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

infer/lib/jit/rmvpe.py

@@ -2,7 +2,7 @@ import torch
 
 
 def get_rmvpe(model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu")):
-    from infer.lib.rmvpe import E2E
+    from rvc.f0.e2e import E2E
 
     model = E2E(4, 1, (2, 2))
     ckpt = torch.load(model_path, map_location=device)

infer/lib/rmvpe.py

@@ -6,17 +6,6 @@ import torch
 
 from infer.lib import jit
 
-try:
-    # Fix "Torch not compiled with CUDA enabled"
-    import intel_extension_for_pytorch as ipex  # pylint: disable=import-error, unused-import
-
-    if torch.xpu.is_available():
-        from infer.modules.ipex import ipex_init
-
-        ipex_init()
-except Exception:  # pylint: disable=broad-exception-caught
-    pass
-import torch.nn as nn
 import torch.nn.functional as F
 
 import logging
@@ -127,13 +116,13 @@ class RMVPE:
             return hidden[:, :n_frames]
 
     def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
+        cents_pred = self.to_local_average_cents(hidden, threshold=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):
+    def infer_from_audio(self, audio, threshold=0.03):
         # torch.cuda.synchronize()
         # t0 = ttime()
         if not torch.is_tensor(audio):
@@ -155,17 +144,15 @@ class RMVPE:
         if self.is_half == True:
             hidden = hidden.astype("float32")
 
-        f0 = self.decode(hidden, thred=thred)
+        f0 = self.decode(hidden, thred=threshold)
         # 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 to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
+    def to_local_average_cents(self, salience, threshold=0.05):
         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 = []
@@ -174,15 +161,11 @@ class RMVPE:
         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]])
-        # 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))
+        devided[maxx <= threshold] = 0
         return devided

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