Delete gui_v0.py

gui_v0.py

@@ -1,696 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
-    def __init__(
-        self, key, hubert_path, pth_path, index_path, npy_path, index_rate
-    ) -> None:
-        """
-        初始化
-        """
-        try:
-            self.f0_up_key = key
-            self.time_step = 160 / 16000 * 1000
-            self.f0_min = 50
-            self.f0_max = 1100
-            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-            self.sr = 16000
-            self.window = 160
-            if index_rate != 0:
-                self.index = faiss.read_index(index_path)
-                # self.big_npy = np.load(npy_path)
-                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-                print("index search enabled")
-            self.index_rate = index_rate
-            model_path = hubert_path
-            print("load model(s) from {}".format(model_path))
-            models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-                [model_path],
-                suffix="",
-            )
-            self.model = models[0]
-            self.model = self.model.to(device)
-            if Config.is_half:
-                self.model = self.model.half()
-            else:
-                self.model = self.model.float()
-            self.model.eval()
-            cpt = torch.load(pth_path, map_location="cpu")
-            self.tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            self.if_f0 = cpt.get("f0", 1)
-            self.version = cpt.get("version", "v1")
-            if self.version == "v1":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs256NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            elif self.version == "v2":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs768NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-            del self.net_g.enc_q
-            print(self.net_g.load_state_dict(cpt["weight"], strict=False))
-            self.net_g.eval().to(device)
-            if Config.is_half:
-                self.net_g = self.net_g.half()
-            else:
-                self.net_g = self.net_g.float()
-        except:
-            print(traceback.format_exc())
-
-    def get_f0(self, x, f0_up_key, inp_f0=None):
-        x_pad = 1
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0, t = pyworld.harvest(
-            x.astype(np.double),
-            fs=self.sr,
-            f0_ceil=f0_max,
-            f0_floor=f0_min,
-            frame_period=10,
-        )
-        f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-        f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def infer(self, feats: torch.Tensor) -> np.ndarray:
-        """
-        推理函数
-        """
-        audio = feats.clone().cpu().numpy()
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        if Config.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        inputs = {
-            "source": feats.to(device),
-            "padding_mask": padding_mask.to(device),
-            "output_layer": 9 if self.version == "v1" else 12,
-        }
-        torch.cuda.synchronize()
-        with torch.no_grad():
-            logits = self.model.extract_features(**inputs)
-            feats = (
-                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
-            )
-
-        ####索引优化
-        try:
-            if (
-                hasattr(self, "index")
-                and hasattr(self, "big_npy")
-                and self.index_rate != 0
-            ):
-                npy = feats[0].cpu().numpy().astype("float32")
-                score, ix = self.index.search(npy, k=8)
-                weight = np.square(1 / score)
-                weight /= weight.sum(axis=1, keepdims=True)
-                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-                if Config.is_half:
-                    npy = npy.astype("float16")
-                feats = (
-                    torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
-                    + (1 - self.index_rate) * feats
-                )
-            else:
-                print("index search FAIL or disabled")
-        except:
-            traceback.print_exc()
-            print("index search FAIL")
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        torch.cuda.synchronize()
-        print(feats.shape)
-        if self.if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio, self.f0_up_key)
-            p_len = min(feats.shape[1], 13000, pitch.shape[0])  # 太大了爆显存
-        else:
-            pitch, pitchf = None, None
-            p_len = min(feats.shape[1], 13000)  # 太大了爆显存
-        torch.cuda.synchronize()
-        # print(feats.shape,pitch.shape)
-        feats = feats[:, :p_len, :]
-        if self.if_f0 == 1:
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
-            pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
-        p_len = torch.LongTensor([p_len]).to(device)
-        ii = 0  # sid
-        sid = torch.LongTensor([ii]).to(device)
-        with torch.no_grad():
-            if self.if_f0 == 1:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
-                    .data.cpu()
-                    .float()
-                )
-            else:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
-                )
-        torch.cuda.synchronize()
-        return infered_audio
-
-
-class GUIConfig:
-    def __init__(self) -> None:
-        self.hubert_path: str = ""
-        self.pth_path: str = ""
-        self.index_path: str = ""
-        self.npy_path: str = ""
-        self.pitch: int = 12
-        self.samplerate: int = 44100
-        self.block_time: float = 1.0  # s
-        self.buffer_num: int = 1
-        self.threhold: int = -30
-        self.crossfade_time: float = 0.08
-        self.extra_time: float = 0.04
-        self.I_noise_reduce = False
-        self.O_noise_reduce = False
-        self.index_rate = 0.3
-
-
-class GUI:
-    def __init__(self) -> None:
-        self.config = GUIConfig()
-        self.flag_vc = False
-
-        self.launcher()
-
-    def load(self):
-        (
-            input_devices,
-            output_devices,
-            input_devices_indices,
-            output_devices_indices,
-        ) = self.get_devices()
-        try:
-            with open("values1.json", "r") as j:
-                data = json.load(j)
-        except:
-            with open("values1.json", "w") as j:
-                data = {
-                    "pth_path": "",
-                    "index_path": "",
-                    "sg_input_device": input_devices[
-                        input_devices_indices.index(sd.default.device[0])
-                    ],
-                    "sg_output_device": output_devices[
-                        output_devices_indices.index(sd.default.device[1])
-                    ],
-                    "threhold": "-45",
-                    "pitch": "0",
-                    "index_rate": "0",
-                    "block_time": "1",
-                    "crossfade_length": "0.04",
-                    "extra_time": "1",
-                }
-        return data
-
-    def launcher(self):
-        data = self.load()
-        sg.theme("LightBlue3")
-        input_devices, output_devices, _, _ = self.get_devices()
-        layout = [
-            [
-                sg.Frame(
-                    title=i18n("加载模型"),
-                    layout=[
-                        [
-                            sg.Input(
-                                default_text="hubert_base.pt",
-                                key="hubert_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("Hubert模型"),
-                                initial_folder=os.path.join(os.getcwd()),
-                                file_types=(("pt files", "*.pt"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("pth_path", ""),
-                                key="pth_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("选择.pth文件"),
-                                initial_folder=os.path.join(os.getcwd(), "weights"),
-                                file_types=(("weight files", "*.pth"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("index_path", ""),
-                                key="index_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("选择.index文件"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("index files", "*.index"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text="你不需要填写这个You don't need write this.",
-                                key="npy_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("选择.npy文件"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("feature files", "*.npy"),),
-                            ),
-                        ],
-                    ],
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("输入设备")),
-                            sg.Combo(
-                                input_devices,
-                                key="sg_input_device",
-                                default_value=data.get("sg_input_device", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("输出设备")),
-                            sg.Combo(
-                                output_devices,
-                                key="sg_output_device",
-                                default_value=data.get("sg_output_device", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("音频设备(请使用同种类驱动)"),
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("响应阈值")),
-                            sg.Slider(
-                                range=(-60, 0),
-                                key="threhold",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("threhold", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("音调设置")),
-                            sg.Slider(
-                                range=(-24, 24),
-                                key="pitch",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("pitch", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Index Rate")),
-                            sg.Slider(
-                                range=(0.0, 1.0),
-                                key="index_rate",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("index_rate", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("常规设置"),
-                ),
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("采样长度")),
-                            sg.Slider(
-                                range=(0.1, 3.0),
-                                key="block_time",
-                                resolution=0.1,
-                                orientation="h",
-                                default_value=data.get("block_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("淡入淡出长度")),
-                            sg.Slider(
-                                range=(0.01, 0.15),
-                                key="crossfade_length",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("crossfade_length", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("额外推理时长")),
-                            sg.Slider(
-                                range=(0.05, 3.00),
-                                key="extra_time",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("extra_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"),
-                            sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"),
-                        ],
-                    ],
-                    title=i18n("性能设置"),
-                ),
-            ],
-            [
-                sg.Button(i18n("开始音频转换"), key="start_vc"),
-                sg.Button(i18n("停止音频转换"), key="stop_vc"),
-                sg.Text(i18n("推理时间(ms):")),
-                sg.Text("0", key="infer_time"),
-            ],
-        ]
-        self.window = sg.Window("RVC - GUI", layout=layout)
-        self.event_handler()
-
-    def event_handler(self):
-        while True:
-            event, values = self.window.read()
-            if event == sg.WINDOW_CLOSED:
-                self.flag_vc = False
-                exit()
-            if event == "start_vc" and self.flag_vc == False:
-                if self.set_values(values) == True:
-                    print("using_cuda:" + str(torch.cuda.is_available()))
-                    self.start_vc()
-                    settings = {
-                        "pth_path": values["pth_path"],
-                        "index_path": values["index_path"],
-                        "sg_input_device": values["sg_input_device"],
-                        "sg_output_device": values["sg_output_device"],
-                        "threhold": values["threhold"],
-                        "pitch": values["pitch"],
-                        "index_rate": values["index_rate"],
-                        "block_time": values["block_time"],
-                        "crossfade_length": values["crossfade_length"],
-                        "extra_time": values["extra_time"],
-                    }
-                    with open("values1.json", "w") as j:
-                        json.dump(settings, j)
-            if event == "stop_vc" and self.flag_vc == True:
-                self.flag_vc = False
-
-    def set_values(self, values):
-        if len(values["pth_path"].strip()) == 0:
-            sg.popup(i18n("请选择pth文件"))
-            return False
-        if len(values["index_path"].strip()) == 0:
-            sg.popup(i18n("请选择index文件"))
-            return False
-        pattern = re.compile("[^\x00-\x7F]+")
-        if pattern.findall(values["hubert_path"]):
-            sg.popup(i18n("hubert模型路径不可包含中文"))
-            return False
-        if pattern.findall(values["pth_path"]):
-            sg.popup(i18n("pth文件路径不可包含中文"))
-            return False
-        if pattern.findall(values["index_path"]):
-            sg.popup(i18n("index文件路径不可包含中文"))
-            return False
-        self.set_devices(values["sg_input_device"], values["sg_output_device"])
-        self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
-        self.config.pth_path = values["pth_path"]
-        self.config.index_path = values["index_path"]
-        self.config.npy_path = values["npy_path"]
-        self.config.threhold = values["threhold"]
-        self.config.pitch = values["pitch"]
-        self.config.block_time = values["block_time"]
-        self.config.crossfade_time = values["crossfade_length"]
-        self.config.extra_time = values["extra_time"]
-        self.config.I_noise_reduce = values["I_noise_reduce"]
-        self.config.O_noise_reduce = values["O_noise_reduce"]
-        self.config.index_rate = values["index_rate"]
-        return True
-
-    def start_vc(self):
-        torch.cuda.empty_cache()
-        self.flag_vc = True
-        self.block_frame = int(self.config.block_time * self.config.samplerate)
-        self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
-        self.sola_search_frame = int(0.012 * self.config.samplerate)
-        self.delay_frame = int(0.01 * self.config.samplerate)  # 往前预留0.02s
-        self.extra_frame = int(self.config.extra_time * self.config.samplerate)
-        self.rvc = None
-        self.rvc = RVC(
-            self.config.pitch,
-            self.config.hubert_path,
-            self.config.pth_path,
-            self.config.index_path,
-            self.config.npy_path,
-            self.config.index_rate,
-        )
-        self.input_wav: np.ndarray = np.zeros(
-            self.extra_frame
-            + self.crossfade_frame
-            + self.sola_search_frame
-            + self.block_frame,
-            dtype="float32",
-        )
-        self.output_wav: torch.Tensor = torch.zeros(
-            self.block_frame, device=device, dtype=torch.float32
-        )
-        self.sola_buffer: torch.Tensor = torch.zeros(
-            self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_in_window: torch.Tensor = torch.linspace(
-            0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
-        self.resampler1 = tat.Resample(
-            orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
-        )
-        self.resampler2 = tat.Resample(
-            orig_freq=self.rvc.tgt_sr,
-            new_freq=self.config.samplerate,
-            dtype=torch.float32,
-        )
-        thread_vc = threading.Thread(target=self.soundinput)
-        thread_vc.start()
-
-    def soundinput(self):
-        """
-        接受音频输入
-        """
-        with sd.Stream(
-            channels=2,
-            callback=self.audio_callback,
-            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"] if "index" in d else d["name"]
-            for d in devices
-            if d["max_input_channels"] > 0
-        ]
-        output_devices_indices = [
-            d["index"] if "index" in d else d["name"]
-            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()