optimize(rtrvc): impl. rvc f0s

gui.py

@@ -645,15 +645,15 @@ if __name__ == "__main__":
                 elif event == "pitch":
                     self.gui_config.pitch = values["pitch"]
                     if hasattr(self, "rvc"):
-                        self.rvc.change_key(values["pitch"])
+                        self.rvc.set_key(values["pitch"])
                 elif event == "formant":
                     self.gui_config.formant = values["formant"]
                     if hasattr(self, "rvc"):
-                        self.rvc.change_formant(values["formant"])
+                        self.rvc.set_formant(values["formant"])
                 elif event == "index_rate":
                     self.gui_config.index_rate = values["index_rate"]
                     if hasattr(self, "rvc"):
-                        self.rvc.change_index_rate(values["index_rate"])
+                        self.rvc.set_index_rate(values["index_rate"])
                 elif event == "rms_mix_rate":
                     self.gui_config.rms_mix_rate = values["rms_mix_rate"]
                 elif event in ["pm", "harvest", "crepe", "rmvpe", "fcpe"]:
@@ -738,10 +738,10 @@ if __name__ == "__main__":
                 self.gui_config.index_path,
                 self.gui_config.index_rate,
                 self.gui_config.n_cpu,
-                inp_q,
+                self.config.device,
-                opt_q,
+                self.config.use_jit,
-                self.config,
+                self.config.is_half,
-                self.rvc if hasattr(self, "rvc") else None,
+                self.config.dml,
             )
             self.gui_config.samplerate = (
                 self.rvc.tgt_sr

infer/lib/rtrvc.py

@@ -1,353 +1,178 @@
 from io import BytesIO
 import os
-import sys
+from typing import Union, Literal, Optional
-import traceback
+
-from infer.lib import jit
-from time import time as ttime
 import fairseq
 import faiss
 import numpy as np
-import parselmouth
-import pyworld
-import scipy.signal as signal
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torchaudio.transforms import Resample
 
+from rvc.f0 import PM, Harvest, RMVPE, CRePE, Dio, FCPE
 from rvc.synthesizer import load_synthesizer
 
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from multiprocessing import Manager as M
 
-from configs import Config
-
-# config = Config()
-
-mm = M()
-
-
-def printt(strr, *args):
-    if len(args) == 0:
-        print(strr)
-    else:
-        print(strr % args)
-
-
-# config.device=torch.device("cpu")########强制cpu测试
-# config.is_half=False########强制cpu测试
 class RVC:
     def __init__(
         self,
-        key,
+        key: Union[int, float],
-        formant,
+        formant: Union[int, float],
-        pth_path,
+        pth_path: torch.serialization.FILE_LIKE,
-        index_path,
+        index_path: str,
-        index_rate,
+        index_rate: Union[int, float],
-        n_cpu,
+        n_cpu: int = os.cpu_count(),
-        inp_q,
+        device: str = "cpu",
-        opt_q,
+        use_jit: bool = False,
-        config: Config,
+        is_half: bool = False,
-        last_rvc=None,
+        is_dml: bool = False,
     ) -> None:
-        """
+        if is_dml:
-        初始化
-        """
-        try:
-            if config.dml == True:
 
-                def forward_dml(ctx, x, scale):
+            def forward_dml(ctx, x, scale):
-                    ctx.scale = scale
+                ctx.scale = scale
-                    res = x.clone().detach()
+                res = x.clone().detach()
-                    return res
+                return res
 
-                fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
+            fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
-            # global config
-            self.config = config
-            self.inp_q = inp_q
-            self.opt_q = opt_q
-            # device="cpu"########强制cpu测试
-            self.device = config.device
-            self.f0_up_key = key
-            self.formant_shift = formant
-            self.f0_min = 50
-            self.f0_max = 1100
-            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-            self.n_cpu = n_cpu
-            self.use_jit = self.config.use_jit
-            self.is_half = config.is_half
 
-            if index_rate != 0:
+        self.device = device
-                self.index = faiss.read_index(index_path)
+        self.f0_up_key = key
-                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
+        self.formant_shift = formant
-                printt("Index search enabled")
+        self.sr = 16000  # hubert sampling rate
-            self.pth_path: str = pth_path
+        self.window = 160  # hop length
-            self.index_path = index_path
+        self.f0_min = 50
-            self.index_rate = index_rate
+        self.f0_max = 1100
-            self.cache_pitch: torch.Tensor = torch.zeros(
+        self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-                1024, device=self.device, dtype=torch.long
+        self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-            )
+        self.n_cpu = n_cpu
-            self.cache_pitchf = torch.zeros(
+        self.use_jit = use_jit
-                1024, device=self.device, dtype=torch.float32
+        self.is_half = is_half
-            )
 
-            self.resample_kernel = {}
+        if index_rate > 0:
+            self.index = faiss.read_index(index_path)
+            self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
 
-            if last_rvc is None:
+        self.pth_path = pth_path
-                models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
+        self.index_path = index_path
-                    ["assets/hubert/hubert_base.pt"],
+        self.index_rate = index_rate
-                    suffix="",
+
-                )
+        self.cache_pitch: torch.Tensor = torch.zeros(
-                hubert_model = models[0]
+            1024, device=self.device, dtype=torch.long
-                hubert_model = hubert_model.to(self.device)
+        )
-                if self.is_half:
+        self.cache_pitchf = torch.zeros(
-                    hubert_model = hubert_model.half()
+            1024, device=self.device, dtype=torch.float32
-                else:
+        )
-                    hubert_model = hubert_model.float()
+
-                hubert_model.eval()
+        self.resample_kernel = {}
-                self.model = hubert_model
+
+        self.f0_methods = {
+            "crepe": self._get_f0_crepe,
+            "rmvpe": self._get_f0_rmvpe,
+            "fcpe": self._get_f0_fcpe,
+            "pm": self._get_f0_pm,
+            "harvest": self._get_f0_harvest,
+            "dio": self._get_f0_dio,
+        }
+
+        models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
+            ["assets/hubert/hubert_base.pt"],
+            suffix="",
+        )
+        hubert_model = models[0]
+        hubert_model = hubert_model.to(self.device)
+        if self.is_half:
+            hubert_model = hubert_model.half()
+        else:
+            hubert_model = hubert_model.float()
+        hubert_model.eval()
+        self.hubert = hubert_model
+
+        self.net_g: Optional[nn.Module] = None
+
+        def set_default_model():
+            self.net_g, cpt = load_synthesizer(self.pth_path, self.device)
+            self.tgt_sr = cpt["config"][-1]
+            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
+            self.if_f0 = cpt.get("f0", 1)
+            self.version = cpt.get("version", "v1")
+            if self.is_half:
+                self.net_g = self.net_g.half()
             else:
-                self.model = last_rvc.model
+                self.net_g = self.net_g.float()
 
-            self.net_g: nn.Module = None
+        def set_jit_model():
+            from rvc.jit import get_jit_model
+            from rvc.synthesizer import synthesizer_jit_export
 
-            def set_default_model():
+            cpt = get_jit_model(self.pth_path, self.is_half, synthesizer_jit_export)
-                self.net_g, cpt = load_synthesizer(self.pth_path, self.device)
-                self.tgt_sr = cpt["config"][-1]
-                cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-                self.if_f0 = cpt.get("f0", 1)
-                self.version = cpt.get("version", "v1")
-                if self.is_half:
-                    self.net_g = self.net_g.half()
-                else:
-                    self.net_g = self.net_g.float()
 
-            def set_jit_model():
+            self.tgt_sr = cpt["config"][-1]
-                from rvc.jit import get_jit_model
+            self.if_f0 = cpt.get("f0", 1)
-                from rvc.synthesizer import synthesizer_jit_export
+            self.version = cpt.get("version", "v1")
+            self.net_g = torch.jit.load(
+                BytesIO(cpt["model"]), map_location=self.device
+            )
+            self.net_g.infer = self.net_g.forward
+            self.net_g.eval().to(self.device)
 
-                cpt = get_jit_model(self.pth_path, self.is_half, synthesizer_jit_export)
+        if self.use_jit and not is_dml and not (self.is_half and "cpu" in str(self.device)):
+            set_jit_model()
+        else:
+            set_default_model()
 
-                self.tgt_sr = cpt["config"][-1]
+    def set_key(self, new_key):
-                self.if_f0 = cpt.get("f0", 1)
-                self.version = cpt.get("version", "v1")
-                self.net_g = torch.jit.load(
-                    BytesIO(cpt["model"]), map_location=self.device
-                )
-                self.net_g.infer = self.net_g.forward
-                self.net_g.eval().to(self.device)
-
-            def set_synthesizer():
-                if self.use_jit and not config.dml:
-                    if self.is_half and "cpu" in str(self.device):
-                        printt(
-                            "Use default Synthesizer model. \
-                                    Jit is not supported on the CPU for half floating point"
-                        )
-                        set_default_model()
-                    else:
-                        set_jit_model()
-                else:
-                    set_default_model()
-
-            if last_rvc is None or last_rvc.pth_path != self.pth_path:
-                set_synthesizer()
-            else:
-                self.tgt_sr = last_rvc.tgt_sr
-                self.if_f0 = last_rvc.if_f0
-                self.version = last_rvc.version
-                self.is_half = last_rvc.is_half
-                if last_rvc.use_jit != self.use_jit:
-                    set_synthesizer()
-                else:
-                    self.net_g = last_rvc.net_g
-
-            if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
-                self.model_rmvpe = last_rvc.model_rmvpe
-            if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
-                self.device_fcpe = last_rvc.device_fcpe
-                self.model_fcpe = last_rvc.model_fcpe
-        except:
-            printt(traceback.format_exc())
-
-    def change_key(self, new_key):
         self.f0_up_key = new_key
 
-    def change_formant(self, new_formant):
+    def set_formant(self, new_formant):
         self.formant_shift = new_formant
 
-    def change_index_rate(self, new_index_rate):
+    def set_index_rate(self, new_index_rate):
-        if new_index_rate != 0 and self.index_rate == 0:
+        if new_index_rate > 0 and self.index_rate <= 0:
             self.index = faiss.read_index(self.index_path)
             self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-            printt("Index search enabled")
         self.index_rate = new_index_rate
 
-    def get_f0_post(self, f0):
-        if not torch.is_tensor(f0):
-            f0 = torch.from_numpy(f0)
-        f0 = f0.float().to(self.device).squeeze()
-        f0_mel = 1127 * torch.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
-            self.f0_mel_max - self.f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = torch.round(f0_mel).long()
-        return f0_coarse, f0
-
-    def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
-        n_cpu = int(n_cpu)
-        if method == "crepe":
-            return self.get_f0_crepe(x, f0_up_key)
-        if method == "rmvpe":
-            return self.get_f0_rmvpe(x, f0_up_key)
-        if method == "fcpe":
-            return self.get_f0_fcpe(x, f0_up_key)
-        x = x.cpu().numpy()
-        if method == "pm":
-            p_len = x.shape[0] // 160 + 1
-            f0_min = 65
-            l_pad = int(np.ceil(1.5 / f0_min * 16000))
-            r_pad = l_pad + 1
-            s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), 16000).to_pitch_ac(
-                time_step=0.01,
-                voicing_threshold=0.6,
-                pitch_floor=f0_min,
-                pitch_ceiling=1100,
-            )
-            assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
-            f0 = s.selected_array["frequency"]
-            if len(f0) < p_len:
-                f0 = np.pad(f0, (0, p_len - len(f0)))
-            f0 = f0[:p_len]
-            f0 *= pow(2, f0_up_key / 12)
-            return self.get_f0_post(f0)
-        if n_cpu == 1:
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=16000,
-                f0_ceil=1100,
-                f0_floor=50,
-                frame_period=10,
-            )
-            f0 = signal.medfilt(f0, 3)
-            f0 *= pow(2, f0_up_key / 12)
-            return self.get_f0_post(f0)
-        f0bak = np.zeros(x.shape[0] // 160 + 1, dtype=np.float64)
-        length = len(x)
-        part_length = 160 * ((length // 160 - 1) // n_cpu + 1)
-        n_cpu = (length // 160 - 1) // (part_length // 160) + 1
-        ts = ttime()
-        res_f0 = mm.dict()
-        for idx in range(n_cpu):
-            tail = part_length * (idx + 1) + 320
-            if idx == 0:
-                self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
-            else:
-                self.inp_q.put(
-                    (idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
-                )
-        while 1:
-            res_ts = self.opt_q.get()
-            if res_ts == ts:
-                break
-        f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
-        for idx, f0 in enumerate(f0s):
-            if idx == 0:
-                f0 = f0[:-3]
-            elif idx != n_cpu - 1:
-                f0 = f0[2:-3]
-            else:
-                f0 = f0[2:]
-            f0bak[part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]] = (
-                f0
-            )
-        f0bak = signal.medfilt(f0bak, 3)
-        f0bak *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0bak)
-
-    def get_f0_crepe(self, x, f0_up_key):
-        if "privateuseone" in str(
-            self.device
-        ):  ###不支持dml，cpu又太慢用不成，拿fcpe顶替
-            return self.get_f0(x, f0_up_key, 1, "fcpe")
-        if hasattr(self, "model_crepe") == False:
-            from rvc.f0 import CRePE
-
-            self.model_crepe = CRePE(
-                160,
-                self.f0_min,
-                self.f0_max,
-                16000,
-                self.device,
-            )
-        f0 = self.model_crepe.compute_f0(x)
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
-    def get_f0_rmvpe(self, x, f0_up_key):
-        if hasattr(self, "model_rmvpe") == False:
-            from rvc.f0 import RMVPE
-
-            printt("Loading rmvpe model")
-            self.model_rmvpe = RMVPE(
-                "assets/rmvpe/rmvpe.pt",
-                is_half=self.is_half,
-                device=self.device,
-                use_jit=self.config.use_jit,
-            )
-        f0 = self.model_rmvpe.compute_f0(x, thred=0.03)
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
-    def get_f0_fcpe(self, x, f0_up_key):
-        if hasattr(self, "model_fcpe") == False:
-            from rvc.f0 import FCPE
-
-            printt("Loading fcpe model")
-            self.model_fcpe = FCPE(
-                160,
-                self.f0_min,
-                self.f0_max,
-                16000,
-                self.device,
-            )
-        f0 = self.model_fcpe.compute_f0(x)
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
     def infer(
         self,
         input_wav: torch.Tensor,
-        block_frame_16k,
+        block_frame_16k: int,
-        skip_head,
+        skip_head: torch.Tensor,
-        return_length,
+        return_length: int,
-        f0method,
+        f0method: Union[tuple, str],
+        inp_f0: Optional[np.ndarray] = None,
+        protect: float = 1.0,
     ) -> np.ndarray:
-        t1 = ttime()
         with torch.no_grad():
-            if self.config.is_half:
+            if self.is_half:
-                feats = input_wav.half().view(1, -1)
+                feats = input_wav.half()
             else:
-                feats = input_wav.float().view(1, -1)
+                feats = input_wav.float()
+            feats = feats.to(self.device)
+            if feats.dim() == 2:  # double channels
+                feats = feats.mean(-1)
+            feats = feats.view(1, -1)
             padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
+
             inputs = {
                 "source": feats,
                 "padding_mask": padding_mask,
                 "output_layer": 9 if self.version == "v1" else 12,
             }
-            logits = self.model.extract_features(**inputs)
+            logits = self.hubert.extract_features(**inputs)
             feats = (
-                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
+                self.hubert.final_proj(logits[0]) if self.version == "v1" else logits[0]
             )
             feats = torch.cat((feats, feats[:, -1:, :]), 1)
-        t2 = ttime()
+            if protect < 0.5 and self.if_f0 == 1:
+                feats0 = feats.clone()
+
         try:
-            if hasattr(self, "index") and self.index_rate != 0:
+            if hasattr(self, "index") and self.index_rate > 0:
-                npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
+                npy = feats[0][skip_head // 2 :].cpu().numpy()
+                if self.is_half:
+                    npy = npy.astype("float32")
                 score, ix = self.index.search(npy, k=8)
                 if (ix >= 0).all():
                     weight = np.square(1 / score)
@@ -355,38 +180,40 @@ class RVC:
                     npy = np.sum(
                         self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
                     )
-                    if self.config.is_half:
+                    if self.is_half:
                         npy = npy.astype("float16")
                     feats[0][skip_head // 2 :] = (
                         torch.from_numpy(npy).unsqueeze(0).to(self.device)
                         * self.index_rate
                         + (1 - self.index_rate) * feats[0][skip_head // 2 :]
                     )
-                else:
-                    printt(
-                        "Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!"
-                    )
-            else:
-                printt("Index search FAILED or disabled")
         except:
-            traceback.print_exc()
+            pass
-            printt("Index search FAILED")
+
-        t3 = ttime()
+        p_len = input_wav.shape[0] // self.window
-        p_len = input_wav.shape[0] // 160
         factor = pow(2, self.formant_shift / 12)
         return_length2 = int(np.ceil(return_length * factor))
         cache_pitch = cache_pitchf = None
-        if self.if_f0 == 1:
+        pitch = pitchf = None
+        if isinstance(f0method, tuple):
+            pitch, pitchf = f0method
+            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
+            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
+        elif self.if_f0 == 1:
             f0_extractor_frame = block_frame_16k + 800
             if f0method == "rmvpe":
-                f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
+                f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - self.window
-            pitch, pitchf = self.get_f0(
+            if inp_f0 is not None:
-                input_wav[-f0_extractor_frame:],
+                pitch, pitchf = self._get_f0_post(inp_f0, self.f0_up_key - self.formant_shift)
-                self.f0_up_key - self.formant_shift,
+            else:
-                self.n_cpu,
+                pitch, pitchf = self._get_f0(
-                f0method,
+                    input_wav[-f0_extractor_frame:],
-            )
+                    self.f0_up_key - self.formant_shift,
-            shift = block_frame_16k // 160
+                    self.n_cpu,
+                    3,
+                    f0method,
+                )
+            shift = block_frame_16k // self.window
             self.cache_pitch[:-shift] = self.cache_pitch[shift:].clone()
             self.cache_pitchf[:-shift] = self.cache_pitchf[shift:].clone()
             self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
@@ -395,13 +222,23 @@ class RVC:
             cache_pitchf = (
                 self.cache_pitchf[None, -p_len:] * return_length2 / return_length
             )
-        t4 = ttime()
+
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
         feats = feats[:, :p_len, :]
+        if protect < 0.5 and pitch is not None and pitchf is not None:
+            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
+                0, 2, 1
+            )
+            feats0 = feats0[:, :p_len, :]
+            pitchff = pitchf.clone()
+            pitchff[pitchf > 0] = 1
+            pitchff[pitchf < 1] = protect
+            pitchff = pitchff.unsqueeze(-1)
+            feats = feats * pitchff + feats0 * (1 - pitchff)
+            feats = feats.to(feats0.dtype)
         p_len = torch.LongTensor([p_len]).to(self.device)
         sid = torch.LongTensor([0]).to(self.device)
         skip_head = torch.LongTensor([skip_head])
-        # return_length2 = torch.LongTensor([return_length2])
         return_length = torch.LongTensor([return_length])
         with torch.no_grad():
             infered_audio = (
@@ -428,12 +265,93 @@ class RVC:
             infered_audio = self.resample_kernel[upp_res](
                 infered_audio[:, : return_length * upp_res]
             )
-        t5 = ttime()
-        printt(
-            "Spent time: fea = %.3fs, index = %.3fs, f0 = %.3fs, model = %.3fs",
-            t2 - t1,
-            t3 - t2,
-            t4 - t3,
-            t5 - t4,
-        )
         return infered_audio.squeeze()
+
+    def _get_f0(
+        self,
+        x: torch.Tensor,
+        f0_up_key: Union[int, float],
+        filter_radius: Union[int, float],
+        method: Literal["crepe", "rmvpe", "fcpe", "pm", "harvest", "dio"]="fcpe",
+    ):
+        if method not in self.f0_methods.keys():
+            raise RuntimeError("Not supported f0 method: "+method)
+        return self.f0_methods[method](x, f0_up_key, filter_radius)
+    
+    def _get_f0_post(self, f0, f0_up_key):
+        f0 *= pow(2, f0_up_key / 12)
+        if not torch.is_tensor(f0):
+            f0 = torch.from_numpy(f0)
+        f0 = f0.float().to(self.device).squeeze()
+        f0_mel = 1127 * torch.log(1 + f0 / 700)
+        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
+            self.f0_mel_max - self.f0_mel_min
+        ) + 1
+        f0_mel[f0_mel <= 1] = 1
+        f0_mel[f0_mel > 255] = 255
+        f0_coarse = torch.round(f0_mel).long()
+        return f0_coarse, f0
+
+    def _get_f0_pm(self, x, f0_up_key, filter_radius):
+        if not hasattr(self, "pm"):
+            self.pm = PM(hop_length=160, sampling_rate=16000)
+        f0 = self.pm.compute_f0(x)
+        return self._get_f0_post(f0, f0_up_key)
+    
+    def _get_f0_harvest(self, x, f0_up_key, filter_radius):
+        if not hasattr(self, "harvest"):
+            self.harvest = Harvest(
+                self.window,
+                self.f0_min,
+                self.f0_max,
+                self.sr,
+            )
+        f0 = self.harvest.compute_f0(x, filter_radius=filter_radius)
+        return self._get_f0_post(f0, f0_up_key)
+    
+    def _get_f0_dio(self, x, f0_up_key, filter_radius):
+        if not hasattr(self, "dio"):
+            self.dio = Dio(
+                self.window,
+                self.f0_min,
+                self.f0_max,
+                self.sr,
+            )
+        f0 = self.dio.compute_f0(x)
+        return self._get_f0_post(f0, f0_up_key)
+
+    def _get_f0_crepe(self, x, f0_up_key, filter_radius):
+        if hasattr(self, "crepe") == False:
+            self.crepe = CRePE(
+                self.window,
+                self.f0_min,
+                self.f0_max,
+                self.sr,
+                self.device,
+            )
+        f0 = self.crepe.compute_f0(x)
+        return self._get_f0_post(f0, f0_up_key)
+
+    def _get_f0_rmvpe(self, x, f0_up_key, filter_radius=0.03):
+        if hasattr(self, "rmvpe") == False:
+            self.rmvpe = RMVPE(
+                "%s/rmvpe.pt" % os.environ["rmvpe_root"],
+                is_half=self.is_half,
+                device=self.device,
+                use_jit=self.use_jit,
+            )
+        return self._get_f0_post(
+            self.rmvpe.compute_f0(x, thred=filter_radius), f0_up_key,
+        )
+
+    def _get_f0_fcpe(self, x, f0_up_key, filter_radius):
+        if hasattr(self, "fcpe") == False:
+            self.fcpe = FCPE(
+                160,
+                self.f0_min,
+                self.f0_max,
+                16000,
+                self.device,
+            )
+        f0 = self.fcpe.compute_f0(x)
+        return self._get_f0_post(f0, f0_up_key)

rvc/f0/harvest.py

@@ -28,5 +28,5 @@ class Harvest(F0Predictor):
         )
         f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
         if filter_radius is not None and filter_radius > 2:
-            f0 = signal.medfilt(f0, 3)
+            f0 = signal.medfilt(f0, filter_radius)
         return self._interpolate_f0(self._resize_f0(f0, p_len))[0]