mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 02:55:05 +08:00
add fcpe for realtime
This commit is contained in:
CN_ChiTu
GitHub
parent
ff093ad88f
commit
e7e9d5934d
@ -1,421 +1,438 @@
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from io import BytesIO
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import os
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import pickle
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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 infer.lib.jit.get_synthesizer import get_synthesizer
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from time import time as ttime
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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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import torchcrepe
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from infer.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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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.config 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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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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) -> 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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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.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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self.use_jit = self.config.use_jit
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self.is_half = config.is_half
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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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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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else:
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self.model = last_rvc.model
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self.net_g: nn.Module = None
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def set_default_model():
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self.net_g, cpt = get_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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def set_jit_model():
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jit_pth_path = self.pth_path.rstrip(".pth")
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jit_pth_path += ".half.jit" if self.is_half else ".jit"
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reload = False
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if str(self.device) == "cuda":
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self.device = torch.device("cuda:0")
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if os.path.exists(jit_pth_path):
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cpt = jit.load(jit_pth_path)
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model_device = cpt["device"]
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if model_device != str(self.device):
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reload = True
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else:
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reload = True
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if reload:
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cpt = jit.synthesizer_jit_export(
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self.pth_path,
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"script",
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None,
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device=self.device,
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is_half=self.is_half,
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)
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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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except:
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printt(traceback.format_exc())
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def change_key(self, new_key):
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self.f0_up_key = new_key
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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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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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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 + 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[
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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 "privateuseone" in str(self.device): ###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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# printt("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 infer.lib.rmvpe import RMVPE
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printt("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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"assets/rmvpe/rmvpe.pt",
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is_half=self.is_half,
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device=self.device, ####正常逻辑
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use_jit=self.config.use_jit,
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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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block_frame_16k,
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rate,
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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 self.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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feats = torch.cat((feats, feats[:, -1:, :]), 1)
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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(rate * 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 self.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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printt("Index search FAILED or disabled")
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except:
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traceback.print_exc()
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printt("Index search FAILED")
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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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start_frame = block_frame_16k // 160
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end_frame = len(cache_pitch) - (pitch.shape[0] - 4) + start_frame
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cache_pitch[:] = np.append(cache_pitch[start_frame:end_frame], pitch[3:-1])
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cache_pitchf[:] = np.append(
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cache_pitchf[start_frame:end_frame], pitchf[3:-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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# printt(12222222222,feats.device,p_len.device,cache_pitch.device,cache_pitchf.device,sid.device,rate2)
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infered_audio = self.net_g.infer(
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feats,
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p_len,
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cache_pitch,
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cache_pitchf,
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sid,
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torch.FloatTensor([rate]),
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)[0][0, 0].data.float()
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else:
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infered_audio = self.net_g.infer(
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feats, p_len, sid, torch.FloatTensor([rate])
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)[0][0, 0].data.float()
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t5 = ttime()
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printt(
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"Spent time: fea = %.2fs, index = %.2fs, f0 = %.2fs, model = %.2fs",
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||||
t2 - t1,
|
||||
t3 - t2,
|
||||
t4 - t3,
|
||||
t5 - t4,
|
||||
)
|
||||
return infered_audio
|
||||
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
|
||||
|
||||
# config = Config()
|
||||
|
||||
mm = M()
|
||||
|
||||
|
||||
def printt(strr, *args):
|
||||
if len(args) == 0:
|
||||
print(strr)
|
||||
else:
|
||||
print(strr % args)
|
||||
|
||||
|
||||
# config.device=torch.device("cpu")########强制cpu测试
|
||||
# config.is_half=False########强制cpu测试
|
||||
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
|
||||
# global config
|
||||
self.config = config
|
||||
self.inp_q = inp_q
|
||||
self.opt_q = opt_q
|
||||
# device="cpu"########强制cpu测试
|
||||
self.device = config.device
|
||||
self.f0_up_key = key
|
||||
self.time_step = 160 / 16000 * 1000
|
||||
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.sr = 16000
|
||||
self.window = 160
|
||||
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
|
||||
|
||||
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.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):
|
||||
f0_min = self.f0_min
|
||||
f0_max = self.f0_max
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
f0bak = f0.copy()
|
||||
f0_mel = 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
|
||||
f0_mel_max - f0_mel_min
|
||||
) + 1
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > 255] = 255
|
||||
f0_coarse = np.rint(f0_mel).astype(np.int32)
|
||||
return f0_coarse, f0bak
|
||||
|
||||
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)
|
||||
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): ###不支持dml，cpu又太慢用不成，拿pm顶替
|
||||
return self.get_f0(x, f0_up_key, 1, "pm")
|
||||
audio = torch.tensor(np.copy(x))[None].float()
|
||||
# printt("using crepe,device:%s"%self.device)
|
||||
f0, pd = torchcrepe.predict(
|
||||
audio,
|
||||
self.sr,
|
||||
160,
|
||||
self.f0_min,
|
||||
self.f0_max,
|
||||
"full",
|
||||
batch_size=512,
|
||||
# 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
|
||||
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 infer.lib.rmvpe import RMVPE
|
||||
|
||||
printt("Loading rmvpe model")
|
||||
self.model_rmvpe = RMVPE(
|
||||
# "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
|
||||
# "rmvpe.pt", is_half=False, device=self.device####dml配置
|
||||
# "rmvpe.pt", is_half=False, device="cpu"####锁定cpu配置
|
||||
"assets/rmvpe/rmvpe.pt",
|
||||
is_half=self.is_half,
|
||||
device=self.device, ####正常逻辑
|
||||
use_jit=self.config.use_jit,
|
||||
)
|
||||
# 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 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")
|
||||
self.model_fcpe = spawn_bundled_infer_model(self.device)
|
||||
f0 = self.model_fcpe.infer(
|
||||
torch.from_numpy(x).to(self.device).unsqueeze(0).float(),
|
||||
sr=16000,
|
||||
decoder_mode='local_argmax',
|
||||
threshold=0.006,
|
||||
).squeeze().cpu().numpy()
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
|
||||
def infer(
|
||||
self,
|
||||
feats: torch.Tensor,
|
||||
indata: np.ndarray,
|
||||
block_frame_16k,
|
||||
rate,
|
||||
cache_pitch,
|
||||
cache_pitchf,
|
||||
f0method,
|
||||
) -> np.ndarray:
|
||||
feats = feats.view(1, -1)
|
||||
if self.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]
|
||||
)
|
||||
feats = torch.cat((feats, feats[:, -1:, :]), 1)
|
||||
t2 = ttime()
|
||||
try:
|
||||
if hasattr(self, "index") and self.index_rate != 0:
|
||||
leng_replace_head = int(rate * 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 self.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:
|
||||
printt("Index search FAILED or disabled")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
printt("Index search FAILED")
|
||||
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)
|
||||
start_frame = block_frame_16k // 160
|
||||
end_frame = len(cache_pitch) - (pitch.shape[0] - 4) + start_frame
|
||||
cache_pitch[:] = np.append(cache_pitch[start_frame:end_frame], pitch[3:-1])
|
||||
cache_pitchf[:] = np.append(
|
||||
cache_pitchf[start_frame:end_frame], pitchf[3:-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:
|
||||
# printt(12222222222,feats.device,p_len.device,cache_pitch.device,cache_pitchf.device,sid.device,rate2)
|
||||
infered_audio = self.net_g.infer(
|
||||
feats,
|
||||
p_len,
|
||||
cache_pitch,
|
||||
cache_pitchf,
|
||||
sid,
|
||||
torch.FloatTensor([rate]),
|
||||
)[0][0, 0].data.float()
|
||||
else:
|
||||
infered_audio = self.net_g.infer(
|
||||
feats, p_len, sid, torch.FloatTensor([rate])
|
||||
)[0][0, 0].data.float()
|
||||
t5 = ttime()
|
||||
printt(
|
||||
"Spent time: fea = %.2fs, index = %.2fs, f0 = %.2fs, model = %.2fs",
|
||||
t2 - t1,
|
||||
t3 - t2,
|
||||
t4 - t3,
|
||||
t5 - t4,
|
||||
)
|
||||
return infered_audio
|
||||
|
||||
Loading…
Reference in New Issue
Block a user