None: device = "cuda:0" 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( "%s/rmvpe.onnx" % os.environ["rmvpe_root"], providers=["DmlExecutionProvider"], ) self.model = ort_session else: if str(self.device) == "cuda": self.device = torch.device("cuda:0") def get_default_model(): model = E2E(4, 1, (2, 2)) ckpt = torch.load(model_path, map_location="cpu") model.load_state_dict(ckpt) model.eval() if is_half: model = model.half() else: model = model.float() return model self.model = get_default_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] n_pad = 32 * ((n_frames - 1) // 32 + 1) - n_frames if n_pad > 0: mel = F.pad(mel, (0, n_pad), mode="constant") 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: mel = mel.half() if self.is_half else mel.float() 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() if not torch.is_tensor(audio): audio = torch.from_numpy(audio) mel = self.mel_extractor( 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