313 lines
12 KiB
Python
313 lines
12 KiB
Python
import os, sys
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import faiss, torch, traceback, parselmouth, numpy as np, torchcrepe, torch.nn as nn, pyworld
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import fairseq
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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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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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if(config.dml==True):
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def forward_dml(ctx, x, scale):
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ctx.scale = scale
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res = x.clone().detach()
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return res
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fairseq.modules.grad_multiply.GradMultiply.forward=forward_dml
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# config.device=torch.device("cpu")########强制cpu测试
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# config.is_half=False########强制cpu测试
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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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# device="cpu"########强制cpu测试
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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, _, _ = fairseq.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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# print(2333333333,device,config.device,self.device)#net_g是device，hubert是config.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.int32)
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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":
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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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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(
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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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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(
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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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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[
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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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def get_f0_crepe(self, x, f0_up_key):
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if self.device.type == "privateuseone":###不支持dml，cpu又太慢用不成，拿pm顶替
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return self.get_f0(x, f0_up_key,1,"pm")
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audio = torch.tensor(np.copy(x))[None].float()
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# print("using crepe,device:%s"%self.device)
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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 if self.device.type!="privateuseone" else "cpu",###crepe不用半精度全部是全精度所以不愁###cpu延迟高到没法用
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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 lib.rmvpe import RMVPE
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print("loading rmvpe model")
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self.model_rmvpe = RMVPE(
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# "rmvpe.pt", is_half=self.is_half if self.device.type!="privateuseone" else False, device=self.device if self.device.type!="privateuseone"else "cpu"####dml时强制对rmvpe用cpu跑
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# "rmvpe.pt", is_half=False, device=self.device####dml配置
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# "rmvpe.pt", is_half=False, device="cpu"####锁定cpu配置
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"rmvpe.pt", is_half=self.is_half, device=self.device####正常逻辑
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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(
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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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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 = (
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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 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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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(
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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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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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# print(12222222222,feats.device,p_len.device,cache_pitch.device,cache_pitchf.device,sid.device,rate2)
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infered_audio = (
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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]
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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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return infered_audio
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