From 3c7f1f1407c2acefd08247971ea645d25d8c8d73 Mon Sep 17 00:00:00 2001 From: Ftps <63702646+Tps-F@users.noreply.github.com> Date: Mon, 28 Aug 2023 16:07:41 +0900 Subject: [PATCH] replace files --- MDXNet.py | 285 ------------------ app.py => tools/app.py | 8 +- tools/infer_cli.py | 2 +- .../rvc_for_realtime.py | 27 +- 4 files changed, 24 insertions(+), 298 deletions(-) delete mode 100644 MDXNet.py rename app.py => tools/app.py (99%) rename rvc_for_realtime.py => tools/rvc_for_realtime.py (98%) diff --git a/MDXNet.py b/MDXNet.py deleted file mode 100644 index c519e25..0000000 --- a/MDXNet.py +++ /dev/null @@ -1,285 +0,0 @@ -import soundfile as sf -import torch, pdb, os, warnings, librosa -import numpy as np -from tqdm import tqdm -import torch - -dim_c = 4 - - -class Conv_TDF_net_trim: - def __init__( - self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024 - ): - super(Conv_TDF_net_trim, self).__init__() - - self.dim_f = dim_f - self.dim_t = 2**dim_t - self.n_fft = n_fft - self.hop = hop - self.n_bins = self.n_fft // 2 + 1 - self.chunk_size = hop * (self.dim_t - 1) - self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to( - device - ) - self.target_name = target_name - self.blender = "blender" in model_name - - out_c = dim_c * 4 if target_name == "*" else dim_c - self.freq_pad = torch.zeros( - [1, out_c, self.n_bins - self.dim_f, self.dim_t] - ).to(device) - - self.n = L // 2 - - def stft(self, x): - x = x.reshape([-1, self.chunk_size]) - x = torch.stft( - x, - n_fft=self.n_fft, - hop_length=self.hop, - window=self.window, - center=True, - return_complex=True, - ) - x = torch.view_as_real(x) - x = x.permute([0, 3, 1, 2]) - x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape( - [-1, dim_c, self.n_bins, self.dim_t] - ) - return x[:, :, : self.dim_f] - - def istft(self, x, freq_pad=None): - freq_pad = ( - self.freq_pad.repeat([x.shape[0], 1, 1, 1]) - if freq_pad is None - else freq_pad - ) - x = torch.cat([x, freq_pad], -2) - c = 4 * 2 if self.target_name == "*" else 2 - x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape( - [-1, 2, self.n_bins, self.dim_t] - ) - x = x.permute([0, 2, 3, 1]) - x = x.contiguous() - x = torch.view_as_complex(x) - x = torch.istft( - x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True - ) - return x.reshape([-1, c, self.chunk_size]) - - -def get_models(device, dim_f, dim_t, n_fft): - return Conv_TDF_net_trim( - device=device, - model_name="Conv-TDF", - target_name="vocals", - L=11, - dim_f=dim_f, - dim_t=dim_t, - n_fft=n_fft, - ) - - -warnings.filterwarnings("ignore") -import sys - -now_dir = os.getcwd() -sys.path.append(now_dir) -from config import Config - -cpu = torch.device("cpu") -device = Config().device -# if torch.cuda.is_available(): -# device = torch.device("cuda:0") -# elif torch.backends.mps.is_available(): -# device = torch.device("mps") -# else: -# device = torch.device("cpu") - - -class Predictor: - def __init__(self, args): - self.args = args - self.model_ = get_models( - device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft - ) - import onnxruntime as ort - - print(ort.get_available_providers()) - self.model = ort.InferenceSession( - os.path.join(args.onnx, self.model_.target_name + ".onnx"), - providers=[ - "CUDAExecutionProvider", - "DmlExecutionProvider", - "CPUExecutionProvider", - ], - ) - print("onnx load done") - - def demix(self, mix): - samples = mix.shape[-1] - margin = self.args.margin - chunk_size = self.args.chunks * 44100 - assert not margin == 0, "margin cannot be zero!" - if margin > chunk_size: - margin = chunk_size - - segmented_mix = {} - - if self.args.chunks == 0 or samples < chunk_size: - chunk_size = samples - - counter = -1 - for skip in range(0, samples, chunk_size): - counter += 1 - - s_margin = 0 if counter == 0 else margin - end = min(skip + chunk_size + margin, samples) - - start = skip - s_margin - - segmented_mix[skip] = mix[:, start:end].copy() - if end == samples: - break - - sources = self.demix_base(segmented_mix, margin_size=margin) - """ - mix:(2,big_sample) - segmented_mix:offset->(2,small_sample) - sources:(1,2,big_sample) - """ - return sources - - def demix_base(self, mixes, margin_size): - chunked_sources = [] - progress_bar = tqdm(total=len(mixes)) - progress_bar.set_description("Processing") - for mix in mixes: - cmix = mixes[mix] - sources = [] - n_sample = cmix.shape[1] - model = self.model_ - trim = model.n_fft // 2 - gen_size = model.chunk_size - 2 * trim - pad = gen_size - n_sample % gen_size - mix_p = np.concatenate( - (np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1 - ) - mix_waves = [] - i = 0 - while i < n_sample + pad: - waves = np.array(mix_p[:, i : i + model.chunk_size]) - mix_waves.append(waves) - i += gen_size - mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu) - with torch.no_grad(): - _ort = self.model - spek = model.stft(mix_waves) - if self.args.denoise: - spec_pred = ( - -_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5 - + _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5 - ) - tar_waves = model.istft(torch.tensor(spec_pred)) - else: - tar_waves = model.istft( - torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0]) - ) - tar_signal = ( - tar_waves[:, :, trim:-trim] - .transpose(0, 1) - .reshape(2, -1) - .numpy()[:, :-pad] - ) - - start = 0 if mix == 0 else margin_size - end = None if mix == list(mixes.keys())[::-1][0] else -margin_size - if margin_size == 0: - end = None - sources.append(tar_signal[:, start:end]) - - progress_bar.update(1) - - chunked_sources.append(sources) - _sources = np.concatenate(chunked_sources, axis=-1) - # del self.model - progress_bar.close() - return _sources - - def prediction(self, m, vocal_root, others_root, format): - os.makedirs(vocal_root, exist_ok=True) - os.makedirs(others_root, exist_ok=True) - basename = os.path.basename(m) - mix, rate = librosa.load(m, mono=False, sr=44100) - if mix.ndim == 1: - mix = np.asfortranarray([mix, mix]) - mix = mix.T - sources = self.demix(mix.T) - opt = sources[0].T - if format in ["wav", "flac"]: - sf.write( - "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate - ) - sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate) - else: - path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename) - path_other = "%s/%s_others.wav" % (others_root, basename) - sf.write(path_vocal, mix - opt, rate) - sf.write(path_other, opt, rate) - if os.path.exists(path_vocal): - os.system( - "ffmpeg -i %s -vn %s -q:a 2 -y" - % (path_vocal, path_vocal[:-4] + ".%s" % format) - ) - if os.path.exists(path_other): - os.system( - "ffmpeg -i %s -vn %s -q:a 2 -y" - % (path_other, path_other[:-4] + ".%s" % format) - ) - - -class MDXNetDereverb: - def __init__(self, chunks): - self.onnx = "uvr5_weights/onnx_dereverb_By_FoxJoy" - self.shifts = 10 #'Predict with randomised equivariant stabilisation' - self.mixing = "min_mag" # ['default','min_mag','max_mag'] - self.chunks = chunks - self.margin = 44100 - self.dim_t = 9 - self.dim_f = 3072 - self.n_fft = 6144 - self.denoise = True - self.pred = Predictor(self) - - def _path_audio_(self, input, vocal_root, others_root, format): - self.pred.prediction(input, vocal_root, others_root, format) - - -if __name__ == "__main__": - dereverb = MDXNetDereverb(15) - from time import time as ttime - - t0 = ttime() - dereverb._path_audio_( - "雪雪伴奏对消HP5.wav", - "vocal", - "others", - ) - t1 = ttime() - print(t1 - t0) - - -""" - -runtime\python.exe MDXNet.py - -6G: -15/9:0.8G->6.8G -14:0.8G->6.5G -25:炸 - -half15:0.7G->6.6G,22.69s -fp32-15:0.7G->6.6G,20.85s - -""" diff --git a/app.py b/tools/app.py similarity index 99% rename from app.py rename to tools/app.py index e4a6415..76a9a83 100644 --- a/app.py +++ b/tools/app.py @@ -1,14 +1,12 @@ +import logging import os # os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt") import gradio as gr -import logging - -from configs.config import Config - -from i18n.i18n import I18nAuto from dotenv import load_dotenv +from configs.config import Config +from i18n.i18n import I18nAuto from infer.modules.vc.modules import VC logging.getLogger("numba").setLevel(logging.WARNING) diff --git a/tools/infer_cli.py b/tools/infer_cli.py index c885a07..bbe0a53 100644 --- a/tools/infer_cli.py +++ b/tools/infer_cli.py @@ -4,11 +4,11 @@ import sys now_dir = os.getcwd() sys.path.append(now_dir) +from dotenv import load_dotenv from scipy.io import wavfile from configs.config import Config from infer.modules.vc.modules import VC -from dotenv import load_dotenv #### # USAGE diff --git a/rvc_for_realtime.py b/tools/rvc_for_realtime.py similarity index 98% rename from rvc_for_realtime.py rename to tools/rvc_for_realtime.py index 548472d..32316c3 100644 --- a/rvc_for_realtime.py +++ b/tools/rvc_for_realtime.py @@ -1,21 +1,34 @@ -import os, sys -import faiss, torch, traceback, parselmouth, numpy as np, torchcrepe, torch.nn as nn, pyworld +import os +import sys +import traceback +from time import time as ttime + import fairseq -from lib.infer_pack.models import ( +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, ) -from time import time as ttime -import torch.nn.functional as F -import scipy.signal as signal now_dir = os.getcwd() sys.path.append(now_dir) -from config import defaultconfig as config from multiprocessing import Manager as M +from configs.config import Config + +Config() + mm = M() if config.dml == True: