mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 02:55:05 +08:00
Update rvc_for_realtime.py
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RVC-Boss
GitHub
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commit
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@ -1,269 +1,269 @@
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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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SynthesizerTrnMs256NSFsid_nono,
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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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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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) -> 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.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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self.f0_max = 1100
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self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
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self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
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self.sr = 16000
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self.window = 160
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self.n_cpu = n_cpu
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if index_rate != 0:
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self.index = faiss.read_index(index_path)
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self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
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print("index search enabled")
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self.index_rate = index_rate
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
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["hubert_base.pt"],
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(config.device)
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if config.is_half:
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hubert_model = hubert_model.half()
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else:
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hubert_model = hubert_model.float()
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hubert_model.eval()
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self.model = hubert_model
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cpt = torch.load(pth_path, map_location="cpu")
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self.tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
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self.if_f0 = cpt.get("f0", 1)
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self.version = cpt.get("version", "v1")
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if self.version == "v1":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif self.version == "v2":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs768NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del self.net_g.enc_q
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print(self.net_g.load_state_dict(cpt["weight"], strict=False))
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self.net_g.eval().to(device)
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if config.is_half:
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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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except:
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print(traceback.format_exc())
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def get_f0_post(self, f0):
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f0_min = self.f0_min
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f0_max = self.f0_max
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
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f0_mel_max - f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse, f0bak
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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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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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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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)
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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 *= 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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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=16000,
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f0_ceil=1100,
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f0_floor=50,
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frame_period=10,
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)
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f0 = signal.medfilt(f0, 3)
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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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f0bak = np.zeros(x.shape[0] // 160, dtype=np.float64)
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length = len(x)
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part_length = int(length / n_cpu / 160) * 160
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ts = ttime()
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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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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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res_ts = self.opt_q.get()
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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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f0 = f0[:-3]
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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 = 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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def get_f0_crepe(self, x, f0_up_key):
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audio = torch.tensor(np.copy(x))[None].float()
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f0, pd = torchcrepe.predict(
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audio,
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self.sr,
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160,
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self.f0_min,
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self.f0_max,
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"full",
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batch_size=512,
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device=self.device,
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return_periodicity=True,
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)
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pd = torchcrepe.filter.median(pd, 3)
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f0 = torchcrepe.filter.mean(f0, 3)
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f0[pd < 0.1] = 0
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f0 = f0[0].cpu().numpy()
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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 get_f0_rmvpe(self, x, f0_up_key):
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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("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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feats = feats.view(1, -1)
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if config.is_half:
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feats = feats.half()
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else:
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feats = feats.float()
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feats = feats.to(self.device)
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t1 = ttime()
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with torch.no_grad():
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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inputs = {
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"source": feats,
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"padding_mask": padding_mask,
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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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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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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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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
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if config.is_half:
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npy = npy.astype("float16")
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feats[0][-leng_replace_head:] = (
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torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
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+ (1 - self.index_rate) * feats[0][-leng_replace_head:]
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)
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else:
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print("index search FAIL or disabled")
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except:
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traceback.print_exc()
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print("index search FAIL")
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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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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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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p_len = min(feats.shape[1], 13000)
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t4 = ttime()
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feats = feats[:, :p_len, :]
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if self.if_f0 == 1:
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cache_pitch = cache_pitch[:p_len]
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cache_pitchf = cache_pitchf[:p_len]
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cache_pitch = torch.LongTensor(cache_pitch).unsqueeze(0).to(self.device)
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cache_pitchf = torch.FloatTensor(cache_pitchf).unsqueeze(0).to(self.device)
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p_len = torch.LongTensor([p_len]).to(self.device)
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ii = 0 # sid
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sid = torch.LongTensor([ii]).to(self.device)
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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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)
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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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)
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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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return infered_audio
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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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SynthesizerTrnMs256NSFsid_nono,
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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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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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) -> 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.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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self.f0_max = 1100
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self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
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self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
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self.sr = 16000
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self.window = 160
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self.n_cpu = n_cpu
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if index_rate != 0:
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self.index = faiss.read_index(index_path)
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self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
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print("index search enabled")
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self.index_rate = index_rate
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
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["hubert_base.pt"],
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(config.device)
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if config.is_half:
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hubert_model = hubert_model.half()
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else:
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hubert_model = hubert_model.float()
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hubert_model.eval()
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self.model = hubert_model
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cpt = torch.load(pth_path, map_location="cpu")
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self.tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
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self.if_f0 = cpt.get("f0", 1)
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self.version = cpt.get("version", "v1")
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if self.version == "v1":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif self.version == "v2":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs768NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del self.net_g.enc_q
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print(self.net_g.load_state_dict(cpt["weight"], strict=False))
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self.net_g.eval().to(device)
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if config.is_half:
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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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except:
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print(traceback.format_exc())
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def get_f0_post(self, f0):
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f0_min = self.f0_min
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f0_max = self.f0_max
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
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f0_mel_max - f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse, f0bak
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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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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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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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)
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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")
|
||||
|
||||
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, dtype=np.float64)
|
||||
length = len(x)
|
||||
part_length = int(length / n_cpu / 160) * 160
|
||||
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:-1]
|
||||
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):
|
||||
audio = torch.tensor(np.copy(x))[None].float()
|
||||
f0, pd = torchcrepe.predict(
|
||||
audio,
|
||||
self.sr,
|
||||
160,
|
||||
self.f0_min,
|
||||
self.f0_max,
|
||||
"full",
|
||||
batch_size=512,
|
||||
device=self.device,
|
||||
return_periodicity=True,
|
||||
)
|
||||
pd = torchcrepe.filter.median(pd, 3)
|
||||
f0 = torchcrepe.filter.mean(f0, 3)
|
||||
f0[pd < 0.1] = 0
|
||||
f0 = f0[0].cpu().numpy()
|
||||
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 rmvpe import RMVPE
|
||||
print("loading rmvpe model")
|
||||
self.model_rmvpe = RMVPE("rmvpe.pt", is_half=self.is_half, device=self.device)
|
||||
# self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device)
|
||||
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
|
||||
def infer(self, feats: torch.Tensor, indata: np.ndarray, rate1, rate2, cache_pitch, cache_pitchf, f0method) -> np.ndarray:
|
||||
feats = feats.view(1, -1)
|
||||
if config.is_half:
|
||||
feats = feats.half()
|
||||
else:
|
||||
feats = feats.float()
|
||||
feats = feats.to(self.device)
|
||||
t1 = ttime()
|
||||
with torch.no_grad():
|
||||
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)
|
||||
feats = self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
|
||||
t2 = ttime()
|
||||
try:
|
||||
if (
|
||||
hasattr(self, "index")
|
||||
and self.index_rate != 0
|
||||
):
|
||||
leng_replace_head = int(rate1 * feats[0].shape[0])
|
||||
npy = feats[0][-leng_replace_head:].cpu().numpy().astype("float32")
|
||||
score, ix = self.index.search(npy, k=8)
|
||||
weight = np.square(1 / score)
|
||||
weight /= weight.sum(axis=1, keepdims=True)
|
||||
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
|
||||
if config.is_half:
|
||||
npy = npy.astype("float16")
|
||||
feats[0][-leng_replace_head:] = (
|
||||
torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
|
||||
+ (1 - self.index_rate) * feats[0][-leng_replace_head:]
|
||||
)
|
||||
else:
|
||||
print("index search FAIL or disabled")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
print("index search FAIL")
|
||||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
t3 = ttime()
|
||||
if self.if_f0 == 1:
|
||||
pitch, pitchf = self.get_f0(indata, self.f0_up_key, self.n_cpu, f0method)
|
||||
cache_pitch[:] = np.append(cache_pitch[pitch[:-1].shape[0]:], pitch[:-1])
|
||||
cache_pitchf[:] = np.append(cache_pitchf[pitchf[:-1].shape[0]:], pitchf[:-1])
|
||||
p_len = min(feats.shape[1], 13000, cache_pitch.shape[0])
|
||||
else:
|
||||
cache_pitch, cache_pitchf = None, None
|
||||
p_len = min(feats.shape[1], 13000)
|
||||
t4 = ttime()
|
||||
feats = feats[:, :p_len, :]
|
||||
if self.if_f0 == 1:
|
||||
cache_pitch = cache_pitch[:p_len]
|
||||
cache_pitchf = cache_pitchf[:p_len]
|
||||
cache_pitch = torch.LongTensor(cache_pitch).unsqueeze(0).to(self.device)
|
||||
cache_pitchf = torch.FloatTensor(cache_pitchf).unsqueeze(0).to(self.device)
|
||||
p_len = torch.LongTensor([p_len]).to(self.device)
|
||||
ii = 0 # sid
|
||||
sid = torch.LongTensor([ii]).to(self.device)
|
||||
with torch.no_grad():
|
||||
if self.if_f0 == 1:
|
||||
infered_audio = (
|
||||
self.net_g.infer(feats, p_len, cache_pitch, cache_pitchf, sid, rate2)[0][0, 0]
|
||||
.data.cpu()
|
||||
.float()
|
||||
)
|
||||
else:
|
||||
infered_audio = (
|
||||
self.net_g.infer(feats, p_len, sid, rate2)[0][0, 0].data.cpu().float()
|
||||
)
|
||||
t5 = ttime()
|
||||
print("time->fea-index-f0-model:", t2 - t1, t3 - t2, t4 - t3, t5 - t4)
|
||||
return infered_audio
|
||||
|
||||
Loading…
Reference in New Issue
Block a user