diff --git a/client/demo/dist/assets/gui_settings/GUI.json b/client/demo/dist/assets/gui_settings/GUI.json index e17a8b81..865e8645 100644 --- a/client/demo/dist/assets/gui_settings/GUI.json +++ b/client/demo/dist/assets/gui_settings/GUI.json @@ -21,7 +21,7 @@ { "name": "configArea", "options": { - "detectors": ["dio", "harvest", "crepe", "crepe_full", "crepe_tiny"], + "detectors": ["dio", "harvest", "crepe", "crepe_full", "crepe_tiny", "rmvpe"], "inputChunkNums": [8, 16, 24, 32, 40, 48, 64, 80, 96, 112, 128, 192, 256, 320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 2048] } } diff --git a/client/demo/public/assets/gui_settings/GUI.json b/client/demo/public/assets/gui_settings/GUI.json index e17a8b81..865e8645 100644 --- a/client/demo/public/assets/gui_settings/GUI.json +++ b/client/demo/public/assets/gui_settings/GUI.json @@ -21,7 +21,7 @@ { "name": "configArea", "options": { - "detectors": ["dio", "harvest", "crepe", "crepe_full", "crepe_tiny"], + "detectors": ["dio", "harvest", "crepe", "crepe_full", "crepe_tiny", "rmvpe"], "inputChunkNums": [8, 16, 24, 32, 40, 48, 64, 80, 96, 112, 128, 192, 256, 320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 2048] } } diff --git a/server/MMVCServerSIO.py b/server/MMVCServerSIO.py index f3f40f77..49c23d3b 100755 --- a/server/MMVCServerSIO.py +++ b/server/MMVCServerSIO.py @@ -51,6 +51,7 @@ def setupArgParser(): parser.add_argument("--nsf_hifigan", type=str, help="path to nsf_hifigan model(pytorch)") parser.add_argument("--crepe_onnx_full", type=str, help="path to crepe_onnx_full") parser.add_argument("--crepe_onnx_tiny", type=str, help="path to crepe_onnx_tiny") + parser.add_argument("--rmvpe", type=str, help="path to rmvpe") return parser @@ -90,6 +91,7 @@ voiceChangerParams = VoiceChangerParams( nsf_hifigan=args.nsf_hifigan, crepe_onnx_full=args.crepe_onnx_full, crepe_onnx_tiny=args.crepe_onnx_tiny, + rmvpe=args.rmvpe, sample_mode=args.sample_mode, ) diff --git a/server/const.py b/server/const.py index 9c9fb1e8..9db3c5b6 100644 --- a/server/const.py +++ b/server/const.py @@ -77,6 +77,7 @@ PitchExtractorType: TypeAlias = Literal[ "crepe", "crepe_full", "crepe_tiny", + "rmvpe", ] ServerAudioDeviceType: TypeAlias = Literal[ diff --git a/server/downloader/WeightDownloader.py b/server/downloader/WeightDownloader.py index 153dcb6d..26ca2fc0 100644 --- a/server/downloader/WeightDownloader.py +++ b/server/downloader/WeightDownloader.py @@ -14,6 +14,7 @@ def downloadWeight(voiceChangerParams: VoiceChangerParams): nsf_hifigan = voiceChangerParams.nsf_hifigan crepe_onnx_full = voiceChangerParams.crepe_onnx_full crepe_onnx_tiny = voiceChangerParams.crepe_onnx_tiny + rmvpe = voiceChangerParams.rmvpe # file exists check (currently only for rvc) downloadParams = [] @@ -86,6 +87,15 @@ def downloadWeight(voiceChangerParams: VoiceChangerParams): } ) + if os.path.exists(content_vec_500_onnx) is False: + downloadParams.append( + { + "url": "https://huggingface.co/wok000/weights/resolve/main/rmvpe/rmvpe.pt", + "saveTo": rmvpe, + "position": 8, + } + ) + with ThreadPoolExecutor() as pool: pool.map(download, downloadParams) diff --git a/server/voice_changer/DiffusionSVC/pipeline/Pipeline.py b/server/voice_changer/DiffusionSVC/pipeline/Pipeline.py index bd93f3a1..2d988442 100644 --- a/server/voice_changer/DiffusionSVC/pipeline/Pipeline.py +++ b/server/voice_changer/DiffusionSVC/pipeline/Pipeline.py @@ -55,8 +55,6 @@ class Pipeline(object): self.resamplerIn = resamplerIn self.resamplerOut = resamplerOut - - # self.f0ex = self.load_f0_extractor(f0_model="harvest", f0_min=50, f0_max=1100) print("VOLUME EXTRACTOR", self.volumeExtractor) print("GENERATE INFERENCER", self.inferencer) @@ -67,18 +65,6 @@ class Pipeline(object): self.device = device self.isHalf = False - def load_f0_extractor(self, f0_model, f0_min=None, f0_max=None): - f0_extractor = F0_Extractor( - f0_extractor=f0_model, - sample_rate=44100, - hop_size=512, - f0_min=f0_min, - f0_max=f0_max, - block_size=512, - model_sampling_rate=44100 - ) - return f0_extractor - def getPipelineInfo(self): volumeExtractorInfo = self.volumeExtractor.getVolumeExtractorInfo() inferencerInfo = self.inferencer.getInferencerInfo() if self.inferencer else {} diff --git a/server/voice_changer/DiffusionSVC/pitchExtractor/CrepeOnnxPitchExtractor.py b/server/voice_changer/DiffusionSVC/pitchExtractor/CrepeOnnxPitchExtractor.py index ed97aafa..10190446 100644 --- a/server/voice_changer/DiffusionSVC/pitchExtractor/CrepeOnnxPitchExtractor.py +++ b/server/voice_changer/DiffusionSVC/pitchExtractor/CrepeOnnxPitchExtractor.py @@ -1,11 +1,12 @@ import numpy as np from const import PitchExtractorType +from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractor import PitchExtractor from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager -from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor import onnxruntime -from voice_changer.RVC.pitchExtractor import onnxcrepe import torch +from voice_changer.RVC.pitchExtractor import onnxcrepe + class CrepeOnnxPitchExtractor(PitchExtractor): diff --git a/server/voice_changer/DiffusionSVC/pitchExtractor/CrepePitchExtractor.py b/server/voice_changer/DiffusionSVC/pitchExtractor/CrepePitchExtractor.py index 5ef947a9..2300412c 100644 --- a/server/voice_changer/DiffusionSVC/pitchExtractor/CrepePitchExtractor.py +++ b/server/voice_changer/DiffusionSVC/pitchExtractor/CrepePitchExtractor.py @@ -2,8 +2,7 @@ import torchcrepe import torch import numpy as np from const import PitchExtractorType - -from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor +from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractor import PitchExtractor class CrepePitchExtractor(PitchExtractor): diff --git a/server/voice_changer/DiffusionSVC/pitchExtractor/PitchExtractorManager.py b/server/voice_changer/DiffusionSVC/pitchExtractor/PitchExtractorManager.py index a325fac9..42d29142 100644 --- a/server/voice_changer/DiffusionSVC/pitchExtractor/PitchExtractorManager.py +++ b/server/voice_changer/DiffusionSVC/pitchExtractor/PitchExtractorManager.py @@ -5,6 +5,7 @@ from voice_changer.DiffusionSVC.pitchExtractor.CrepePitchExtractor import CrepeP from voice_changer.DiffusionSVC.pitchExtractor.DioPitchExtractor import DioPitchExtractor from voice_changer.DiffusionSVC.pitchExtractor.HarvestPitchExtractor import HarvestPitchExtractor from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractor import PitchExtractor +from voice_changer.DiffusionSVC.pitchExtractor.RMVPEPitchExtractor import RMVPEPitchExtractor from voice_changer.utils.VoiceChangerParams import VoiceChangerParams @@ -37,6 +38,8 @@ class PitchExtractorManager(Protocol): return CrepeOnnxPitchExtractor(pitchExtractorType, cls.params.crepe_onnx_tiny, gpu) elif pitchExtractorType == "crepe_full": return CrepeOnnxPitchExtractor(pitchExtractorType, cls.params.crepe_onnx_full, gpu) + elif pitchExtractorType == "rmvpe": + return RMVPEPitchExtractor(cls.params.rmvpe, gpu) else: # return hubert as default raise RuntimeError( diff --git a/server/voice_changer/DiffusionSVC/pitchExtractor/RMVPEPitchExtractor.py b/server/voice_changer/DiffusionSVC/pitchExtractor/RMVPEPitchExtractor.py new file mode 100644 index 00000000..ce543b4b --- /dev/null +++ b/server/voice_changer/DiffusionSVC/pitchExtractor/RMVPEPitchExtractor.py @@ -0,0 +1,61 @@ +import torch +import numpy as np +from const import PitchExtractorType +from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractor import PitchExtractor +from voice_changer.DiffusionSVC.pitchExtractor.rmvpe.rmvpe import RMVPE + + +class RMVPEPitchExtractor(PitchExtractor): + + def __init__(self, file: str, gpu: int): + super().__init__() + self.pitchExtractorType: PitchExtractorType = "rmvpe" + self.f0_min = 50 + self.f0_max = 1100 + self.sapmle_rate = 16000 + self.uv_interp = True + if torch.cuda.is_available() and gpu >= 0: + self.device = torch.device("cuda:" + str(torch.cuda.current_device())) + else: + self.device = torch.device("cpu") + + self.rmvpe = RMVPE(model_path=file, is_half=False, device=self.device) + + def extract(self, audio: torch.Tensor, pitch, f0_up_key, window, silence_front=0): + start_frame = int(silence_front * self.sapmle_rate / window) + real_silence_front = start_frame * window / self.sapmle_rate + audio = audio[int(np.round(real_silence_front * self.sapmle_rate)):] + + print("[RMVPE AUDI]", audio.device) + print("[RMVPE RMVPE]", self.rmvpe.device) + + f0 = self.rmvpe.infer_from_audio_t(audio, thred=0.03) + # f0, pd = torchcrepe.predict( + # audio.unsqueeze(0), + # self.sapmle_rate, + # hop_length=window, + # fmin=self.f0_min, + # fmax=self.f0_max, + # # model="tiny", + # model="full", + # batch_size=256, + # decoder=torchcrepe.decode.weighted_argmax, + # device=self.device, + # return_periodicity=True, + # ) + # f0 = torchcrepe.filter.median(f0, 3) # 本家だとmeanですが、harvestに合わせmedianフィルタ + # pd = torchcrepe.filter.median(pd, 3) + # f0[pd < 0.1] = 0 + # f0 = f0.squeeze() + pitch[-f0.shape[0]:] = f0[:pitch.shape[0]] + f0 = pitch + + if self.uv_interp: + uv = f0 == 0 + if len(f0[~uv]) > 0: + f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) + f0[f0 < self.f0_min] = self.f0_min + + f0 = f0 * 2 ** (float(f0_up_key) / 12) + + return f0 diff --git a/server/voice_changer/DiffusionSVC/pitchExtractor/rmvpe/readme.txt b/server/voice_changer/DiffusionSVC/pitchExtractor/rmvpe/readme.txt new file mode 100644 index 00000000..1abe3fff --- /dev/null +++ b/server/voice_changer/DiffusionSVC/pitchExtractor/rmvpe/readme.txt @@ -0,0 +1,4 @@ +modules in this folder from https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI at 86ed98aacaa8b2037aad795abd11cdca122cf39f + + + diff --git a/server/voice_changer/DiffusionSVC/pitchExtractor/rmvpe/rmvpe.py b/server/voice_changer/DiffusionSVC/pitchExtractor/rmvpe/rmvpe.py new file mode 100644 index 00000000..8d36e504 --- /dev/null +++ b/server/voice_changer/DiffusionSVC/pitchExtractor/rmvpe/rmvpe.py @@ -0,0 +1,420 @@ +import torch.nn as nn +import torch.nn.functional as F +import torch +import numpy as np +from librosa.filters import mel + + +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(), + ) + 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): + if self.is_shortcut: + return self.conv(x) + self.shortcut(x) + else: + return self.conv(x) + 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): + concat_tensors = [] + x = self.bn(x) + for i in range(self.n_encoders): + _, x = self.layers[i](x) + concat_tensors.append(_) + 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 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 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 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 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 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 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, 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) + 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: + print("N_GRUE") + 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 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid() + # ) + + def forward(self, mel): + mel = mel.transpose(-1, -2).unsqueeze(1) + x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2) + x = self.fc(x) + return x + + +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 + ) + 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 is 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, is_half, device=None): + self.resample_kernel = {} + model = E2E(4, 1, (2, 2)) + ckpt = torch.load(model_path, map_location="cpu") + model.load_state_dict(ckpt) + model.eval() + if is_half is True: + model = model.half() + self.model = model + 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) + 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" + ) + 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): + audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0) + # torch.cuda.synchronize() + # t0=ttime() + mel = self.mel_extractor(audio, center=True) + # torch.cuda.synchronize() + # t1=ttime() + hidden = self.mel2hidden(mel) + # torch.cuda.synchronize() + # t2=ttime() + hidden = hidden.squeeze(0).cpu().numpy() + if self.is_half is 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 infer_from_audio_t(self, audio, thred=0.03): + # audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0) + mel = self.mel_extractor(audio.unsqueeze(0), center=True) + hidden = self.mel2hidden(mel) + hidden = hidden.squeeze(0).cpu().numpy() + if self.is_half is True: + hidden = hidden.astype("float32") + f0 = self.decode(hidden, thred=thred) + 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]) # NOQA + todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]]) # NOQA + # 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 diff --git a/server/voice_changer/utils/VoiceChangerParams.py b/server/voice_changer/utils/VoiceChangerParams.py index 429d1627..b640227e 100644 --- a/server/voice_changer/utils/VoiceChangerParams.py +++ b/server/voice_changer/utils/VoiceChangerParams.py @@ -14,3 +14,5 @@ class VoiceChangerParams: sample_mode: str crepe_onnx_full: str crepe_onnx_tiny: str + rmvpe: str +