mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-08-29 10:09:32 +02:00
366 lines
13 KiB
Python
366 lines
13 KiB
Python
import traceback
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from time import time as ttime
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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 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 infer.hubert import extract_hubert_features, load_hubert_model
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from i18n.i18n import I18nAuto
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from tools.cuda_graph import run_cuda_graph
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i18n = I18nAuto()
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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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def get_synthesizer(pth_path, device=torch.device("cpu")):
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from infer.module.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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cpt = torch.load(pth_path, map_location=torch.device("cpu"))
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=False)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=False)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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net_g.load_state_dict(cpt["weight"], strict=False)
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net_g = net_g.float()
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net_g.eval().to(device)
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net_g.remove_weight_norm()
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return net_g, cpt
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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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config,
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last_rvc=None,
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) :
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"""
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初始化
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"""
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try:
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# global config
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self.config = config
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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.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(i18n("已启用索引检索"))
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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.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.infer_count = 0
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self.resample_kernel = {}
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if last_rvc is None:
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self.model = load_hubert_model(self.device, self.is_half)
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else:
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self.model = last_rvc.model
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self.net_g = None
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def set_synthesizer():
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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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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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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.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(i18n("已启用索引检索"))
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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, method="rmvpe"):
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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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if method != "pm":
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raise ValueError(f"Unsupported F0 method: {method}")
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x = x.cpu().numpy()
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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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uv = f0 == 0
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if np.any(~uv):
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f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
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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.rmvpe import RMVPE
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printt(i18n("正在加载RMVPE模型"))
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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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)
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f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
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uv = f0 == 0
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if np.any(~uv):
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f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
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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 infer.fcpe import FCPEInfer
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printt("Loading fcpe model")
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self.model_fcpe = FCPEInfer(self.device)
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f0 = self.model_fcpe.infer(
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x.unsqueeze(0).float(),
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sr=16000,
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decoder_mode="local_argmax",
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threshold=0.006,
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).squeeze().detach().cpu().numpy()
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uv = f0 == 0
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if np.any(~uv):
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f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
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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,
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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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) :
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report_status = self.infer_count < 3 or self.infer_count % 100 == 0
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self.infer_count += 1
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t1 = ttime()
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with torch.no_grad():
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if self.config.is_half:
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feats = input_wav.half().view(1, -1)
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else:
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feats = input_wav.float().view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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feats = extract_hubert_features(
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self.model,
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feats,
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self.version,
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padding_mask=padding_mask,
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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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npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
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score, ix = self.index.search(npy, k=8)
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if (ix >= 0).all():
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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(
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self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
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)
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if self.config.is_half:
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npy = npy.astype("float16")
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feats[0][skip_head // 2 :] = (
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torch.from_numpy(npy).unsqueeze(0).to(self.device)
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* self.index_rate
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+ (1 - self.index_rate) * feats[0][skip_head // 2 :]
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)
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else:
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printt(
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i18n("索引无效：必须使用added_xxxx.index，不能使用trained_xxxx.index")
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)
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else:
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if report_status:
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printt(i18n("索引检索失败或未启用"))
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except Exception:
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traceback.print_exc()
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printt(i18n("索引检索失败"))
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t3 = ttime()
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p_len = input_wav.shape[0] // 160
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factor = pow(2, self.formant_shift / 12)
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return_length2 = int(np.ceil(return_length * factor))
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if self.if_f0 == 1:
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f0_extractor_frame = block_frame_16k + 800
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if f0method == "rmvpe":
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f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
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pitch, pitchf = self.get_f0(
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input_wav[-f0_extractor_frame:],
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self.f0_up_key - self.formant_shift,
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f0method,
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)
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shift = block_frame_16k // 160
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self.cache_pitch[:-shift] = self.cache_pitch[shift:].clone()
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self.cache_pitchf[:-shift] = self.cache_pitchf[shift:].clone()
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self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
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self.cache_pitchf[4 - pitch.shape[0] :] = pitchf[3:-1]
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cache_pitch = self.cache_pitch[None, -p_len:]
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cache_pitchf = self.cache_pitchf[None, -p_len:] * return_length2 / return_length
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t4 = ttime()
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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feats = feats[:, :p_len, :]
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p_len_tensor = torch.LongTensor([p_len]).to(self.device)
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sid = torch.LongTensor([0]).to(self.device)
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skip_head_value = int(skip_head)
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return_length_value = int(return_length)
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return_length2_value = int(return_length2)
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with torch.no_grad():
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if self.if_f0 == 1:
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infered_audio = run_cuda_graph(
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self.net_g,
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"rvc-realtime-f0-%s-%s-%s"
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% (skip_head_value, return_length_value, return_length2_value),
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lambda phone, lengths, coarse, continuous, speaker: self.net_g.infer(
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phone,
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lengths,
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coarse,
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continuous,
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speaker,
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skip_head_value,
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return_length_value,
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return_length2_value,
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)[0],
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feats,
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p_len_tensor,
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cache_pitch,
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cache_pitchf,
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sid,
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)
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else:
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infered_audio = run_cuda_graph(
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self.net_g,
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"rvc-realtime-no-f0-%s-%s-%s"
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% (skip_head_value, return_length_value, return_length2_value),
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lambda phone, lengths, speaker: self.net_g.infer(
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phone,
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lengths,
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speaker,
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skip_head_value,
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return_length_value,
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return_length2_value,
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)[0],
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feats,
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p_len_tensor,
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sid,
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)
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infered_audio = infered_audio.squeeze(1).float()
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upp_res = int(np.floor(factor * self.tgt_sr // 100))
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if upp_res != self.tgt_sr // 100:
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if upp_res not in self.resample_kernel:
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self.resample_kernel[upp_res] = Resample(
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orig_freq=upp_res,
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new_freq=self.tgt_sr // 100,
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dtype=torch.float32,
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).to(self.device)
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infered_audio = self.resample_kernel[upp_res](
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infered_audio[:, : return_length * upp_res]
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)
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t5 = ttime()
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if report_status:
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printt(
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i18n("耗时：特征=%.3f秒，索引=%.3f秒，音高=%.3f秒，模型=%.3f秒"),
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t2 - t1,
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t3 - t2,
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t4 - t3,
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t5 - t4,
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)
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return infered_audio.squeeze()
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