optimize(f0): move some f0s into rvc.f0

infer/modules/train/extract/extract_f0_print.py

@@ -89,7 +89,7 @@ class FeatureInput(object):
                 self.model_rmvpe = RMVPE(
                     "assets/rmvpe/rmvpe.pt", is_half=False, device="cpu"
                 )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+            f0 = self.model_rmvpe.infer_from_audio(x, threshold=0.03)
         return f0
 
     def coarse_f0(self, f0):

infer/modules/train/extract/extract_f0_rmvpe.py

@@ -52,7 +52,7 @@ class FeatureInput(object):
                 self.model_rmvpe = RMVPE(
                     "assets/rmvpe/rmvpe.pt", is_half=is_half, device="cuda"
                 )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+            f0 = self.model_rmvpe.infer_from_audio(x, threshold=0.03)
         return f0
 
     def coarse_f0(self, f0):

infer/modules/train/extract/extract_f0_rmvpe_dml.py

@@ -50,7 +50,7 @@ class FeatureInput(object):
                 self.model_rmvpe = RMVPE(
                     "assets/rmvpe/rmvpe.pt", is_half=False, device=device
                 )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+            f0 = self.model_rmvpe.infer_from_audio(x, threshold=0.03)
         return f0
 
     def coarse_f0(self, f0):

infer/modules/train/train.py

@@ -47,7 +47,7 @@ from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
 
-from rvc import utils
+from rvc.layers import utils
 from infer.lib.train.data_utils import (
     DistributedBucketSampler,
     TextAudioCollate,

infer/modules/vc/pipeline.py

@@ -5,40 +5,24 @@ import logging
 
 logger = logging.getLogger(__name__)
 
-from functools import lru_cache
 from time import time
 
 import faiss
 import librosa
 import numpy as np
-import parselmouth
 import pyworld
 import torch
 import torch.nn.functional as F
 import torchcrepe
 from scipy import signal
 
+from rvc.f0 import PM, Harvest
+
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 
 bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
 
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(f0_cache_key, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[f0_cache_key]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
 
 def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
     # print(data1.max(),data2.max())
@@ -90,37 +74,18 @@ class Pipeline(object):
         filter_radius,
         inp_f0=None,
     ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
         f0_min = 50
         f0_max = 1100
         f0_mel_min = 1127 * np.log(1 + f0_min / 700)
         f0_mel_max = 1127 * np.log(1 + f0_max / 700)
         if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
+            if not hasattr(self, "pm"):
+                self.pm = PM(self.window, f0_min, f0_max, self.sr)
+            f0 = self.pm.compute_f0(x, p_len=p_len)
         elif f0_method == "harvest":
-            from hashlib import md5
-
-            f0_cache_key = md5(x.tobytes()).digest()
-            input_audio_path2wav[f0_cache_key] = x.astype(np.double)
-            f0 = cache_harvest_f0(f0_cache_key, self.sr, f0_max, f0_min, 10)
-            del input_audio_path2wav[f0_cache_key]
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
+            if not hasattr(self, "harvest"):
+                self.harvest = Harvest(self.window, f0_min, f0_max, self.sr)
+            f0 = self.harvest.compute_f0(x, p_len=p_len, filter_radius=filter_radius)
         elif f0_method == "crepe":
             model = "full"
             # Pick a batch size that doesn't cause memory errors on your gpu
@@ -155,7 +120,7 @@ class Pipeline(object):
                     device=self.device,
                     # use_jit=self.config.use_jit,
                 )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+            f0 = self.model_rmvpe.infer_from_audio(x, threshold=0.03)
 
             if "privateuseone" in str(self.device):  # clean ortruntime memory
                 del self.model_rmvpe.model