Add files via upload

MDXNet.py

@@ -0,0 +1,198 @@
+import soundfile as sf
+import torch,pdb,time,argparse,os,warnings,sys,librosa
+import numpy as np
+import onnxruntime as ort
+from scipy.io.wavfile import write
+from tqdm import tqdm
+import torch
+import torch.nn as nn
+
+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")
+cpu = torch.device('cpu')
+device = torch.device('cuda:0' if torch.cuda.is_available() else '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)
+        self.model = ort.InferenceSession(os.path.join(args.onnx,self.model_.target_name+'.onnx'), providers=['CUDAExecutionProvider', '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):
+        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
+        sf.write("%s/%s_main_vocal.wav"%(vocal_root,basename), mix-opt, rate)
+        sf.write("%s/%s_others.wav"%(others_root,basename), opt , rate)
+
+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):
+        self.pred.prediction(input,vocal_root,others_root)
+
+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
+
+'''

infer-web.py

@@ -21,6 +21,7 @@ warnings.filterwarnings("ignore")
 torch.manual_seed(114514)
 from i18n import I18nAuto
 import ffmpeg
+from MDXNet import MDXNetDereverb
 
 i18n = I18nAuto()
 i18n.print()
@@ -82,7 +83,7 @@ import gradio as gr
 import logging
 from vc_infer_pipeline import VC
 from config import Config
-from infer_uvr5 import _audio_pre_
+from infer_uvr5 import _audio_pre_,_audio_pre_new
 from my_utils import load_audio
 from train.process_ckpt import show_info, change_info, merge, extract_small_model
 
@@ -133,7 +134,7 @@ for root, dirs, files in os.walk(index_root, topdown=False):
             index_paths.append("%s/%s" % (root, name))
 uvr5_names = []
 for name in os.listdir(weight_uvr5_root):
-    if name.endswith(".pth"):
+    if name.endswith(".pth")or "onnx"in name:
         uvr5_names.append(name.replace(".pth", ""))
 
 
@@ -150,6 +151,7 @@ def vc_single(
     filter_radius,
     resample_sr,
     rms_mix_rate,
+    protect
 ):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
     global tgt_sr, net_g, vc, hubert_model, version
     if input_audio_path is None:
@@ -197,6 +199,7 @@ def vc_single(
             resample_sr,
             rms_mix_rate,
             version,
+            protect,
             f0_file=f0_file,
         )
         if resample_sr >= 16000 and tgt_sr != resample_sr:
@@ -232,6 +235,7 @@ def vc_multi(
     filter_radius,
     resample_sr,
     rms_mix_rate,
+    protect
 ):
     try:
         dir_path = (
@@ -262,6 +266,7 @@ def vc_multi(
                 filter_radius,
                 resample_sr,
                 rms_mix_rate,
+                protect
             )
             if "Success" in info:
                 try:
@@ -288,12 +293,16 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
         save_root_ins = (
             save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
         )
-        pre_fun = _audio_pre_(
-            agg=int(agg),
-            model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
-            device=config.device,
-            is_half=config.is_half,
-        )
+        if(model_name=="onnx_dereverb_By_FoxJoy"):
+            pre_fun=MDXNetDereverb(15)
+        else:
+            func=_audio_pre_ if "DeEcho"not in model_name else  _audio_pre_new
+            pre_fun = func(
+                agg=int(agg),
+                model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
+                device=config.device,
+                is_half=config.is_half,
+            )
         if inp_root != "":
             paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
         else:
@@ -336,8 +345,12 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
         yield "\n".join(infos)
     finally:
         try:
-            del pre_fun.model
-            del pre_fun
+            if (model_name == "onnx_dereverb_By_FoxJoy"):
+                del pre_fun.pred.model
+                del pre_fun.pred.model_
+            else:
+                del pre_fun.model
+                del pre_fun
         except:
             traceback.print_exc()
         print("clean_empty_cache")
@@ -790,7 +803,7 @@ def train_index(exp_dir1, version19):
     faiss.write_index(
         index,
         "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+        % (exp_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
     )
     # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
     infos.append("adding")
@@ -801,11 +814,11 @@ def train_index(exp_dir1, version19):
     faiss.write_index(
         index,
         "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+        % (exp_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
     )
     infos.append(
         "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
+        % (n_ivf, index_ivf.nprobe,exp_dir1, version19)
     )
     # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
     # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
@@ -1030,7 +1043,7 @@ def train1key(
     faiss.write_index(
         index,
         "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+        % (model_log_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
     )
     yield get_info_str("adding index")
     batch_size_add = 8192
@@ -1039,11 +1052,11 @@ def train1key(
     faiss.write_index(
         index,
         "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+        % (model_log_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
     )
     yield get_info_str(
         "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
+        % (n_ivf, index_ivf.nprobe, exp_dir1,version19)
     )
     yield get_info_str(i18n("全流程结束！"))
 
@@ -1161,8 +1174,8 @@ with gr.Blocks() as app:
                             value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav",
                         )
                         f0method0 = gr.Radio(
-                            label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"),
-                            choices=["pm", "harvest"],
+                            label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
+                            choices=["pm", "harvest","crepe"],
                             value="pm",
                             interactive=True,
                         )
@@ -1197,9 +1210,10 @@ with gr.Blocks() as app:
                             minimum=0,
                             maximum=1,
                             label=i18n("检索特征占比"),
-                            value=0.76,
+                            value=0.88,
                             interactive=True,
                         )
+                    with gr.Column():
                         resample_sr0 = gr.Slider(
                             minimum=0,
                             maximum=48000,
@@ -1215,9 +1229,17 @@ with gr.Blocks() as app:
                             value=1,
                             interactive=True,
                         )
+                        protect0 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                        )
                     f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
                     but0 = gr.Button(i18n("转换"), variant="primary")
-                    with gr.Column():
+                    with gr.Row():
                         vc_output1 = gr.Textbox(label=i18n("输出信息"))
                         vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
                     but0.click(
@@ -1235,6 +1257,7 @@ with gr.Blocks() as app:
                             filter_radius0,
                             resample_sr0,
                             rms_mix_rate0,
+                            protect0
                         ],
                         [vc_output1, vc_output2],
                     )
@@ -1249,8 +1272,8 @@ with gr.Blocks() as app:
                         )
                         opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
                         f0method1 = gr.Radio(
-                            label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"),
-                            choices=["pm", "harvest"],
+                            label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
+                            choices=["pm", "harvest","crepe"],
                             value="pm",
                             interactive=True,
                         )
@@ -1285,6 +1308,7 @@ with gr.Blocks() as app:
                             value=1,
                             interactive=True,
                         )
+                    with gr.Column():
                         resample_sr1 = gr.Slider(
                             minimum=0,
                             maximum=48000,
@@ -1300,6 +1324,14 @@ with gr.Blocks() as app:
                             value=1,
                             interactive=True,
                         )
+                        protect1 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                        )
                     with gr.Column():
                         dir_input = gr.Textbox(
                             label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
@@ -1308,8 +1340,9 @@ with gr.Blocks() as app:
                         inputs = gr.File(
                             file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
                         )
-                    but1 = gr.Button(i18n("转换"), variant="primary")
-                    vc_output3 = gr.Textbox(label=i18n("输出信息"))
+                    with gr.Row():
+                        but1 = gr.Button(i18n("转换"), variant="primary")
+                        vc_output3 = gr.Textbox(label=i18n("输出信息"))
                     but1.click(
                         vc_multi,
                         [
@@ -1326,14 +1359,26 @@ with gr.Blocks() as app:
                             filter_radius1,
                             resample_sr1,
                             rms_mix_rate1,
+                            protect1
                         ],
                         [vc_output3],
                     )
-        with gr.TabItem(i18n("伴奏人声分离")):
+        with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
             with gr.Group():
                 gr.Markdown(
                     value=i18n(
-                        "人声伴奏分离批量处理, 使用UVR5模型. <br>不带和声用HP2, 带和声且提取的人声不需要和声用HP5<br>合格的文件夹路径格式举例: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)"
+                        "人声伴奏分离批量处理， 使用UVR5模型。 <br>"
+                        "合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>"
+                        "模型分为三类： <br>"
+                        "1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>"
+                        "2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> "
+                        "3、去混响、去延迟模型（by FoxJoy）：<br>"
+                        "  (1)MDX-Net:对于双通道混响是最好的选择，不能去除单通道混响；<br>"
+                        "        (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>"
+                        "去混响/去延迟，附：<br>"
+                        "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>"
+                        "2、MDX-Net-Dereverb模型挺慢的；<br>"
+                        "3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"
                     )
                 )
                 with gr.Row():
@@ -1384,7 +1429,7 @@ with gr.Blocks() as app:
                 exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test")
                 sr2 = gr.Radio(
                     label=i18n("目标采样率"),
-                    choices=["32k", "40k", "48k"],
+                    choices=["40k", "48k"],
                     value="40k",
                     interactive=True,
                 )

infer_uvr5.py

@@ -1,5 +1,7 @@
 import os, sys, torch, warnings, pdb
-
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from json import load as ll
 warnings.filterwarnings("ignore")
 import librosa
 import importlib
@@ -10,7 +12,8 @@ from uvr5_pack.lib_v5 import spec_utils
 from uvr5_pack.utils import _get_name_params, inference
 from uvr5_pack.lib_v5.model_param_init import ModelParameters
 from scipy.io import wavfile
-
+from uvr5_pack.lib_v5.nets_new import CascadedNet
+from uvr5_pack.lib_v5 import nets_61968KB as nets
 
 class _audio_pre_:
     def __init__(self, agg, model_path, device, is_half):
@@ -25,28 +28,7 @@ class _audio_pre_:
             "agg": agg,
             "high_end_process": "mirroring",
         }
-        nn_arch_sizes = [
-            31191,  # default
-            33966,
-            61968,
-            123821,
-            123812,
-            537238,  # custom
-        ]
-        self.nn_architecture = list("{}KB".format(s) for s in nn_arch_sizes)
-        model_size = math.ceil(os.stat(model_path).st_size / 1024)
-        nn_architecture = "{}KB".format(
-            min(nn_arch_sizes, key=lambda x: abs(x - model_size))
-        )
-        nets = importlib.import_module(
-            "uvr5_pack.lib_v5.nets"
-            + f"_{nn_architecture}".replace("_{}KB".format(nn_arch_sizes[0]), ""),
-            package=None,
-        )
-        model_hash = hashlib.md5(open(model_path, "rb").read()).hexdigest()
-        param_name, model_params_d = _get_name_params(model_path, model_hash)
-
-        mp = ModelParameters(model_params_d)
+        mp = ModelParameters("uvr5_pack/lib_v5/modelparams/4band_v2.json")
         model = nets.CascadedASPPNet(mp.param["bins"] * 2)
         cpk = torch.load(model_path, map_location="cpu")
         model.load_state_dict(cpk)
@@ -164,12 +146,148 @@ class _audio_pre_:
                 (np.array(wav_vocals) * 32768).astype("int16"),
             )
 
+class _audio_pre_new:
+    def __init__(self, agg, model_path, device, is_half):
+        self.model_path = model_path
+        self.device = device
+        self.data = {
+            # Processing Options
+            "postprocess": False,
+            "tta": False,
+            # Constants
+            "window_size": 512,
+            "agg": agg,
+            "high_end_process": "mirroring",
+        }
+        mp=ModelParameters("uvr5_pack/lib_v5/modelparams/4band_v3.json")
+        nout=64 if "DeReverb"in model_path else 48
+        model = CascadedNet(mp.param["bins"] * 2,nout)
+        cpk = torch.load(model_path, map_location="cpu")
+        model.load_state_dict(cpk)
+        model.eval()
+        if is_half:
+            model = model.half().to(device)
+        else:
+            model = model.to(device)
+
+        self.mp = mp
+        self.model = model
+
+    def _path_audio_(self, music_file, vocal_root=None, ins_root=None):#3个VR模型vocal和ins是反的
+        if ins_root is None and vocal_root is None:
+            return "No save root."
+        name = os.path.basename(music_file)
+        if ins_root is not None:
+            os.makedirs(ins_root, exist_ok=True)
+        if vocal_root is not None:
+            os.makedirs(vocal_root, exist_ok=True)
+        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
+        bands_n = len(self.mp.param["band"])
+        # print(bands_n)
+        for d in range(bands_n, 0, -1):
+            bp = self.mp.param["band"][d]
+            if d == bands_n:  # high-end band
+                (
+                    X_wave[d],
+                    _,
+                ) = librosa.core.load(  # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
+                    music_file,
+                    bp["sr"],
+                    False,
+                    dtype=np.float32,
+                    res_type=bp["res_type"],
+                )
+                if X_wave[d].ndim == 1:
+                    X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
+            else:  # lower bands
+                X_wave[d] = librosa.core.resample(
+                    X_wave[d + 1],
+                    self.mp.param["band"][d + 1]["sr"],
+                    bp["sr"],
+                    res_type=bp["res_type"],
+                )
+            # Stft of wave source
+            X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
+                X_wave[d],
+                bp["hl"],
+                bp["n_fft"],
+                self.mp.param["mid_side"],
+                self.mp.param["mid_side_b2"],
+                self.mp.param["reverse"],
+            )
+            # pdb.set_trace()
+            if d == bands_n and self.data["high_end_process"] != "none":
+                input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
+                    self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
+                )
+                input_high_end = X_spec_s[d][
+                    :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
+                ]
+
+        X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
+        aggresive_set = float(self.data["agg"] / 100)
+        aggressiveness = {
+            "value": aggresive_set,
+            "split_bin": self.mp.param["band"][1]["crop_stop"],
+        }
+        with torch.no_grad():
+            pred, X_mag, X_phase = inference(
+                X_spec_m, self.device, self.model, aggressiveness, self.data
+            )
+        # Postprocess
+        if self.data["postprocess"]:
+            pred_inv = np.clip(X_mag - pred, 0, np.inf)
+            pred = spec_utils.mask_silence(pred, pred_inv)
+        y_spec_m = pred * X_phase
+        v_spec_m = X_spec_m - y_spec_m
+
+        if ins_root is not None:
+            if self.data["high_end_process"].startswith("mirroring"):
+                input_high_end_ = spec_utils.mirroring(
+                    self.data["high_end_process"], y_spec_m, input_high_end, self.mp
+                )
+                wav_instrument = spec_utils.cmb_spectrogram_to_wave(
+                    y_spec_m, self.mp, input_high_end_h, input_high_end_
+                )
+            else:
+                wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
+            print("%s instruments done" % name)
+            wavfile.write(
+                os.path.join(
+                    ins_root, "main_vocal_{}_{}.wav".format(name, self.data["agg"])
+                ),
+                self.mp.param["sr"],
+                (np.array(wav_instrument) * 32768).astype("int16"),
+            )  #
+        if vocal_root is not None:
+            if self.data["high_end_process"].startswith("mirroring"):
+                input_high_end_ = spec_utils.mirroring(
+                    self.data["high_end_process"], v_spec_m, input_high_end, self.mp
+                )
+                wav_vocals = spec_utils.cmb_spectrogram_to_wave(
+                    v_spec_m, self.mp, input_high_end_h, input_high_end_
+                )
+            else:
+                wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
+            print("%s vocals done" % name)
+            wavfile.write(
+                os.path.join(
+                    vocal_root, "others_{}_{}.wav".format(name, self.data["agg"])
+                ),
+                self.mp.param["sr"],
+                (np.array(wav_vocals) * 32768).astype("int16"),
+            )
+
 
 if __name__ == "__main__":
     device = "cuda"
     is_half = True
-    model_path = "uvr5_weights/2_HP-UVR.pth"
-    pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True)
-    audio_path = "神女劈观.aac"
+    # model_path = "uvr5_weights/2_HP-UVR.pth"
+    # model_path = "uvr5_weights/VR-DeEchoDeReverb.pth"
+    # model_path = "uvr5_weights/VR-DeEchoNormal.pth"
+    model_path = "uvr5_weights/DeEchoNormal.pth"
+    # pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True,agg=10)
+    pre_fun = _audio_pre_new(model_path=model_path, device=device, is_half=True,agg=10)
+    audio_path = "雪雪伴奏对消HP5.wav"
     save_path = "opt"
     pre_fun._path_audio_(audio_path, save_path, save_path)

vc_infer_pipeline.py

@@ -2,7 +2,7 @@ import numpy as np, parselmouth, torch, pdb
 from time import time as ttime
 import torch.nn.functional as F
 import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa
+import pyworld, os, traceback, faiss, librosa,torchcrepe
 from scipy import signal
 from functools import lru_cache
 
@@ -103,6 +103,27 @@ class VC(object):
             f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
             if filter_radius > 2:
                 f0 = signal.medfilt(f0, 3)
+        elif f0_method == "crepe":
+            model = "full"
+            # Pick a batch size that doesn't cause memory errors on your gpu
+            batch_size = 512
+            # Compute pitch using first gpu
+            audio = torch.tensor(np.copy(x))[None].float()
+            f0, pd = torchcrepe.predict(
+                audio,
+                self.sr,
+                self.window,
+                f0_min,
+                f0_max,
+                model,
+                batch_size=batch_size,
+                device=self.device,
+                return_periodicity=True,
+            )
+            pd = torchcrepe.filter.median(pd, 3)
+            f0 = torchcrepe.filter.mean(f0, 3)
+            f0[pd < 0.1] = 0
+            f0 = f0[0].cpu().numpy()
         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
@@ -141,6 +162,7 @@ class VC(object):
         big_npy,
         index_rate,
         version,
+        protect
     ):  # ,file_index,file_big_npy
         feats = torch.from_numpy(audio0)
         if self.is_half:
@@ -162,7 +184,8 @@ class VC(object):
         with torch.no_grad():
             logits = model.extract_features(**inputs)
             feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-
+        if(protect<0.5):
+            feats0=feats.clone()
         if (
             isinstance(index, type(None)) == False
             and isinstance(big_npy, type(None)) == False
@@ -188,6 +211,8 @@ class VC(object):
             )
 
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+        if(protect<0.5):
+            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
         t1 = ttime()
         p_len = audio0.shape[0] // self.window
         if feats.shape[1] < p_len:
@@ -195,6 +220,14 @@ class VC(object):
             if pitch != None and pitchf != None:
                 pitch = pitch[:, :p_len]
                 pitchf = pitchf[:, :p_len]
+
+        if(protect<0.5):
+            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.tensor([p_len], device=self.device).long()
         with torch.no_grad():
             if pitch != None and pitchf != None:
@@ -235,6 +268,7 @@ class VC(object):
         resample_sr,
         rms_mix_rate,
         version,
+        protect,
         f0_file=None,
     ):
         if (
@@ -322,6 +356,7 @@ class VC(object):
                         big_npy,
                         index_rate,
                         version,
+                        protect
                     )[self.t_pad_tgt : -self.t_pad_tgt]
                 )
             else:
@@ -338,6 +373,7 @@ class VC(object):
                         big_npy,
                         index_rate,
                         version,
+                        protect
                     )[self.t_pad_tgt : -self.t_pad_tgt]
                 )
             s = t
@@ -355,6 +391,7 @@ class VC(object):
                     big_npy,
                     index_rate,
                     version,
+                    protect
                 )[self.t_pad_tgt : -self.t_pad_tgt]
             )
         else:
@@ -371,6 +408,7 @@ class VC(object):
                     big_npy,
                     index_rate,
                     version,
+                    protect
                 )[self.t_pad_tgt : -self.t_pad_tgt]
             )
         audio_opt = np.concatenate(audio_opt)