612 lines
23 KiB
Python
612 lines
23 KiB
Python
"""
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0416后的更新：
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引入config中half
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重建npy而不用填写
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v2支持
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无f0模型支持
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修复
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int16：
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增加无索引支持
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f0算法改harvest(怎么看就只有这个会影响CPU占用)，但是不这么改效果不好
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"""
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import os, sys, traceback
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from config import Config
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Config = Config()
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import PySimpleGUI as sg
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import sounddevice as sd
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import noisereduce as nr
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import numpy as np
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from fairseq import checkpoint_utils
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import librosa, torch, pyworld, faiss, time, threading
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import torch.nn.functional as F
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import torchaudio.transforms as tat
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import scipy.signal as signal
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# import matplotlib.pyplot as plt
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from 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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from i18n import I18nAuto
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i18n = I18nAuto()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class RVC:
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def __init__(
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self, key, hubert_path, pth_path, index_path, npy_path, index_rate
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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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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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if index_rate != 0:
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self.index = faiss.read_index(index_path)
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# self.big_npy = np.load(npy_path)
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self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
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print("index search enabled")
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self.index_rate = index_rate
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model_path = hubert_path
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print("load model(s) from {}".format(model_path))
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[model_path],
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suffix="",
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)
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self.model = models[0]
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self.model = self.model.to(device)
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if Config.is_half:
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self.model = self.model.half()
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else:
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self.model = self.model.float()
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self.model.eval()
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cpt = torch.load(pth_path, map_location="cpu")
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self.tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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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.version == "v1":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=Config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif self.version == "v2":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs768NSFsid(
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*cpt["config"], is_half=Config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del self.net_g.enc_q
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print(self.net_g.load_state_dict(cpt["weight"], strict=False))
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self.net_g.eval().to(device)
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if Config.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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except:
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print(traceback.format_exc())
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def get_f0(self, x, f0_up_key, inp_f0=None):
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x_pad = 1
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f0_min = 50
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f0_max = 1100
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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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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=self.sr,
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f0_ceil=f0_max,
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f0_floor=f0_min,
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frame_period=10,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
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f0 = signal.medfilt(f0, 3)
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f0 *= pow(2, f0_up_key / 12)
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# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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tf0 = self.sr // self.window # 每秒f0点数
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if inp_f0 is not None:
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delta_t = np.round(
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(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
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).astype("int16")
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replace_f0 = np.interp(
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list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
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)
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shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
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f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
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# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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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.int)
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return f0_coarse, f0bak # 1-0
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def infer(self, feats: torch.Tensor) -> np.ndarray:
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"""
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推理函数
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"""
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audio = feats.clone().cpu().numpy()
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.half().to(device),
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"padding_mask": padding_mask.to(device),
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"output_layer": 9 if self.version == "v1" else 12,
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}
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torch.cuda.synchronize()
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with torch.no_grad():
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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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####索引优化
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try:
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if (
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hasattr(self, "index")
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and hasattr(self, "big_npy")
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and self.index_rate != 0
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):
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npy = feats[0].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 Config.is_half:
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npy = npy.astype("float16")
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feats = (
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torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
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+ (1 - self.index_rate) * feats
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)
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else:
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print("index search FAIL or disabled")
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except:
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traceback.print_exc()
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print("index search FAIL")
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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torch.cuda.synchronize()
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print(feats.shape)
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if self.if_f0 == 1:
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pitch, pitchf = self.get_f0(audio, self.f0_up_key)
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p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
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else:
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pitch, pitchf = None, None
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p_len = min(feats.shape[1], 13000) # 太大了爆显存
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torch.cuda.synchronize()
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# print(feats.shape,pitch.shape)
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feats = feats[:, :p_len, :]
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if self.if_f0 == 1:
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
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pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
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p_len = torch.LongTensor([p_len]).to(device)
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ii = 0 # sid
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sid = torch.LongTensor([ii]).to(device)
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with torch.no_grad():
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if self.if_f0 == 1:
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infered_audio = (
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self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
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.data.cpu()
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.float()
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)
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else:
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infered_audio = (
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self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
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)
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torch.cuda.synchronize()
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return infered_audio
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class GUIConfig:
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def __init__(self) -> None:
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self.hubert_path: str = ""
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self.pth_path: str = ""
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self.index_path: str = ""
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self.npy_path: str = ""
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self.pitch: int = 12
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self.samplerate: int = 44100
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self.block_time: float = 1.0 # s
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self.buffer_num: int = 1
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self.threhold: int = -30
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self.crossfade_time: float = 0.08
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self.extra_time: float = 0.04
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self.I_noise_reduce = False
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self.O_noise_reduce = False
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self.index_rate = 0.3
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class GUI:
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def __init__(self) -> None:
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self.config = GUIConfig()
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self.flag_vc = False
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self.launcher()
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def launcher(self):
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sg.theme("LightBlue3")
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input_devices, output_devices, _, _ = self.get_devices()
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layout = [
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[
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sg.Frame(
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title=i18n("加载模型"),
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layout=[
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[
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sg.Input(default_text="hubert_base.pt", key="hubert_path"),
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sg.FileBrowse(i18n("Hubert模型")),
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],
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[
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sg.Input(default_text="TEMP\\atri.pth", key="pth_path"),
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sg.FileBrowse(i18n("选择.pth文件")),
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],
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[
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sg.Input(
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default_text="TEMP\\added_IVF512_Flat_atri_baseline_src_feat.index",
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key="index_path",
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),
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sg.FileBrowse(i18n("选择.index文件")),
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],
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[
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sg.Input(
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default_text="你不需要填写这个You don't need write this.",
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key="npy_path",
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),
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sg.FileBrowse(i18n("选择.npy文件")),
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],
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],
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)
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],
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[
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sg.Frame(
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layout=[
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[
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sg.Text(i18n("输入设备")),
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sg.Combo(
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input_devices,
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key="sg_input_device",
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default_value=input_devices[sd.default.device[0]],
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),
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],
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[
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sg.Text(i18n("输出设备")),
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sg.Combo(
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output_devices,
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key="sg_output_device",
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default_value=output_devices[sd.default.device[1]],
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),
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],
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],
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title=i18n("音频设备(请使用同种类驱动)"),
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)
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],
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[
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sg.Frame(
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layout=[
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[
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sg.Text(i18n("响应阈值")),
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sg.Slider(
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range=(-60, 0),
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key="threhold",
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resolution=1,
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orientation="h",
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default_value=-30,
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),
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],
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[
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sg.Text(i18n("音调设置")),
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sg.Slider(
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range=(-24, 24),
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key="pitch",
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resolution=1,
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orientation="h",
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default_value=12,
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),
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],
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[
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sg.Text(i18n("Index Rate")),
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sg.Slider(
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range=(0.0, 1.0),
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key="index_rate",
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resolution=0.01,
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orientation="h",
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default_value=0.5,
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),
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],
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],
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title=i18n("常规设置"),
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),
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sg.Frame(
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layout=[
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[
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sg.Text(i18n("采样长度")),
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sg.Slider(
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range=(0.1, 3.0),
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key="block_time",
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resolution=0.1,
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orientation="h",
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default_value=1.0,
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),
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],
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[
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sg.Text(i18n("淡入淡出长度")),
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sg.Slider(
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range=(0.01, 0.15),
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key="crossfade_length",
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resolution=0.01,
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orientation="h",
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default_value=0.08,
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),
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],
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[
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sg.Text(i18n("额外推理时长")),
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sg.Slider(
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range=(0.05, 3.00),
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key="extra_time",
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resolution=0.01,
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orientation="h",
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default_value=0.05,
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),
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],
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[
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sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"),
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sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"),
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],
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],
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title=i18n("性能设置"),
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),
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],
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[
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sg.Button(i18n("开始音频转换"), key="start_vc"),
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sg.Button(i18n("停止音频转换"), key="stop_vc"),
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sg.Text(i18n("推理时间(ms):")),
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sg.Text("0", key="infer_time"),
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],
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]
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self.window = sg.Window("RVC - GUI", layout=layout)
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self.event_handler()
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def event_handler(self):
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while True:
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event, values = self.window.read()
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if event == sg.WINDOW_CLOSED:
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self.flag_vc = False
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exit()
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if event == "start_vc" and self.flag_vc == False:
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self.set_values(values)
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print(str(self.config.__dict__))
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print("using_cuda:" + str(torch.cuda.is_available()))
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self.start_vc()
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if event == "stop_vc" and self.flag_vc == True:
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self.flag_vc = False
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def set_values(self, values):
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self.set_devices(values["sg_input_device"], values["sg_output_device"])
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self.config.hubert_path = values["hubert_path"]
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self.config.pth_path = values["pth_path"]
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self.config.index_path = values["index_path"]
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self.config.npy_path = values["npy_path"]
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self.config.threhold = values["threhold"]
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self.config.pitch = values["pitch"]
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self.config.block_time = values["block_time"]
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self.config.crossfade_time = values["crossfade_length"]
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self.config.extra_time = values["extra_time"]
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self.config.I_noise_reduce = values["I_noise_reduce"]
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self.config.O_noise_reduce = values["O_noise_reduce"]
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self.config.index_rate = values["index_rate"]
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def start_vc(self):
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torch.cuda.empty_cache()
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self.flag_vc = True
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self.block_frame = int(self.config.block_time * self.config.samplerate)
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self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
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self.sola_search_frame = int(0.012 * self.config.samplerate)
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self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
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self.extra_frame = int(self.config.extra_time * self.config.samplerate)
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self.rvc = None
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self.rvc = RVC(
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self.config.pitch,
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self.config.hubert_path,
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self.config.pth_path,
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self.config.index_path,
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self.config.npy_path,
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self.config.index_rate,
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)
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self.input_wav: np.ndarray = np.zeros(
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self.extra_frame
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+ self.crossfade_frame
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+ self.sola_search_frame
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+ self.block_frame,
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dtype="float32",
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)
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self.output_wav: torch.Tensor = torch.zeros(
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self.block_frame, device=device, dtype=torch.float32
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)
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self.sola_buffer: torch.Tensor = torch.zeros(
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self.crossfade_frame, device=device, dtype=torch.float32
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)
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self.fade_in_window: torch.Tensor = torch.linspace(
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0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
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)
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self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
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self.resampler1 = tat.Resample(
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orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
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)
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self.resampler2 = tat.Resample(
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orig_freq=self.rvc.tgt_sr,
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new_freq=self.config.samplerate,
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dtype=torch.float32,
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)
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thread_vc = threading.Thread(target=self.soundinput)
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thread_vc.start()
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def soundinput(self):
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"""
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接受音频输入
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"""
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with sd.Stream(
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callback=self.audio_callback,
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blocksize=self.block_frame,
|
||
samplerate=self.config.samplerate,
|
||
dtype="float32",
|
||
):
|
||
while self.flag_vc:
|
||
time.sleep(self.config.block_time)
|
||
print("Audio block passed.")
|
||
print("ENDing VC")
|
||
|
||
def audio_callback(
|
||
self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
|
||
):
|
||
"""
|
||
音频处理
|
||
"""
|
||
start_time = time.perf_counter()
|
||
indata = librosa.to_mono(indata.T)
|
||
if self.config.I_noise_reduce:
|
||
indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
|
||
|
||
"""noise gate"""
|
||
frame_length = 2048
|
||
hop_length = 1024
|
||
rms = librosa.feature.rms(
|
||
y=indata, frame_length=frame_length, hop_length=hop_length
|
||
)
|
||
db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
|
||
# print(rms.shape,db.shape,db)
|
||
for i in range(db_threhold.shape[0]):
|
||
if db_threhold[i]:
|
||
indata[i * hop_length : (i + 1) * hop_length] = 0
|
||
self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
|
||
|
||
# infer
|
||
print("input_wav:" + str(self.input_wav.shape))
|
||
# print('infered_wav:'+str(infer_wav.shape))
|
||
infer_wav: torch.Tensor = self.resampler2(
|
||
self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
|
||
)[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
|
||
device
|
||
)
|
||
print("infer_wav:" + str(infer_wav.shape))
|
||
|
||
# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
|
||
cor_nom = F.conv1d(
|
||
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
|
||
self.sola_buffer[None, None, :],
|
||
)
|
||
cor_den = torch.sqrt(
|
||
F.conv1d(
|
||
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
|
||
** 2,
|
||
torch.ones(1, 1, self.crossfade_frame, device=device),
|
||
)
|
||
+ 1e-8
|
||
)
|
||
sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
|
||
print("sola offset: " + str(int(sola_offset)))
|
||
|
||
# crossfade
|
||
self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
|
||
self.output_wav[: self.crossfade_frame] *= self.fade_in_window
|
||
self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
|
||
if sola_offset < self.sola_search_frame:
|
||
self.sola_buffer[:] = (
|
||
infer_wav[
|
||
-self.sola_search_frame
|
||
- self.crossfade_frame
|
||
+ sola_offset : -self.sola_search_frame
|
||
+ sola_offset
|
||
]
|
||
* self.fade_out_window
|
||
)
|
||
else:
|
||
self.sola_buffer[:] = (
|
||
infer_wav[-self.crossfade_frame :] * self.fade_out_window
|
||
)
|
||
|
||
if self.config.O_noise_reduce:
|
||
outdata[:] = np.tile(
|
||
nr.reduce_noise(
|
||
y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
|
||
),
|
||
(2, 1),
|
||
).T
|
||
else:
|
||
outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
|
||
total_time = time.perf_counter() - start_time
|
||
self.window["infer_time"].update(int(total_time * 1000))
|
||
print("infer time:" + str(total_time))
|
||
|
||
def get_devices(self, update: bool = True):
|
||
"""获取设备列表"""
|
||
if update:
|
||
sd._terminate()
|
||
sd._initialize()
|
||
devices = sd.query_devices()
|
||
hostapis = sd.query_hostapis()
|
||
for hostapi in hostapis:
|
||
for device_idx in hostapi["devices"]:
|
||
devices[device_idx]["hostapi_name"] = hostapi["name"]
|
||
input_devices = [
|
||
f"{d['name']} ({d['hostapi_name']})"
|
||
for d in devices
|
||
if d["max_input_channels"] > 0
|
||
]
|
||
output_devices = [
|
||
f"{d['name']} ({d['hostapi_name']})"
|
||
for d in devices
|
||
if d["max_output_channels"] > 0
|
||
]
|
||
input_devices_indices = [
|
||
d["index"] for d in devices if d["max_input_channels"] > 0
|
||
]
|
||
output_devices_indices = [
|
||
d["index"] for d in devices if d["max_output_channels"] > 0
|
||
]
|
||
return (
|
||
input_devices,
|
||
output_devices,
|
||
input_devices_indices,
|
||
output_devices_indices,
|
||
)
|
||
|
||
def set_devices(self, input_device, output_device):
|
||
"""设置输出设备"""
|
||
(
|
||
input_devices,
|
||
output_devices,
|
||
input_device_indices,
|
||
output_device_indices,
|
||
) = self.get_devices()
|
||
sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
|
||
sd.default.device[1] = output_device_indices[
|
||
output_devices.index(output_device)
|
||
]
|
||
print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
|
||
print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
|
||
|
||
|
||
gui = GUI()
|