WIP: RMVPE

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]
                 }
             }

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]
                 }
             }

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,
 )
 

server/const.py

@@ -77,6 +77,7 @@ PitchExtractorType: TypeAlias = Literal[
     "crepe",
     "crepe_full",
     "crepe_tiny",
+    "rmvpe",
 ]
 
 ServerAudioDeviceType: TypeAlias = Literal[

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)
 

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 {}

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):
 

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.DiffusionSVC.pitchExtractor.PitchExtractor import PitchExtractor
-from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
 
 
 class CrepePitchExtractor(PitchExtractor):

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(

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

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
+
+
+

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

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
+