| from io import BytesIO
|
| import os
|
| import pickle
|
| import sys
|
| import traceback
|
| from infer.lib import jit
|
| from infer.lib.jit.get_synthesizer import get_synthesizer
|
| from time import time as ttime
|
| import fairseq
|
| import faiss
|
| import numpy as np
|
| import parselmouth
|
| import pyworld
|
| import scipy.signal as signal
|
| import torch
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
| import torchcrepe
|
|
|
| from infer.lib.infer_pack.models import (
|
| SynthesizerTrnMs256NSFsid,
|
| SynthesizerTrnMs256NSFsid_nono,
|
| SynthesizerTrnMs768NSFsid,
|
| SynthesizerTrnMs768NSFsid_nono,
|
| )
|
|
|
| now_dir = os.getcwd()
|
| sys.path.append(now_dir)
|
| from multiprocessing import Manager as M
|
|
|
| from configs.config import Config
|
|
|
|
|
|
|
| mm = M()
|
|
|
|
|
| def printt(strr, *args):
|
| if len(args) == 0:
|
| print(strr)
|
| else:
|
| print(strr % args)
|
|
|
|
|
|
|
|
|
| class RVC:
|
| def __init__(
|
| self,
|
| key,
|
| pth_path,
|
| index_path,
|
| index_rate,
|
| n_cpu,
|
| inp_q,
|
| opt_q,
|
| config: Config,
|
| last_rvc=None,
|
| ) -> None:
|
| """
|
| 初始化
|
| """
|
| try:
|
| if config.dml == True:
|
|
|
| def forward_dml(ctx, x, scale):
|
| ctx.scale = scale
|
| res = x.clone().detach()
|
| return res
|
|
|
| fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
|
|
|
| self.config = config
|
| self.inp_q = inp_q
|
| self.opt_q = opt_q
|
|
|
| self.device = config.device
|
| self.f0_up_key = key
|
| self.f0_min = 50
|
| self.f0_max = 1100
|
| self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
|
| self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
|
| self.n_cpu = n_cpu
|
| self.use_jit = self.config.use_jit
|
| self.is_half = config.is_half
|
|
|
| if index_rate != 0:
|
| self.index = faiss.read_index(index_path)
|
| self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
|
| printt("Index search enabled")
|
| self.pth_path: str = pth_path
|
| self.index_path = index_path
|
| self.index_rate = index_rate
|
| self.cache_pitch: torch.Tensor = torch.zeros(
|
| 1024, device=self.device, dtype=torch.long
|
| )
|
| self.cache_pitchf = torch.zeros(
|
| 1024, device=self.device, dtype=torch.float32
|
| )
|
|
|
| if last_rvc is None:
|
| models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
|
| ["assets/hubert/hubert_base.pt"],
|
| suffix="",
|
| )
|
| hubert_model = models[0]
|
| hubert_model = hubert_model.to(self.device)
|
| if self.is_half:
|
| hubert_model = hubert_model.half()
|
| else:
|
| hubert_model = hubert_model.float()
|
| hubert_model.eval()
|
| self.model = hubert_model
|
| else:
|
| self.model = last_rvc.model
|
|
|
| self.net_g: nn.Module = None
|
|
|
| def set_default_model():
|
| self.net_g, cpt = get_synthesizer(self.pth_path, self.device)
|
| self.tgt_sr = cpt["config"][-1]
|
| cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
|
| self.if_f0 = cpt.get("f0", 1)
|
| self.version = cpt.get("version", "v1")
|
| if self.is_half:
|
| self.net_g = self.net_g.half()
|
| else:
|
| self.net_g = self.net_g.float()
|
|
|
| def set_jit_model():
|
| jit_pth_path = self.pth_path.rstrip(".pth")
|
| jit_pth_path += ".half.jit" if self.is_half else ".jit"
|
| reload = False
|
| if str(self.device) == "cuda":
|
| self.device = torch.device("cuda:0")
|
| if os.path.exists(jit_pth_path):
|
| cpt = jit.load(jit_pth_path)
|
| model_device = cpt["device"]
|
| if model_device != str(self.device):
|
| reload = True
|
| else:
|
| reload = True
|
|
|
| if reload:
|
| cpt = jit.synthesizer_jit_export(
|
| self.pth_path,
|
| "script",
|
| None,
|
| device=self.device,
|
| is_half=self.is_half,
|
| )
|
|
|
| self.tgt_sr = cpt["config"][-1]
|
| self.if_f0 = cpt.get("f0", 1)
|
| self.version = cpt.get("version", "v1")
|
| self.net_g = torch.jit.load(
|
| BytesIO(cpt["model"]), map_location=self.device
|
| )
|
| self.net_g.infer = self.net_g.forward
|
| self.net_g.eval().to(self.device)
|
|
|
| def set_synthesizer():
|
| if self.use_jit and not config.dml:
|
| if self.is_half and "cpu" in str(self.device):
|
| printt(
|
| "Use default Synthesizer model. \
|
| Jit is not supported on the CPU for half floating point"
|
| )
|
| set_default_model()
|
| else:
|
| set_jit_model()
|
| else:
|
| set_default_model()
|
|
|
| if last_rvc is None or last_rvc.pth_path != self.pth_path:
|
| set_synthesizer()
|
| else:
|
| self.tgt_sr = last_rvc.tgt_sr
|
| self.if_f0 = last_rvc.if_f0
|
| self.version = last_rvc.version
|
| self.is_half = last_rvc.is_half
|
| if last_rvc.use_jit != self.use_jit:
|
| set_synthesizer()
|
| else:
|
| self.net_g = last_rvc.net_g
|
|
|
| if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
|
| self.model_rmvpe = last_rvc.model_rmvpe
|
| if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
|
| self.device_fcpe = last_rvc.device_fcpe
|
| self.model_fcpe = last_rvc.model_fcpe
|
| except:
|
| printt(traceback.format_exc())
|
|
|
| def change_key(self, new_key):
|
| self.f0_up_key = new_key
|
|
|
| def change_index_rate(self, new_index_rate):
|
| if new_index_rate != 0 and self.index_rate == 0:
|
| self.index = faiss.read_index(self.index_path)
|
| self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
|
| printt("Index search enabled")
|
| self.index_rate = new_index_rate
|
|
|
| def get_f0_post(self, f0):
|
| if not torch.is_tensor(f0):
|
| f0 = torch.from_numpy(f0)
|
| f0 = f0.float().to(self.device).squeeze()
|
| f0_mel = 1127 * torch.log(1 + f0 / 700)
|
| f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
|
| self.f0_mel_max - self.f0_mel_min
|
| ) + 1
|
| f0_mel[f0_mel <= 1] = 1
|
| f0_mel[f0_mel > 255] = 255
|
| f0_coarse = torch.round(f0_mel).long()
|
| return f0_coarse, f0
|
|
|
| def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
|
| n_cpu = int(n_cpu)
|
| if method == "crepe":
|
| return self.get_f0_crepe(x, f0_up_key)
|
| if method == "rmvpe":
|
| return self.get_f0_rmvpe(x, f0_up_key)
|
| if method == "fcpe":
|
| return self.get_f0_fcpe(x, f0_up_key)
|
| x = x.cpu().numpy()
|
| if method == "pm":
|
| p_len = x.shape[0] // 160 + 1
|
| f0_min = 65
|
| l_pad = int(np.ceil(1.5 / f0_min * 16000))
|
| r_pad = l_pad + 1
|
| s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), 16000).to_pitch_ac(
|
| time_step=0.01,
|
| voicing_threshold=0.6,
|
| pitch_floor=f0_min,
|
| pitch_ceiling=1100,
|
| )
|
| assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
|
| f0 = s.selected_array["frequency"]
|
| if len(f0) < p_len:
|
| f0 = np.pad(f0, (0, p_len - len(f0)))
|
| f0 = f0[:p_len]
|
| 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 + 1, dtype=np.float64)
|
| length = len(x)
|
| part_length = 160 * ((length // 160 - 1) // n_cpu + 1)
|
| n_cpu = (length // 160 - 1) // (part_length // 160) + 1
|
| 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:]
|
| 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):
|
| if "privateuseone" in str(
|
| self.device
|
| ):
|
| return self.get_f0(x, f0_up_key, 1, "fcpe")
|
|
|
| f0, pd = torchcrepe.predict(
|
| x.unsqueeze(0).float(),
|
| 16000,
|
| 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 *= 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 infer.lib.rmvpe import RMVPE
|
|
|
| printt("Loading rmvpe model")
|
| self.model_rmvpe = RMVPE(
|
| "assets/rmvpe/rmvpe.pt",
|
| is_half=self.is_half,
|
| device=self.device,
|
| use_jit=self.config.use_jit,
|
| )
|
| 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 get_f0_fcpe(self, x, f0_up_key):
|
| if hasattr(self, "model_fcpe") == False:
|
| from torchfcpe import spawn_bundled_infer_model
|
|
|
| printt("Loading fcpe model")
|
| if "privateuseone" in str(self.device):
|
| self.device_fcpe = "cpu"
|
| else:
|
| self.device_fcpe = self.device
|
| self.model_fcpe = spawn_bundled_infer_model(self.device_fcpe)
|
| f0 = self.model_fcpe.infer(
|
| x.to(self.device_fcpe).unsqueeze(0).float(),
|
| sr=16000,
|
| decoder_mode="local_argmax",
|
| threshold=0.006,
|
| )
|
| f0 *= pow(2, f0_up_key / 12)
|
| return self.get_f0_post(f0)
|
|
|
| def infer(
|
| self,
|
| input_wav: torch.Tensor,
|
| block_frame_16k,
|
| skip_head,
|
| return_length,
|
| f0method,
|
| ) -> np.ndarray:
|
| t1 = ttime()
|
| with torch.no_grad():
|
| if self.config.is_half:
|
| feats = input_wav.half().view(1, -1)
|
| else:
|
| feats = input_wav.float().view(1, -1)
|
| 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]
|
| )
|
| feats = torch.cat((feats, feats[:, -1:, :]), 1)
|
| t2 = ttime()
|
| try:
|
| if hasattr(self, "index") and self.index_rate != 0:
|
| npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
|
| score, ix = self.index.search(npy, k=8)
|
| if (ix >= 0).all():
|
| 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 self.config.is_half:
|
| npy = npy.astype("float16")
|
| feats[0][skip_head // 2 :] = (
|
| torch.from_numpy(npy).unsqueeze(0).to(self.device)
|
| * self.index_rate
|
| + (1 - self.index_rate) * feats[0][skip_head // 2 :]
|
| )
|
| else:
|
| printt(
|
| "Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!"
|
| )
|
| else:
|
| printt("Index search FAILED or disabled")
|
| except:
|
| traceback.print_exc()
|
| printt("Index search FAILED")
|
| t3 = ttime()
|
| p_len = input_wav.shape[0] // 160
|
| if self.if_f0 == 1:
|
| f0_extractor_frame = block_frame_16k + 800
|
| if f0method == "rmvpe":
|
| f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
|
| pitch, pitchf = self.get_f0(
|
| input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
|
| )
|
| shift = block_frame_16k // 160
|
| self.cache_pitch[:-shift] = self.cache_pitch[shift:].clone()
|
| self.cache_pitchf[:-shift] = self.cache_pitchf[shift:].clone()
|
| self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
|
| self.cache_pitchf[4 - pitch.shape[0] :] = pitchf[3:-1]
|
| cache_pitch = self.cache_pitch[None, -p_len:]
|
| cache_pitchf = self.cache_pitchf[None, -p_len:]
|
| t4 = ttime()
|
| feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
| feats = feats[:, :p_len, :]
|
| p_len = torch.LongTensor([p_len]).to(self.device)
|
| sid = torch.LongTensor([0]).to(self.device)
|
| skip_head = torch.LongTensor([skip_head])
|
| return_length = torch.LongTensor([return_length])
|
| with torch.no_grad():
|
| if self.if_f0 == 1:
|
| infered_audio, _, _ = self.net_g.infer(
|
| feats,
|
| p_len,
|
| cache_pitch,
|
| cache_pitchf,
|
| sid,
|
| skip_head,
|
| return_length,
|
| )
|
| else:
|
| infered_audio, _, _ = self.net_g.infer(
|
| feats, p_len, sid, skip_head, return_length
|
| )
|
| t5 = ttime()
|
| printt(
|
| "Spent time: fea = %.3fs, index = %.3fs, f0 = %.3fs, model = %.3fs",
|
| t2 - t1,
|
| t3 - t2,
|
| t4 - t3,
|
| t5 - t4,
|
| )
|
| return infered_audio.squeeze().float()
|
|
|
