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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
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Format code (#727)
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@@ -1,4 +1,4 @@
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import faiss,torch,traceback,parselmouth,numpy as np,torchcrepe,torch.nn as nn,pyworld
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import faiss, torch, traceback, parselmouth, numpy as np, torchcrepe, torch.nn as nn, pyworld
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from fairseq import checkpoint_utils
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from lib.infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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@@ -6,29 +6,32 @@ from lib.infer_pack.models import (
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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import os,sys
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import os, sys
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from time import time as ttime
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import torch.nn.functional as F
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import scipy.signal as signal
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from config import Config
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from multiprocessing import Manager as M
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mm = M()
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config = Config()
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class RVC:
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def __init__(
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self, key, pth_path, index_path, index_rate, n_cpu,inp_q,opt_q,device
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self, key, pth_path, index_path, index_rate, n_cpu, inp_q, opt_q, device
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) -> None:
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"""
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初始化
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"""
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try:
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global config
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self.inp_q=inp_q
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self.opt_q=opt_q
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self.device=device
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self.inp_q = inp_q
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self.opt_q = opt_q
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self.device = device
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self.f0_up_key = key
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self.time_step = 160 / 16000 * 1000
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self.f0_min = 50
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@@ -81,7 +84,7 @@ class RVC:
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self.net_g = self.net_g.half()
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else:
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self.net_g = self.net_g.float()
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self.is_half=config.is_half
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self.is_half = config.is_half
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except:
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print(traceback.format_exc())
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@@ -102,29 +105,33 @@ class RVC:
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def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
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n_cpu = int(n_cpu)
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if (method == "crepe"): return self.get_f0_crepe(x, f0_up_key)
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if (method == "rmvpe"): return self.get_f0_rmvpe(x, f0_up_key)
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if (method == "pm"):
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if method == "crepe":
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return self.get_f0_crepe(x, f0_up_key)
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if method == "rmvpe":
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return self.get_f0_rmvpe(x, f0_up_key)
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if method == "pm":
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p_len = x.shape[0] // 160
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f0 = (
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parselmouth.Sound(x, 16000)
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.to_pitch_ac(
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.to_pitch_ac(
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time_step=0.01,
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voicing_threshold=0.6,
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pitch_floor=50,
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pitch_ceiling=1100,
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)
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.selected_array["frequency"]
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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print(pad_size, p_len - len(f0) - pad_size)
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f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
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f0 = np.pad(
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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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f0 *= pow(2, f0_up_key / 12)
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return self.get_f0_post(f0)
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if (n_cpu == 1):
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if n_cpu == 1:
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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=16000,
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@@ -142,23 +149,27 @@ class RVC:
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res_f0 = mm.dict()
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for idx in range(n_cpu):
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tail = part_length * (idx + 1) + 320
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if (idx == 0):
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if idx == 0:
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self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
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else:
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self.inp_q.put((idx, x[part_length * idx - 320:tail], res_f0, n_cpu, ts))
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while (1):
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self.inp_q.put(
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(idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
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)
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while 1:
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res_ts = self.opt_q.get()
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if (res_ts == ts):
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if res_ts == ts:
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break
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f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
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for idx, f0 in enumerate(f0s):
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if (idx == 0):
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if idx == 0:
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f0 = f0[:-3]
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elif (idx != n_cpu - 1):
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elif idx != n_cpu - 1:
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f0 = f0[2:-3]
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else:
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f0 = f0[2:-1]
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f0bak[part_length * idx // 160:part_length * idx // 160 + f0.shape[0]] = f0
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f0bak[
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part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]
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] = f0
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f0bak = signal.medfilt(f0bak, 3)
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f0bak *= pow(2, f0_up_key / 12)
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return self.get_f0_post(f0bak)
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@@ -184,16 +195,28 @@ class RVC:
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return self.get_f0_post(f0)
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def get_f0_rmvpe(self, x, f0_up_key):
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if (hasattr(self, "model_rmvpe") == False):
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if hasattr(self, "model_rmvpe") == False:
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from rmvpe import RMVPE
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print("loading rmvpe model")
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self.model_rmvpe = RMVPE("rmvpe.pt", is_half=self.is_half, device=self.device)
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self.model_rmvpe = RMVPE(
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"rmvpe.pt", is_half=self.is_half, device=self.device
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)
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# self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device)
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f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
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f0 *= pow(2, f0_up_key / 12)
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return self.get_f0_post(f0)
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def infer(self, feats: torch.Tensor, indata: np.ndarray, rate1, rate2, cache_pitch, cache_pitchf, f0method) -> np.ndarray:
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def infer(
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self,
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feats: torch.Tensor,
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indata: np.ndarray,
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rate1,
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rate2,
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cache_pitch,
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cache_pitchf,
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f0method,
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) -> np.ndarray:
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feats = feats.view(1, -1)
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if config.is_half:
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feats = feats.half()
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@@ -209,13 +232,12 @@ class RVC:
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"output_layer": 9 if self.version == "v1" else 12,
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}
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logits = self.model.extract_features(**inputs)
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feats = self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
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feats = (
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self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
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)
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t2 = ttime()
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try:
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if (
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hasattr(self, "index")
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and self.index_rate != 0
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):
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if hasattr(self, "index") and self.index_rate != 0:
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leng_replace_head = int(rate1 * feats[0].shape[0])
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npy = feats[0][-leng_replace_head:].cpu().numpy().astype("float32")
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score, ix = self.index.search(npy, k=8)
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@@ -237,8 +259,10 @@ class RVC:
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t3 = ttime()
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if self.if_f0 == 1:
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pitch, pitchf = self.get_f0(indata, self.f0_up_key, self.n_cpu, f0method)
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cache_pitch[:] = np.append(cache_pitch[pitch[:-1].shape[0]:], pitch[:-1])
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cache_pitchf[:] = np.append(cache_pitchf[pitchf[:-1].shape[0]:], pitchf[:-1])
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cache_pitch[:] = np.append(cache_pitch[pitch[:-1].shape[0] :], pitch[:-1])
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cache_pitchf[:] = np.append(
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cache_pitchf[pitchf[:-1].shape[0] :], pitchf[:-1]
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)
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p_len = min(feats.shape[1], 13000, cache_pitch.shape[0])
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else:
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cache_pitch, cache_pitchf = None, None
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@@ -256,13 +280,17 @@ class RVC:
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with torch.no_grad():
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if self.if_f0 == 1:
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infered_audio = (
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self.net_g.infer(feats, p_len, cache_pitch, cache_pitchf, sid, rate2)[0][0, 0]
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.data.cpu()
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.float()
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self.net_g.infer(
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feats, p_len, cache_pitch, cache_pitchf, sid, rate2
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)[0][0, 0]
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.data.cpu()
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.float()
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)
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else:
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infered_audio = (
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self.net_g.infer(feats, p_len, sid, rate2)[0][0, 0].data.cpu().float()
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self.net_g.infer(feats, p_len, sid, rate2)[0][0, 0]
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.data.cpu()
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.float()
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)
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t5 = ttime()
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print("time->fea-index-f0-model:", t2 - t1, t3 - t2, t4 - t3, t5 - t4)
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