462 lines
17 KiB
Python
462 lines
17 KiB
Python
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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 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 torchaudio.transforms import Resample
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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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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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) -> 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.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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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.resample_kernel = {}
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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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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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self.f0_up_key = new_key
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def change_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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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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# printt("using crepe,device:%s"%self.device)
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f0, pd = torchcrepe.predict(
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x.unsqueeze(0).float(),
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16000,
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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 *= 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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"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.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 get_f0_fcpe(self, x, f0_up_key):
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if hasattr(self, "model_fcpe") == False:
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from torchfcpe import spawn_bundled_infer_model
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printt("Loading fcpe model")
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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
|
|||
|
|
factor = pow(2, self.formant_shift / 12)
|
|||
|
|
return_length2 = int(np.ceil(return_length * factor))
|
|||
|
|
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.formant_shift, 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:] * 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, :]
|
|||
|
|
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():
|
|||
|
|
if self.if_f0 == 1:
|
|||
|
|
infered_audio, _, _ = self.net_g.infer(
|
|||
|
|
feats,
|
|||
|
|
p_len,
|
|||
|
|
cache_pitch,
|
|||
|
|
cache_pitchf,
|
|||
|
|
sid,
|
|||
|
|
skip_head,
|
|||
|
|
return_length,
|
|||
|
|
return_length2,
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
infered_audio, _, _ = self.net_g.infer(
|
|||
|
|
feats, p_len, sid, skip_head, return_length, return_length2
|
|||
|
|
)
|
|||
|
|
infered_audio = infered_audio.squeeze(1).float()
|
|||
|
|
upp_res = int(np.floor(factor * self.tgt_sr // 100))
|
|||
|
|
if upp_res != self.tgt_sr // 100:
|
|||
|
|
if upp_res not in self.resample_kernel:
|
|||
|
|
self.resample_kernel[upp_res] = Resample(
|
|||
|
|
orig_freq=upp_res,
|
|||
|
|
new_freq=self.tgt_sr // 100,
|
|||
|
|
dtype=torch.float32,
|
|||
|
|
).to(self.device)
|
|||
|
|
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()
|