Delete tools/rvc_for_realtime.py

tools/rvc_for_realtime.py

@@ -1,445 +0,0 @@
-from io import BytesIO
-import os
-import pickle
-import sys
-import traceback
-from infer.lib import jit
-from infer.lib.jit.get_synthesizer import get_synthesizer
-from time import time as ttime
-import fairseq
-import faiss
-import numpy as np
-import parselmouth
-import pyworld
-import scipy.signal as signal
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchcrepe
-
-from infer.lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from multiprocessing import Manager as M
-
-from configs.config import Config
-
-# config = Config()
-
-mm = M()
-
-
-def printt(strr, *args):
-    if len(args) == 0:
-        print(strr)
-    else:
-        print(strr % args)
-
-
-# config.device=torch.device("cpu")########强制cpu测试
-# config.is_half=False########强制cpu测试
-class RVC:
-    def __init__(
-        self,
-        key,
-        pth_path,
-        index_path,
-        index_rate,
-        n_cpu,
-        inp_q,
-        opt_q,
-        config: Config,
-        last_rvc=None,
-    ) -> None:
-        """
-        初始化
-        """
-        try:
-            if config.dml == True:
-
-                def forward_dml(ctx, x, scale):
-                    ctx.scale = scale
-                    res = x.clone().detach()
-                    return res
-
-                fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
-            # global config
-            self.config = config
-            self.inp_q = inp_q
-            self.opt_q = opt_q
-            # device="cpu"########强制cpu测试
-            self.device = config.device
-            self.f0_up_key = key
-            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.n_cpu = n_cpu
-            self.use_jit = self.config.use_jit
-            self.is_half = config.is_half
-
-            if index_rate != 0:
-                self.index = faiss.read_index(index_path)
-                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-                printt("Index search enabled")
-            self.pth_path: str = pth_path
-            self.index_path = index_path
-            self.index_rate = index_rate
-            self.cache_pitch: torch.Tensor = torch.zeros(
-                1024, device=self.device, dtype=torch.long
-            )
-            self.cache_pitchf = torch.zeros(
-                1024, device=self.device, dtype=torch.float32
-            )
-
-            if last_rvc is None:
-                models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
-                    ["assets/hubert/hubert_base.pt"],
-                    suffix="",
-                )
-                hubert_model = models[0]
-                hubert_model = hubert_model.to(self.device)
-                if self.is_half:
-                    hubert_model = hubert_model.half()
-                else:
-                    hubert_model = hubert_model.float()
-                hubert_model.eval()
-                self.model = hubert_model
-            else:
-                self.model = last_rvc.model
-
-            self.net_g: nn.Module = None
-
-            def set_default_model():
-                self.net_g, cpt = get_synthesizer(self.pth_path, self.device)
-                self.tgt_sr = cpt["config"][-1]
-                cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-                self.if_f0 = cpt.get("f0", 1)
-                self.version = cpt.get("version", "v1")
-                if self.is_half:
-                    self.net_g = self.net_g.half()
-                else:
-                    self.net_g = self.net_g.float()
-
-            def set_jit_model():
-                jit_pth_path = self.pth_path.rstrip(".pth")
-                jit_pth_path += ".half.jit" if self.is_half else ".jit"
-                reload = False
-                if str(self.device) == "cuda":
-                    self.device = torch.device("cuda:0")
-                if os.path.exists(jit_pth_path):
-                    cpt = jit.load(jit_pth_path)
-                    model_device = cpt["device"]
-                    if model_device != str(self.device):
-                        reload = True
-                else:
-                    reload = True
-
-                if reload:
-                    cpt = jit.synthesizer_jit_export(
-                        self.pth_path,
-                        "script",
-                        None,
-                        device=self.device,
-                        is_half=self.is_half,
-                    )
-
-                self.tgt_sr = cpt["config"][-1]
-                self.if_f0 = cpt.get("f0", 1)
-                self.version = cpt.get("version", "v1")
-                self.net_g = torch.jit.load(
-                    BytesIO(cpt["model"]), map_location=self.device
-                )
-                self.net_g.infer = self.net_g.forward
-                self.net_g.eval().to(self.device)
-
-            def set_synthesizer():
-                if self.use_jit and not config.dml:
-                    if self.is_half and "cpu" in str(self.device):
-                        printt(
-                            "Use default Synthesizer model. \
-                                    Jit is not supported on the CPU for half floating point"
-                        )
-                        set_default_model()
-                    else:
-                        set_jit_model()
-                else:
-                    set_default_model()
-
-            if last_rvc is None or last_rvc.pth_path != self.pth_path:
-                set_synthesizer()
-            else:
-                self.tgt_sr = last_rvc.tgt_sr
-                self.if_f0 = last_rvc.if_f0
-                self.version = last_rvc.version
-                self.is_half = last_rvc.is_half
-                if last_rvc.use_jit != self.use_jit:
-                    set_synthesizer()
-                else:
-                    self.net_g = last_rvc.net_g
-
-            if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
-                self.model_rmvpe = last_rvc.model_rmvpe
-            if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
-                self.device_fcpe = last_rvc.device_fcpe
-                self.model_fcpe = last_rvc.model_fcpe
-        except:
-            printt(traceback.format_exc())
-
-    def change_key(self, new_key):
-        self.f0_up_key = new_key
-
-    def change_index_rate(self, new_index_rate):
-        if new_index_rate != 0 and self.index_rate == 0:
-            self.index = faiss.read_index(self.index_path)
-            self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-            printt("Index search enabled")
-        self.index_rate = new_index_rate
-
-    def get_f0_post(self, f0):
-        if not torch.is_tensor(f0):
-            f0 = torch.from_numpy(f0)
-        f0 = f0.float().to(self.device).squeeze()
-        f0_mel = 1127 * torch.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
-            self.f0_mel_max - self.f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = torch.round(f0_mel).long()
-        return f0_coarse, f0
-
-    def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
-        n_cpu = int(n_cpu)
-        if method == "crepe":
-            return self.get_f0_crepe(x, f0_up_key)
-        if method == "rmvpe":
-            return self.get_f0_rmvpe(x, f0_up_key)
-        if method == "fcpe":
-            return self.get_f0_fcpe(x, f0_up_key)
-        x = x.cpu().numpy()
-        if method == "pm":
-            p_len = x.shape[0] // 160 + 1
-            f0_min = 65
-            l_pad = int(np.ceil(1.5 / f0_min * 16000))
-            r_pad = l_pad + 1
-            s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), 16000).to_pitch_ac(
-                time_step=0.01,
-                voicing_threshold=0.6,
-                pitch_floor=f0_min,
-                pitch_ceiling=1100,
-            )
-            assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
-            f0 = s.selected_array["frequency"]
-            if len(f0) < p_len:
-                f0 = np.pad(f0, (0, p_len - len(f0)))
-            f0 = f0[:p_len]
-            f0 *= pow(2, f0_up_key / 12)
-            return self.get_f0_post(f0)
-        if n_cpu == 1:
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=16000,
-                f0_ceil=1100,
-                f0_floor=50,
-                frame_period=10,
-            )
-            f0 = signal.medfilt(f0, 3)
-            f0 *= pow(2, f0_up_key / 12)
-            return self.get_f0_post(f0)
-        f0bak = np.zeros(x.shape[0] // 160 + 1, dtype=np.float64)
-        length = len(x)
-        part_length = 160 * ((length // 160 - 1) // n_cpu + 1)
-        n_cpu = (length // 160 - 1) // (part_length // 160) + 1
-        ts = ttime()
-        res_f0 = mm.dict()
-        for idx in range(n_cpu):
-            tail = part_length * (idx + 1) + 320
-            if idx == 0:
-                self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
-            else:
-                self.inp_q.put(
-                    (idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
-                )
-        while 1:
-            res_ts = self.opt_q.get()
-            if res_ts == ts:
-                break
-        f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
-        for idx, f0 in enumerate(f0s):
-            if idx == 0:
-                f0 = f0[:-3]
-            elif idx != n_cpu - 1:
-                f0 = f0[2:-3]
-            else:
-                f0 = f0[2:]
-            f0bak[part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]] = (
-                f0
-            )
-        f0bak = signal.medfilt(f0bak, 3)
-        f0bak *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0bak)
-
-    def get_f0_crepe(self, x, f0_up_key):
-        if "privateuseone" in str(
-            self.device
-        ):  ###不支持dml，cpu又太慢用不成，拿fcpe顶替
-            return self.get_f0(x, f0_up_key, 1, "fcpe")
-        # printt("using crepe,device:%s"%self.device)
-        f0, pd = torchcrepe.predict(
-            x.unsqueeze(0).float(),
-            16000,
-            160,
-            self.f0_min,
-            self.f0_max,
-            "full",
-            batch_size=512,
-            # device=self.device if self.device.type!="privateuseone" else "cpu",###crepe不用半精度全部是全精度所以不愁###cpu延迟高到没法用
-            device=self.device,
-            return_periodicity=True,
-        )
-        pd = torchcrepe.filter.median(pd, 3)
-        f0 = torchcrepe.filter.mean(f0, 3)
-        f0[pd < 0.1] = 0
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
-    def get_f0_rmvpe(self, x, f0_up_key):
-        if hasattr(self, "model_rmvpe") == False:
-            from infer.lib.rmvpe import RMVPE
-
-            printt("Loading rmvpe model")
-            self.model_rmvpe = RMVPE(
-                "assets/rmvpe/rmvpe.pt",
-                is_half=self.is_half,
-                device=self.device,
-                use_jit=self.config.use_jit,
-            )
-        f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
-    def get_f0_fcpe(self, x, f0_up_key):
-        if hasattr(self, "model_fcpe") == False:
-            from torchfcpe import spawn_bundled_infer_model
-
-            printt("Loading fcpe model")
-            if "privateuseone" in str(self.device):
-                self.device_fcpe = "cpu"
-            else:
-                self.device_fcpe = self.device
-            self.model_fcpe = spawn_bundled_infer_model(self.device_fcpe)
-        f0 = self.model_fcpe.infer(
-            x.to(self.device_fcpe).unsqueeze(0).float(),
-            sr=16000,
-            decoder_mode="local_argmax",
-            threshold=0.006,
-        )
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
-    def infer(
-        self,
-        input_wav: torch.Tensor,
-        block_frame_16k,
-        skip_head,
-        return_length,
-        f0method,
-    ) -> np.ndarray:
-        t1 = ttime()
-        with torch.no_grad():
-            if self.config.is_half:
-                feats = input_wav.half().view(1, -1)
-            else:
-                feats = input_wav.float().view(1, -1)
-            padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-            inputs = {
-                "source": feats,
-                "padding_mask": padding_mask,
-                "output_layer": 9 if self.version == "v1" else 12,
-            }
-            logits = self.model.extract_features(**inputs)
-            feats = (
-                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
-            )
-            feats = torch.cat((feats, feats[:, -1:, :]), 1)
-        t2 = ttime()
-        try:
-            if hasattr(self, "index") and self.index_rate != 0:
-                npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
-                score, ix = self.index.search(npy, k=8)
-                if (ix >= 0).all():
-                    weight = np.square(1 / score)
-                    weight /= weight.sum(axis=1, keepdims=True)
-                    npy = np.sum(
-                        self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
-                    )
-                    if self.config.is_half:
-                        npy = npy.astype("float16")
-                    feats[0][skip_head // 2 :] = (
-                        torch.from_numpy(npy).unsqueeze(0).to(self.device)
-                        * self.index_rate
-                        + (1 - self.index_rate) * feats[0][skip_head // 2 :]
-                    )
-                else:
-                    printt(
-                        "Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!"
-                    )
-            else:
-                printt("Index search FAILED or disabled")
-        except:
-            traceback.print_exc()
-            printt("Index search FAILED")
-        t3 = ttime()
-        p_len = input_wav.shape[0] // 160
-        if self.if_f0 == 1:
-            f0_extractor_frame = block_frame_16k + 800
-            if f0method == "rmvpe":
-                f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
-            pitch, pitchf = self.get_f0(
-                input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
-            )
-            shift = block_frame_16k // 160
-            self.cache_pitch[:-shift] = self.cache_pitch[shift:].clone()
-            self.cache_pitchf[:-shift] = self.cache_pitchf[shift:].clone()
-            self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
-            self.cache_pitchf[4 - pitch.shape[0] :] = pitchf[3:-1]
-            cache_pitch = self.cache_pitch[None, -p_len:]
-            cache_pitchf = self.cache_pitchf[None, -p_len:]
-        t4 = ttime()
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        feats = feats[:, :p_len, :]
-        p_len = torch.LongTensor([p_len]).to(self.device)
-        sid = torch.LongTensor([0]).to(self.device)
-        skip_head = torch.LongTensor([skip_head])
-        return_length = torch.LongTensor([return_length])
-        with torch.no_grad():
-            if self.if_f0 == 1:
-                infered_audio, _, _ = self.net_g.infer(
-                    feats,
-                    p_len,
-                    cache_pitch,
-                    cache_pitchf,
-                    sid,
-                    skip_head,
-                    return_length,
-                )
-            else:
-                infered_audio, _, _ = self.net_g.infer(
-                    feats, p_len, sid, skip_head, return_length
-                )
-        t5 = ttime()
-        printt(
-            "Spent time: fea = %.3fs, index = %.3fs, f0 = %.3fs, model = %.3fs",
-            t2 - t1,
-            t3 - t2,
-            t4 - t3,
-            t5 - t4,
-        )
-        return infered_audio.squeeze().float()