c8261b2ccc
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
541 lines
20 KiB
Python
541 lines
20 KiB
Python
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, parselmouth, 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 matplotlib.pyplot as plt
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from webui_locale 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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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.index = faiss.read_index(index_path)
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self.index_rate = index_rate
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"""NOT YET USED"""
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self.big_npy = np.load(npy_path)
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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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self.model = self.model.half()
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self.model.eval()
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cpt = torch.load(pth_path, map_location="cpu")
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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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if_f0 = cpt.get("f0", 1)
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if if_f0 == 1:
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self.net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=True)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_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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self.net_g.half()
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def get_f0_coarse(self, f0):
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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] - 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_mel[f0_mel > 188] = 188
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse
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def get_f0(self, x, p_len, f0_up_key=0):
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f0 = (
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parselmouth.Sound(x, 16000)
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.to_pitch_ac(
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time_step=self.time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=self.f0_min,
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pitch_ceiling=self.f0_max,
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)
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
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f0 *= pow(2, f0_up_key / 12)
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# f0=suofang(f0)
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f0bak = f0.copy()
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f0_coarse = self.get_f0_coarse(f0)
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return f0_coarse, f0bak
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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, # layer 9
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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 = self.model.final_proj(logits[0])
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####索引优化
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if (
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isinstance(self.index, type(None)) == False
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and isinstance(self.big_npy, type(None)) == False
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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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_, I = self.index.search(npy, 1)
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npy = self.big_npy[I.squeeze()].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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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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# p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
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p_len = min(feats.shape[1], 12000) #
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print(feats.shape)
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pitch, pitchf = self.get_f0(audio, p_len, self.f0_up_key)
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p_len = min(feats.shape[1], 12000, pitch.shape[0]) # 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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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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p_len = torch.LongTensor([p_len]).to(device)
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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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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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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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) # nsf
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torch.cuda.synchronize()
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return infered_audio
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class Config:
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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 = Config()
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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(
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default_text="TEMP\\hubert_base.pt", key="hubert_path"
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),
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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="TEMP\\big_src_feature_atri.npy",
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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(0.02 * self.config.samplerate) # 往前预留0.02s
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self.extra_frame = int(
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self.config.extra_time * self.config.samplerate
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) # 往后预留0.04s
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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=40000, new_freq=self.config.samplerate, 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,
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samplerate=self.config.samplerate,
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dtype="float32",
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):
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while self.flag_vc:
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time.sleep(self.config.block_time)
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print("Audio block passed.")
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print("ENDing VC")
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def audio_callback(
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self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
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):
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"""
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音频处理
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"""
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start_time = time.perf_counter()
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indata = librosa.to_mono(indata.T)
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if self.config.I_noise_reduce:
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indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
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"""noise gate"""
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frame_length = 2048
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hop_length = 1024
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rms = librosa.feature.rms(
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y=indata, frame_length=frame_length, hop_length=hop_length
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)
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db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
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# print(rms.shape,db.shape,db)
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for i in range(db_threhold.shape[0]):
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if db_threhold[i]:
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indata[i * hop_length : (i + 1) * hop_length] = 0
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self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
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# infer
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print("input_wav:" + str(self.input_wav.shape))
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# print('infered_wav:'+str(infer_wav.shape))
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infer_wav: torch.Tensor = self.resampler2(
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self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
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)[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
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device
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)
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print("infer_wav:" + str(infer_wav.shape))
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# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
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cor_nom = F.conv1d(
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infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
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self.sola_buffer[None, None, :],
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)
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cor_den = torch.sqrt(
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F.conv1d(
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infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
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** 2,
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torch.ones(1, 1, self.crossfade_frame, device=device),
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)
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+ 1e-8
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)
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sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
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print("sola offset: " + str(int(sola_offset)))
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# crossfade
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self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
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self.output_wav[: self.crossfade_frame] *= self.fade_in_window
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|
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
|
|
print("infer time:" + str(total_time))
|
|
self.window["infer_time"].update(int(total_time * 1000))
|
|
|
|
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()
|