mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-06 01:30:24 +08:00
optimize(rtrvc): impl. rvc f0s
This commit is contained in:
源文雨
@@ -1,353 +1,178 @@
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from io import BytesIO
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import os
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import sys
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import traceback
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from infer.lib import jit
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from time import time as ttime
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from typing import Union, Literal, Optional
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import fairseq
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import faiss
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import numpy as np
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import parselmouth
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import pyworld
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import scipy.signal as signal
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torchaudio.transforms import Resample
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from rvc.f0 import PM, Harvest, RMVPE, CRePE, Dio, FCPE
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from rvc.synthesizer import load_synthesizer
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from multiprocessing import Manager as M
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from configs import Config
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# config = Config()
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mm = M()
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def printt(strr, *args):
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if len(args) == 0:
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print(strr)
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else:
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print(strr % args)
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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,
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key,
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formant,
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pth_path,
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index_path,
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index_rate,
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n_cpu,
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inp_q,
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opt_q,
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config: Config,
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last_rvc=None,
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key: Union[int, float],
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formant: Union[int, float],
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pth_path: torch.serialization.FILE_LIKE,
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index_path: str,
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index_rate: Union[int, float],
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n_cpu: int = os.cpu_count(),
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device: str = "cpu",
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use_jit: bool = False,
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is_half: bool = False,
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is_dml: bool = False,
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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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if config.dml == True:
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if is_dml:
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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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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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# global config
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self.config = 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 = config.device
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self.f0_up_key = key
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self.formant_shift = formant
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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.n_cpu = n_cpu
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self.use_jit = self.config.use_jit
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self.is_half = config.is_half
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fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
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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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printt("Index search enabled")
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self.pth_path: str = pth_path
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self.index_path = index_path
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self.index_rate = index_rate
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self.cache_pitch: torch.Tensor = torch.zeros(
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1024, device=self.device, dtype=torch.long
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)
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self.cache_pitchf = torch.zeros(
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1024, device=self.device, dtype=torch.float32
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)
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self.device = device
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self.f0_up_key = key
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self.formant_shift = formant
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self.sr = 16000 # hubert sampling rate
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self.window = 160 # hop length
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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.n_cpu = n_cpu
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self.use_jit = use_jit
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self.is_half = is_half
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self.resample_kernel = {}
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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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if last_rvc is None:
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models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
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["assets/hubert/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(self.device)
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if self.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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self.pth_path = pth_path
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self.index_path = index_path
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self.index_rate = index_rate
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self.cache_pitch: torch.Tensor = torch.zeros(
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1024, device=self.device, dtype=torch.long
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)
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self.cache_pitchf = torch.zeros(
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1024, device=self.device, dtype=torch.float32
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)
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self.resample_kernel = {}
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self.f0_methods = {
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"crepe": self._get_f0_crepe,
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"rmvpe": self._get_f0_rmvpe,
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"fcpe": self._get_f0_fcpe,
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"pm": self._get_f0_pm,
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"harvest": self._get_f0_harvest,
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"dio": self._get_f0_dio,
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}
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models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
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["assets/hubert/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(self.device)
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if self.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.hubert = hubert_model
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self.net_g: Optional[nn.Module] = None
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def set_default_model():
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self.net_g, cpt = load_synthesizer(self.pth_path, self.device)
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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.is_half:
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self.net_g = self.net_g.half()
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else:
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self.model = last_rvc.model
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self.net_g = self.net_g.float()
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self.net_g: nn.Module = None
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def set_jit_model():
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from rvc.jit import get_jit_model
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from rvc.synthesizer import synthesizer_jit_export
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def set_default_model():
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self.net_g, cpt = load_synthesizer(self.pth_path, self.device)
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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.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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cpt = get_jit_model(self.pth_path, self.is_half, synthesizer_jit_export)
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def set_jit_model():
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from rvc.jit import get_jit_model
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from rvc.synthesizer import synthesizer_jit_export
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self.tgt_sr = cpt["config"][-1]
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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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self.net_g = torch.jit.load(
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BytesIO(cpt["model"]), map_location=self.device
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)
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self.net_g.infer = self.net_g.forward
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self.net_g.eval().to(self.device)
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cpt = get_jit_model(self.pth_path, self.is_half, synthesizer_jit_export)
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if self.use_jit and not is_dml and not (self.is_half and "cpu" in str(self.device)):
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set_jit_model()
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else:
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set_default_model()
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self.tgt_sr = cpt["config"][-1]
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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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self.net_g = torch.jit.load(
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BytesIO(cpt["model"]), map_location=self.device
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)
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self.net_g.infer = self.net_g.forward
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self.net_g.eval().to(self.device)
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def set_synthesizer():
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if self.use_jit and not config.dml:
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if self.is_half and "cpu" in str(self.device):
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printt(
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"Use default Synthesizer model. \
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Jit is not supported on the CPU for half floating point"
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)
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set_default_model()
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else:
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set_jit_model()
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else:
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set_default_model()
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if last_rvc is None or last_rvc.pth_path != self.pth_path:
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set_synthesizer()
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else:
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self.tgt_sr = last_rvc.tgt_sr
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self.if_f0 = last_rvc.if_f0
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self.version = last_rvc.version
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self.is_half = last_rvc.is_half
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if last_rvc.use_jit != self.use_jit:
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set_synthesizer()
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else:
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self.net_g = last_rvc.net_g
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if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
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self.model_rmvpe = last_rvc.model_rmvpe
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if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
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self.device_fcpe = last_rvc.device_fcpe
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self.model_fcpe = last_rvc.model_fcpe
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except:
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printt(traceback.format_exc())
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def change_key(self, new_key):
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def set_key(self, new_key):
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self.f0_up_key = new_key
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def change_formant(self, new_formant):
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def set_formant(self, new_formant):
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self.formant_shift = new_formant
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def change_index_rate(self, new_index_rate):
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if new_index_rate != 0 and self.index_rate == 0:
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def set_index_rate(self, new_index_rate):
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if new_index_rate > 0 and self.index_rate <= 0:
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self.index = faiss.read_index(self.index_path)
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self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
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printt("Index search enabled")
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self.index_rate = new_index_rate
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def get_f0_post(self, f0):
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if not torch.is_tensor(f0):
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f0 = torch.from_numpy(f0)
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f0 = f0.float().to(self.device).squeeze()
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f0_mel = 1127 * torch.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
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self.f0_mel_max - self.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 = torch.round(f0_mel).long()
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return f0_coarse, f0
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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 == "fcpe":
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return self.get_f0_fcpe(x, f0_up_key)
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x = x.cpu().numpy()
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if method == "pm":
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p_len = x.shape[0] // 160 + 1
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f0_min = 65
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l_pad = int(np.ceil(1.5 / f0_min * 16000))
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r_pad = l_pad + 1
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s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), 16000).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=f0_min,
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pitch_ceiling=1100,
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)
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assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
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f0 = s.selected_array["frequency"]
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if len(f0) < p_len:
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f0 = np.pad(f0, (0, p_len - len(f0)))
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f0 = f0[:p_len]
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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 + 1, dtype=np.float64)
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length = len(x)
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part_length = 160 * ((length // 160 - 1) // n_cpu + 1)
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n_cpu = (length // 160 - 1) // (part_length // 160) + 1
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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:]
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f0bak[part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]] = (
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f0
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)
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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 "privateuseone" in str(
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self.device
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): ###不支持dml，cpu又太慢用不成，拿fcpe顶替
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return self.get_f0(x, f0_up_key, 1, "fcpe")
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if hasattr(self, "model_crepe") == False:
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from rvc.f0 import CRePE
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self.model_crepe = CRePE(
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160,
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self.f0_min,
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self.f0_max,
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16000,
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self.device,
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)
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f0 = self.model_crepe.compute_f0(x)
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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 rvc.f0 import RMVPE
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printt("Loading rmvpe model")
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self.model_rmvpe = RMVPE(
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"assets/rmvpe/rmvpe.pt",
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is_half=self.is_half,
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device=self.device,
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use_jit=self.config.use_jit,
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)
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f0 = self.model_rmvpe.compute_f0(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 get_f0_fcpe(self, x, f0_up_key):
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if hasattr(self, "model_fcpe") == False:
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from rvc.f0 import FCPE
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printt("Loading fcpe model")
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self.model_fcpe = FCPE(
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160,
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self.f0_min,
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self.f0_max,
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16000,
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self.device,
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)
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f0 = self.model_fcpe.compute_f0(x)
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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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input_wav: torch.Tensor,
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block_frame_16k,
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skip_head,
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return_length,
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f0method,
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block_frame_16k: int,
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skip_head: torch.Tensor,
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return_length: int,
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f0method: Union[tuple, str],
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inp_f0: Optional[np.ndarray] = None,
|
||||
protect: float = 1.0,
|
||||
) -> np.ndarray:
|
||||
t1 = ttime()
|
||||
with torch.no_grad():
|
||||
if self.config.is_half:
|
||||
feats = input_wav.half().view(1, -1)
|
||||
if self.is_half:
|
||||
feats = input_wav.half()
|
||||
else:
|
||||
feats = input_wav.float().view(1, -1)
|
||||
feats = input_wav.float()
|
||||
feats = feats.to(self.device)
|
||||
if feats.dim() == 2: # double channels
|
||||
feats = feats.mean(-1)
|
||||
feats = feats.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)
|
||||
logits = self.hubert.extract_features(**inputs)
|
||||
feats = (
|
||||
self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
|
||||
self.hubert.final_proj(logits[0]) if self.version == "v1" else logits[0]
|
||||
)
|
||||
feats = torch.cat((feats, feats[:, -1:, :]), 1)
|
||||
t2 = ttime()
|
||||
if protect < 0.5 and self.if_f0 == 1:
|
||||
feats0 = feats.clone()
|
||||
|
||||
try:
|
||||
if hasattr(self, "index") and self.index_rate != 0:
|
||||
npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
|
||||
if hasattr(self, "index") and self.index_rate > 0:
|
||||
npy = feats[0][skip_head // 2 :].cpu().numpy()
|
||||
if self.is_half:
|
||||
npy = npy.astype("float32")
|
||||
score, ix = self.index.search(npy, k=8)
|
||||
if (ix >= 0).all():
|
||||
weight = np.square(1 / score)
|
||||
@@ -355,38 +180,40 @@ class RVC:
|
||||
npy = np.sum(
|
||||
self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
|
||||
)
|
||||
if self.config.is_half:
|
||||
if self.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
|
||||
pass
|
||||
|
||||
p_len = input_wav.shape[0] // self.window
|
||||
factor = pow(2, self.formant_shift / 12)
|
||||
return_length2 = int(np.ceil(return_length * factor))
|
||||
cache_pitch = cache_pitchf = None
|
||||
if self.if_f0 == 1:
|
||||
pitch = pitchf = None
|
||||
if isinstance(f0method, tuple):
|
||||
pitch, pitchf = f0method
|
||||
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
|
||||
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
|
||||
elif 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.formant_shift,
|
||||
self.n_cpu,
|
||||
f0method,
|
||||
)
|
||||
shift = block_frame_16k // 160
|
||||
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - self.window
|
||||
if inp_f0 is not None:
|
||||
pitch, pitchf = self._get_f0_post(inp_f0, self.f0_up_key - self.formant_shift)
|
||||
else:
|
||||
pitch, pitchf = self._get_f0(
|
||||
input_wav[-f0_extractor_frame:],
|
||||
self.f0_up_key - self.formant_shift,
|
||||
self.n_cpu,
|
||||
3,
|
||||
f0method,
|
||||
)
|
||||
shift = block_frame_16k // self.window
|
||||
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]
|
||||
@@ -395,13 +222,23 @@ class RVC:
|
||||
cache_pitchf = (
|
||||
self.cache_pitchf[None, -p_len:] * return_length2 / return_length
|
||||
)
|
||||
t4 = ttime()
|
||||
|
||||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
feats = feats[:, :p_len, :]
|
||||
if protect < 0.5 and pitch is not None and pitchf is not None:
|
||||
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
|
||||
0, 2, 1
|
||||
)
|
||||
feats0 = feats0[:, :p_len, :]
|
||||
pitchff = pitchf.clone()
|
||||
pitchff[pitchf > 0] = 1
|
||||
pitchff[pitchf < 1] = protect
|
||||
pitchff = pitchff.unsqueeze(-1)
|
||||
feats = feats * pitchff + feats0 * (1 - pitchff)
|
||||
feats = feats.to(feats0.dtype)
|
||||
p_len = torch.LongTensor([p_len]).to(self.device)
|
||||
sid = torch.LongTensor([0]).to(self.device)
|
||||
skip_head = torch.LongTensor([skip_head])
|
||||
# return_length2 = torch.LongTensor([return_length2])
|
||||
return_length = torch.LongTensor([return_length])
|
||||
with torch.no_grad():
|
||||
infered_audio = (
|
||||
@@ -428,12 +265,93 @@ class RVC:
|
||||
infered_audio = self.resample_kernel[upp_res](
|
||||
infered_audio[:, : return_length * upp_res]
|
||||
)
|
||||
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()
|
||||
|
||||
def _get_f0(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
f0_up_key: Union[int, float],
|
||||
filter_radius: Union[int, float],
|
||||
method: Literal["crepe", "rmvpe", "fcpe", "pm", "harvest", "dio"]="fcpe",
|
||||
):
|
||||
if method not in self.f0_methods.keys():
|
||||
raise RuntimeError("Not supported f0 method: "+method)
|
||||
return self.f0_methods[method](x, f0_up_key, filter_radius)
|
||||
|
||||
def _get_f0_post(self, f0, f0_up_key):
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
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_pm(self, x, f0_up_key, filter_radius):
|
||||
if not hasattr(self, "pm"):
|
||||
self.pm = PM(hop_length=160, sampling_rate=16000)
|
||||
f0 = self.pm.compute_f0(x)
|
||||
return self._get_f0_post(f0, f0_up_key)
|
||||
|
||||
def _get_f0_harvest(self, x, f0_up_key, filter_radius):
|
||||
if not hasattr(self, "harvest"):
|
||||
self.harvest = Harvest(
|
||||
self.window,
|
||||
self.f0_min,
|
||||
self.f0_max,
|
||||
self.sr,
|
||||
)
|
||||
f0 = self.harvest.compute_f0(x, filter_radius=filter_radius)
|
||||
return self._get_f0_post(f0, f0_up_key)
|
||||
|
||||
def _get_f0_dio(self, x, f0_up_key, filter_radius):
|
||||
if not hasattr(self, "dio"):
|
||||
self.dio = Dio(
|
||||
self.window,
|
||||
self.f0_min,
|
||||
self.f0_max,
|
||||
self.sr,
|
||||
)
|
||||
f0 = self.dio.compute_f0(x)
|
||||
return self._get_f0_post(f0, f0_up_key)
|
||||
|
||||
def _get_f0_crepe(self, x, f0_up_key, filter_radius):
|
||||
if hasattr(self, "crepe") == False:
|
||||
self.crepe = CRePE(
|
||||
self.window,
|
||||
self.f0_min,
|
||||
self.f0_max,
|
||||
self.sr,
|
||||
self.device,
|
||||
)
|
||||
f0 = self.crepe.compute_f0(x)
|
||||
return self._get_f0_post(f0, f0_up_key)
|
||||
|
||||
def _get_f0_rmvpe(self, x, f0_up_key, filter_radius=0.03):
|
||||
if hasattr(self, "rmvpe") == False:
|
||||
self.rmvpe = RMVPE(
|
||||
"%s/rmvpe.pt" % os.environ["rmvpe_root"],
|
||||
is_half=self.is_half,
|
||||
device=self.device,
|
||||
use_jit=self.use_jit,
|
||||
)
|
||||
return self._get_f0_post(
|
||||
self.rmvpe.compute_f0(x, thred=filter_radius), f0_up_key,
|
||||
)
|
||||
|
||||
def _get_f0_fcpe(self, x, f0_up_key, filter_radius):
|
||||
if hasattr(self, "fcpe") == False:
|
||||
self.fcpe = FCPE(
|
||||
160,
|
||||
self.f0_min,
|
||||
self.f0_max,
|
||||
16000,
|
||||
self.device,
|
||||
)
|
||||
f0 = self.fcpe.compute_f0(x)
|
||||
return self._get_f0_post(f0, f0_up_key)
|
||||
|
||||
Reference in New Issue
Block a user