diff --git a/gui.py b/gui.py index 0dfb15b..6693d24 100644 --- a/gui.py +++ b/gui.py @@ -645,15 +645,15 @@ if __name__ == "__main__": elif event == "pitch": self.gui_config.pitch = values["pitch"] if hasattr(self, "rvc"): - self.rvc.change_key(values["pitch"]) + self.rvc.set_key(values["pitch"]) elif event == "formant": self.gui_config.formant = values["formant"] if hasattr(self, "rvc"): - self.rvc.change_formant(values["formant"]) + self.rvc.set_formant(values["formant"]) elif event == "index_rate": self.gui_config.index_rate = values["index_rate"] if hasattr(self, "rvc"): - self.rvc.change_index_rate(values["index_rate"]) + self.rvc.set_index_rate(values["index_rate"]) elif event == "rms_mix_rate": self.gui_config.rms_mix_rate = values["rms_mix_rate"] elif event in ["pm", "harvest", "crepe", "rmvpe", "fcpe"]: @@ -738,10 +738,10 @@ if __name__ == "__main__": self.gui_config.index_path, self.gui_config.index_rate, self.gui_config.n_cpu, - inp_q, - opt_q, - self.config, - self.rvc if hasattr(self, "rvc") else None, + self.config.device, + self.config.use_jit, + self.config.is_half, + self.config.dml, ) self.gui_config.samplerate = ( self.rvc.tgt_sr diff --git a/infer/lib/rtrvc.py b/infer/lib/rtrvc.py index acd2773..b30c496 100644 --- a/infer/lib/rtrvc.py +++ b/infer/lib/rtrvc.py @@ -1,353 +1,178 @@ from io import BytesIO import os -import sys -import traceback -from infer.lib import jit -from time import time as ttime +from typing import Union, Literal, Optional + 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 from torchaudio.transforms import Resample +from rvc.f0 import PM, Harvest, RMVPE, CRePE, Dio, FCPE from rvc.synthesizer import load_synthesizer -now_dir = os.getcwd() -sys.path.append(now_dir) -from multiprocessing import Manager as M -from configs 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, - formant, - pth_path, - index_path, - index_rate, - n_cpu, - inp_q, - opt_q, - config: Config, - last_rvc=None, + key: Union[int, float], + formant: Union[int, float], + pth_path: torch.serialization.FILE_LIKE, + index_path: str, + index_rate: Union[int, float], + n_cpu: int = os.cpu_count(), + device: str = "cpu", + use_jit: bool = False, + is_half: bool = False, + is_dml: bool = False, ) -> None: - """ - 初始化 - """ - try: - if config.dml == True: + if is_dml: - def forward_dml(ctx, x, scale): - ctx.scale = scale - res = x.clone().detach() - return res + 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.formant_shift = formant - 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 + fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml - 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 - ) + self.device = device + self.f0_up_key = key + self.formant_shift = formant + self.sr = 16000 # hubert sampling rate + self.window = 160 # hop length + 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 = use_jit + self.is_half = is_half - self.resample_kernel = {} + if index_rate > 0: + self.index = faiss.read_index(index_path) + self.big_npy = self.index.reconstruct_n(0, self.index.ntotal) - 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 + self.pth_path = 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 + ) + + self.resample_kernel = {} + + self.f0_methods = { + "crepe": self._get_f0_crepe, + "rmvpe": self._get_f0_rmvpe, + "fcpe": self._get_f0_fcpe, + "pm": self._get_f0_pm, + "harvest": self._get_f0_harvest, + "dio": self._get_f0_dio, + } + + 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.hubert = hubert_model + + self.net_g: Optional[nn.Module] = None + + def set_default_model(): + self.net_g, cpt = load_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.model = last_rvc.model + self.net_g = self.net_g.float() - self.net_g: nn.Module = None + def set_jit_model(): + from rvc.jit import get_jit_model + from rvc.synthesizer import synthesizer_jit_export - def set_default_model(): - self.net_g, cpt = load_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() + cpt = get_jit_model(self.pth_path, self.is_half, synthesizer_jit_export) - def set_jit_model(): - from rvc.jit import get_jit_model - from rvc.synthesizer import synthesizer_jit_export + 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) - cpt = get_jit_model(self.pth_path, self.is_half, synthesizer_jit_export) + if self.use_jit and not is_dml and not (self.is_half and "cpu" in str(self.device)): + set_jit_model() + else: + set_default_model() - 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): + def set_key(self, new_key): self.f0_up_key = new_key - def change_formant(self, new_formant): + def set_formant(self, new_formant): self.formant_shift = new_formant - def change_index_rate(self, new_index_rate): - if new_index_rate != 0 and self.index_rate == 0: + def set_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") - if hasattr(self, "model_crepe") == False: - from rvc.f0 import CRePE - - self.model_crepe = CRePE( - 160, - self.f0_min, - self.f0_max, - 16000, - self.device, - ) - f0 = self.model_crepe.compute_f0(x) - 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 rvc.f0 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.compute_f0(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 rvc.f0 import FCPE - - printt("Loading fcpe model") - self.model_fcpe = FCPE( - 160, - self.f0_min, - self.f0_max, - 16000, - self.device, - ) - f0 = self.model_fcpe.compute_f0(x) - 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, + block_frame_16k: int, + skip_head: torch.Tensor, + return_length: int, + f0method: Union[tuple, str], + inp_f0: Optional[np.ndarray] = None, + protect: float = 1.0, ) -> np.ndarray: - t1 = ttime() with torch.no_grad(): - if self.config.is_half: - feats = input_wav.half().view(1, -1) + if self.is_half: + feats = input_wav.half() else: - feats = input_wav.float().view(1, -1) + feats = input_wav.float() + feats = feats.to(self.device) + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + feats = feats.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) + logits = self.hubert.extract_features(**inputs) feats = ( - self.model.final_proj(logits[0]) if self.version == "v1" else logits[0] + self.hubert.final_proj(logits[0]) if self.version == "v1" else logits[0] ) feats = torch.cat((feats, feats[:, -1:, :]), 1) - t2 = ttime() + if protect < 0.5 and self.if_f0 == 1: + feats0 = feats.clone() + try: - if hasattr(self, "index") and self.index_rate != 0: - npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32") + if hasattr(self, "index") and self.index_rate > 0: + npy = feats[0][skip_head // 2 :].cpu().numpy() + if self.is_half: + npy = npy.astype("float32") score, ix = self.index.search(npy, k=8) if (ix >= 0).all(): weight = np.square(1 / score) @@ -355,38 +180,40 @@ class RVC: npy = np.sum( self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1 ) - if self.config.is_half: + if self.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 + pass + + p_len = input_wav.shape[0] // self.window factor = pow(2, self.formant_shift / 12) return_length2 = int(np.ceil(return_length * factor)) cache_pitch = cache_pitchf = None - if self.if_f0 == 1: + pitch = pitchf = None + if isinstance(f0method, tuple): + pitch, pitchf = f0method + pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() + pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float() + elif 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.formant_shift, - self.n_cpu, - f0method, - ) - shift = block_frame_16k // 160 + f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - self.window + if inp_f0 is not None: + pitch, pitchf = self._get_f0_post(inp_f0, self.f0_up_key - self.formant_shift) + else: + pitch, pitchf = self._get_f0( + input_wav[-f0_extractor_frame:], + self.f0_up_key - self.formant_shift, + self.n_cpu, + 3, + f0method, + ) + shift = block_frame_16k // self.window 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] @@ -395,13 +222,23 @@ class RVC: cache_pitchf = ( self.cache_pitchf[None, -p_len:] * return_length2 / return_length ) - t4 = ttime() + feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) feats = feats[:, :p_len, :] + if protect < 0.5 and pitch is not None and pitchf is not None: + feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute( + 0, 2, 1 + ) + feats0 = feats0[:, :p_len, :] + pitchff = pitchf.clone() + pitchff[pitchf > 0] = 1 + pitchff[pitchf < 1] = protect + pitchff = pitchff.unsqueeze(-1) + feats = feats * pitchff + feats0 * (1 - pitchff) + feats = feats.to(feats0.dtype) p_len = torch.LongTensor([p_len]).to(self.device) sid = torch.LongTensor([0]).to(self.device) skip_head = torch.LongTensor([skip_head]) - # return_length2 = torch.LongTensor([return_length2]) return_length = torch.LongTensor([return_length]) with torch.no_grad(): infered_audio = ( @@ -428,12 +265,93 @@ class RVC: infered_audio = self.resample_kernel[upp_res]( infered_audio[:, : return_length * upp_res] ) - 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() + + def _get_f0( + self, + x: torch.Tensor, + f0_up_key: Union[int, float], + filter_radius: Union[int, float], + method: Literal["crepe", "rmvpe", "fcpe", "pm", "harvest", "dio"]="fcpe", + ): + if method not in self.f0_methods.keys(): + raise RuntimeError("Not supported f0 method: "+method) + return self.f0_methods[method](x, f0_up_key, filter_radius) + + def _get_f0_post(self, f0, f0_up_key): + f0 *= pow(2, f0_up_key / 12) + 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_pm(self, x, f0_up_key, filter_radius): + if not hasattr(self, "pm"): + self.pm = PM(hop_length=160, sampling_rate=16000) + f0 = self.pm.compute_f0(x) + return self._get_f0_post(f0, f0_up_key) + + def _get_f0_harvest(self, x, f0_up_key, filter_radius): + if not hasattr(self, "harvest"): + self.harvest = Harvest( + self.window, + self.f0_min, + self.f0_max, + self.sr, + ) + f0 = self.harvest.compute_f0(x, filter_radius=filter_radius) + return self._get_f0_post(f0, f0_up_key) + + def _get_f0_dio(self, x, f0_up_key, filter_radius): + if not hasattr(self, "dio"): + self.dio = Dio( + self.window, + self.f0_min, + self.f0_max, + self.sr, + ) + f0 = self.dio.compute_f0(x) + return self._get_f0_post(f0, f0_up_key) + + def _get_f0_crepe(self, x, f0_up_key, filter_radius): + if hasattr(self, "crepe") == False: + self.crepe = CRePE( + self.window, + self.f0_min, + self.f0_max, + self.sr, + self.device, + ) + f0 = self.crepe.compute_f0(x) + return self._get_f0_post(f0, f0_up_key) + + def _get_f0_rmvpe(self, x, f0_up_key, filter_radius=0.03): + if hasattr(self, "rmvpe") == False: + self.rmvpe = RMVPE( + "%s/rmvpe.pt" % os.environ["rmvpe_root"], + is_half=self.is_half, + device=self.device, + use_jit=self.use_jit, + ) + return self._get_f0_post( + self.rmvpe.compute_f0(x, thred=filter_radius), f0_up_key, + ) + + def _get_f0_fcpe(self, x, f0_up_key, filter_radius): + if hasattr(self, "fcpe") == False: + self.fcpe = FCPE( + 160, + self.f0_min, + self.f0_max, + 16000, + self.device, + ) + f0 = self.fcpe.compute_f0(x) + return self._get_f0_post(f0, f0_up_key) diff --git a/rvc/f0/harvest.py b/rvc/f0/harvest.py index 6635cbc..823c564 100644 --- a/rvc/f0/harvest.py +++ b/rvc/f0/harvest.py @@ -28,5 +28,5 @@ class Harvest(F0Predictor): ) f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) if filter_radius is not None and filter_radius > 2: - f0 = signal.medfilt(f0, 3) + f0 = signal.medfilt(f0, filter_radius) return self._interpolate_f0(self._resize_f0(f0, p_len))[0]