| import torch | |
| import numpy as np | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from librosa.filters import mel | |
| N_MELS, N_CLASS = 128, 360 | |
| 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()) | |
| if in_channels != out_channels: | |
| self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1)) | |
| self.is_shortcut = True | |
| else: self.is_shortcut = False | |
| def forward(self, x): | |
| return self.conv(x) + self.shortcut(x) if self.is_shortcut else self.conv(x) + x | |
| 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 _ 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 in range(self.n_blocks): | |
| x = self.conv[i](x) | |
| if self.kernel_size is not None: return x, self.pool(x) | |
| else: return 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 _ 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): | |
| concat_tensors = [] | |
| x = self.bn(x) | |
| for i in range(self.n_encoders): | |
| t, x = self.layers[i](x) | |
| concat_tensors.append(t) | |
| return x, concat_tensors | |
| 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 _ in range(self.n_inters - 1): | |
| self.layers.append(ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)) | |
| def forward(self, x): | |
| for i in range(self.n_inters): | |
| x = self.layers[i](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 _ in range(n_blocks - 1): | |
| self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum)) | |
| def forward(self, x, concat_tensor): | |
| x = torch.cat((self.conv1(x), concat_tensor), dim=1) | |
| for i in range(self.n_blocks): | |
| x = self.conv2[i](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 _ 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, concat_tensors): | |
| for i in range(self.n_decoders): | |
| x = self.layers[i](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): | |
| x, concat_tensors = self.encoder(x) | |
| return self.decoder(self.intermediate(x), concat_tensors) | |
| 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)) | |
| self.fc = nn.Sequential(BiGRU(3 * 128, 256, n_gru), nn.Linear(512, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()) if n_gru else nn.Sequential(nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()) | |
| def forward(self, mel): | |
| return self.fc(self.cnn(self.unet(mel.transpose(-1, -2).unsqueeze(1))).transpose(1, 2).flatten(-2)) | |
| class MelSpectrogram(torch.nn.Module): | |
| def __init__(self, n_mel_channels, sample_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=sample_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.sample_rate = sample_rate | |
| self.n_mel_channels = n_mel_channels | |
| self.clamp = clamp | |
| def forward(self, audio, keyshift=0, speed=1, center=True): | |
| factor = 2 ** (keyshift / 12) | |
| win_length_new = int(np.round(self.win_length * factor)) | |
| 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) | |
| fft = torch.stft(audio, n_fft=int(np.round(self.n_fft * factor)), hop_length=int(np.round(self.hop_length * speed)), 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) | |
| return torch.log(torch.clamp(mel_output, min=self.clamp)) | |
| class RMVPE: | |
| def __init__(self, model_path, device=None, providers=None, onnx=False): | |
| self.resample_kernel = {} | |
| self.onnx = onnx | |
| if self.onnx: | |
| import onnxruntime as ort | |
| sess_options = ort.SessionOptions() | |
| sess_options.log_severity_level = 3 | |
| self.model = ort.InferenceSession(model_path, sess_options=sess_options, providers=providers) | |
| else: | |
| model = E2E(4, 1, (2, 2)) | |
| ckpt = torch.load(model_path, map_location="cpu") | |
| model.load_state_dict(ckpt) | |
| model.eval() | |
| self.model = model.to(device) | |
| self.resample_kernel = {} | |
| self.device = device | |
| self.mel_extractor = MelSpectrogram(N_MELS, 16000, 1024, 160, None, 30, 8000).to(device) | |
| cents_mapping = 20 * np.arange(N_CLASS) + 1997.3794084376191 | |
| self.cents_mapping = np.pad(cents_mapping, (4, 4)) | |
| 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") | |
| hidden = self.model.run([self.model.get_outputs()[0].name], input_feed={self.model.get_inputs()[0].name: mel.cpu().numpy()})[0] if self.onnx else self.model(mel.float()) | |
| return hidden[:, :n_frames] | |
| def decode(self, hidden, thred=0.03): | |
| f0 = 10 * (2 ** (self.to_local_average_cents(hidden, thred=thred) / 1200)) | |
| f0[f0 == 10] = 0 | |
| return f0 | |
| def infer_from_audio(self, audio, thred=0.03): | |
| hidden = self.mel2hidden(self.mel_extractor(torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True)) | |
| return self.decode(hidden.squeeze(0).cpu().numpy() if not self.onnx else hidden[0], thred=thred) | |
| def infer_from_audio_with_pitch(self, audio, thred=0.03, f0_min=50, f0_max=1100): | |
| hidden = self.mel2hidden(self.mel_extractor(torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True)) | |
| f0 = self.decode(hidden.squeeze(0).cpu().numpy() if not self.onnx else hidden[0], thred=thred) | |
| f0[(f0 < f0_min) | (f0 > f0_max)] = 0 | |
| return f0 | |
| def to_local_average_cents(self, salience, thred=0.05): | |
| center = np.argmax(salience, axis=1) | |
| salience = np.pad(salience, ((0, 0), (4, 4))) | |
| 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]]) | |
| todo_salience = np.array(todo_salience) | |
| devided = np.sum(todo_salience * np.array(todo_cents_mapping), 1) / np.sum(todo_salience, 1) | |
| devided[np.max(salience, axis=1) <= thred] = 0 | |
| return devided | |
| 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] |