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README.md

@@ -1,12 +1,57 @@
 ---
-title: RVC V2 Huggingface Version
-emoji: 🐠
-colorFrom: indigo
-colorTo: gray
+title: Magic Vocals
+emoji: 🦀
+colorFrom: red
+colorTo: pink
 sdk: gradio
-sdk_version: 3.44.4
+sdk_version: 3.42.0
 app_file: app.py
 pinned: false
+license: lgpl-3.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 🔧 Pre-requisites
+
+Before running the project, you must have the following tool installed on your machine: 
+* [Python v3.8.0](https://www.python.org/downloads/release/python-380/)
+
+Also, you will need to clone the repository:
+
+```bash
+# Clone the repository
+git clone https://huggingface.co/spaces/mateuseap/magic-vocals/
+# Enter in the root directory
+cd magic-vocals
+```
+
+## 🚀 How to run
+
+After you've cloned the repository and entered in the root directory, run the following commands:
+
+```bash
+# Create and activate a Virtual Environment (make sure you're using Python v3.8.0 to do it)
+python -m venv venv
+. venv/bin/activate
+
+# Change mode and execute a shell script to configure and run the application
+chmod +x run.sh
+./run.sh
+```
+
+After the shell script executes everything, the application will be running at http://127.0.0.1:7860! Open up the link in a browser to use the app:
+
+![Magic Vocals](https://i.imgur.com/V55oKv8.png)
+
+**You only need to execute the `run.sh` one time**, once you've executed it one time, you just need to activate the virtual environment and run the command below to start the app again:
+
+```bash
+python app.py
+```
+
+**THE `run.sh` IS SUPPORTED BY THE FOLLOWING OPERATING SYSTEMS:**
+
+
+| OS        | Supported |
+|-----------|:---------:|
+| `Windows` |     ❌    |
+| `Ubuntu`  |     ✅    |

app.py

@@ -0,0 +1,2086 @@
+import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np
+from mega import Mega
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+import threading
+from time import sleep
+from subprocess import Popen
+import faiss
+from random import shuffle
+import json, datetime, requests
+from gtts import gTTS
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+tmp = os.path.join(now_dir, "TEMP")
+shutil.rmtree(tmp, ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
+os.makedirs(tmp, exist_ok=True)
+os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
+os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
+os.environ["TEMP"] = tmp
+warnings.filterwarnings("ignore")
+torch.manual_seed(114514)
+from i18n import I18nAuto
+
+import signal
+
+import math
+
+from utils import load_audio, CSVutil
+
+global DoFormant, Quefrency, Timbre
+
+if not os.path.isdir('csvdb/'):
+    os.makedirs('csvdb')
+    frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w')
+    frmnt.close()
+    stp.close()
+
+try:
+    DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting')
+    DoFormant = (
+        lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant)
+    )(DoFormant)
+except (ValueError, TypeError, IndexError):
+    DoFormant, Quefrency, Timbre = False, 1.0, 1.0
+    CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre)
+
+def download_models():
+    # Download hubert base model if not present
+    if not os.path.isfile('./hubert_base.pt'):
+        response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt')
+
+        if response.status_code == 200:
+            with open('./hubert_base.pt', 'wb') as f:
+                f.write(response.content)
+            print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.")
+        else:
+            raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".")
+        
+    # Download rmvpe model if not present
+    if not os.path.isfile('./rmvpe.pt'):
+        response = requests.get('https://drive.usercontent.google.com/download?id=1Hkn4kNuVFRCNQwyxQFRtmzmMBGpQxptI&export=download&authuser=0&confirm=t&uuid=0b3a40de-465b-4c65-8c41-135b0b45c3f7&at=APZUnTV3lA3LnyTbeuduura6Dmi2:1693724254058')
+
+        if response.status_code == 200:
+            with open('./rmvpe.pt', 'wb') as f:
+                f.write(response.content)
+            print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.")
+        else:
+            raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".")
+
+download_models()
+
+print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n")
+
+def formant_apply(qfrency, tmbre):
+    Quefrency = qfrency
+    Timbre = tmbre
+    DoFormant = True
+    CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
+    
+    return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"})
+
+def get_fshift_presets():
+    fshift_presets_list = []
+    for dirpath, _, filenames in os.walk("./formantshiftcfg/"):
+        for filename in filenames:
+            if filename.endswith(".txt"):
+                fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/'))
+                
+    if len(fshift_presets_list) > 0:
+        return fshift_presets_list
+    else:
+        return ''
+
+
+
+def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button):
+    
+    if (cbox):
+
+        DoFormant = True
+        CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
+        #print(f"is checked? - {cbox}\ngot {DoFormant}")
+        
+        return (
+            {"value": True, "__type__": "update"},
+            {"visible": True, "__type__": "update"},
+            {"visible": True, "__type__": "update"},
+            {"visible": True, "__type__": "update"},
+            {"visible": True, "__type__": "update"},
+            {"visible": True, "__type__": "update"},
+        )
+        
+        
+    else:
+        
+        DoFormant = False
+        CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
+        
+        #print(f"is checked? - {cbox}\ngot {DoFormant}")
+        return (
+            {"value": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+            {"visible": False, "__type__": "update"},
+        )
+        
+
+
+def preset_apply(preset, qfer, tmbr):
+    if str(preset) != '':
+        with open(str(preset), 'r') as p:
+            content = p.readlines()
+            qfer, tmbr = content[0].split('\n')[0], content[1]
+            
+            formant_apply(qfer, tmbr)
+    else:
+        pass
+    return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"})
+
+def update_fshift_presets(preset, qfrency, tmbre):
+    
+    qfrency, tmbre = preset_apply(preset, qfrency, tmbre)
+    
+    if (str(preset) != ''):
+        with open(str(preset), 'r') as p:
+            content = p.readlines()
+            qfrency, tmbre = content[0].split('\n')[0], content[1]
+            
+            formant_apply(qfrency, tmbre)
+    else:
+        pass
+    return (
+        {"choices": get_fshift_presets(), "__type__": "update"},
+        {"value": qfrency, "__type__": "update"},
+        {"value": tmbre, "__type__": "update"},
+    )
+
+i18n = I18nAuto()
+#i18n.print()
+# 判断是否有能用来训练和加速推理的N卡
+ngpu = torch.cuda.device_count()
+gpu_infos = []
+mem = []
+if (not torch.cuda.is_available()) or ngpu == 0:
+    if_gpu_ok = False
+else:
+    if_gpu_ok = False
+    for i in range(ngpu):
+        gpu_name = torch.cuda.get_device_name(i)
+        if (
+            "10" in gpu_name
+            or "16" in gpu_name
+            or "20" in gpu_name
+            or "30" in gpu_name
+            or "40" in gpu_name
+            or "A2" in gpu_name.upper()
+            or "A3" in gpu_name.upper()
+            or "A4" in gpu_name.upper()
+            or "P4" in gpu_name.upper()
+            or "A50" in gpu_name.upper()
+            or "A60" in gpu_name.upper()
+            or "70" in gpu_name
+            or "80" in gpu_name
+            or "90" in gpu_name
+            or "M4" in gpu_name.upper()
+            or "T4" in gpu_name.upper()
+            or "TITAN" in gpu_name.upper()
+        ):  # A10#A100#V100#A40#P40#M40#K80#A4500
+            if_gpu_ok = True  # 至少有一张能用的N卡
+            gpu_infos.append("%s\t%s" % (i, gpu_name))
+            mem.append(
+                int(
+                    torch.cuda.get_device_properties(i).total_memory
+                    / 1024
+                    / 1024
+                    / 1024
+                    + 0.4
+                )
+            )
+if if_gpu_ok == True and len(gpu_infos) > 0:
+    gpu_info = "\n".join(gpu_infos)
+    default_batch_size = min(mem) // 2
+else:
+    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
+    default_batch_size = 1
+gpus = "-".join([i[0] for i in gpu_infos])
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+import soundfile as sf
+from fairseq import checkpoint_utils
+import gradio as gr
+import logging
+from vc_infer_pipeline import VC
+from config import Config
+
+config = Config()
+# from trainset_preprocess_pipeline import PreProcess
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+hubert_model = None
+
+def load_hubert():
+    global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(config.device)
+    if config.is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
+
+
+weight_root = "weights"
+index_root = "logs"
+names = []
+for name in os.listdir(weight_root):
+    if name.endswith(".pth"):
+        names.append(name)
+index_paths = []
+for root, dirs, files in os.walk(index_root, topdown=False):
+    for name in files:
+        if name.endswith(".index") and "trained" not in name:
+            index_paths.append("%s/%s" % (root, name))
+
+
+
+def vc_single(
+    sid,
+    input_audio_path,
+    f0_up_key,
+    f0_file,
+    f0_method,
+    file_index,
+    #file_index2,
+    # file_big_npy,
+    index_rate,
+    filter_radius,
+    resample_sr,
+    rms_mix_rate,
+    protect,
+    crepe_hop_length,
+):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
+    global tgt_sr, net_g, vc, hubert_model, version
+    if input_audio_path is None:
+        return "You need to upload an audio", None
+    f0_up_key = int(f0_up_key)
+    try:
+        audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre)
+        audio_max = np.abs(audio).max() / 0.95
+        if audio_max > 1:
+            audio /= audio_max
+        times = [0, 0, 0]
+        if hubert_model == None:
+            load_hubert()
+        if_f0 = cpt.get("f0", 1)
+        file_index = (
+            (
+                file_index.strip(" ")
+                .strip('"')
+                .strip("\n")
+                .strip('"')
+                .strip(" ")
+                .replace("trained", "added")
+            )
+        )  # 防止小白写错，自动帮他替换掉
+        # file_big_npy = (
+        #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        # )
+        audio_opt = vc.pipeline(
+            hubert_model,
+            net_g,
+            sid,
+            audio,
+            input_audio_path,
+            times,
+            f0_up_key,
+            f0_method,
+            file_index,
+            # file_big_npy,
+            index_rate,
+            if_f0,
+            filter_radius,
+            tgt_sr,
+            resample_sr,
+            rms_mix_rate,
+            version,
+            protect,
+            crepe_hop_length,
+            f0_file=f0_file,
+        )
+        if resample_sr >= 16000 and tgt_sr != resample_sr:
+            tgt_sr = resample_sr
+        index_info = (
+            "Using index:%s." % file_index
+            if os.path.exists(file_index)
+            else "Index not used."
+        )
+        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
+            index_info,
+            times[0],
+            times[1],
+            times[2],
+        ), (tgt_sr, audio_opt)
+    except:
+        info = traceback.format_exc()
+        print(info)
+        return info, (None, None)
+
+
+def vc_multi(
+    sid,
+    dir_path,
+    opt_root,
+    paths,
+    f0_up_key,
+    f0_method,
+    file_index,
+    file_index2,
+    # file_big_npy,
+    index_rate,
+    filter_radius,
+    resample_sr,
+    rms_mix_rate,
+    protect,
+    format1,
+    crepe_hop_length,
+):
+    try:
+        dir_path = (
+            dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )  # 防止小白拷路径头尾带了空格和"和回车
+        opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        os.makedirs(opt_root, exist_ok=True)
+        try:
+            if dir_path != "":
+                paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
+            else:
+                paths = [path.name for path in paths]
+        except:
+            traceback.print_exc()
+            paths = [path.name for path in paths]
+        infos = []
+        for path in paths:
+            info, opt = vc_single(
+                sid,
+                path,
+                f0_up_key,
+                None,
+                f0_method,
+                file_index,
+                # file_big_npy,
+                index_rate,
+                filter_radius,
+                resample_sr,
+                rms_mix_rate,
+                protect,
+                crepe_hop_length
+            )
+            if "Success" in info:
+                try:
+                    tgt_sr, audio_opt = opt
+                    if format1 in ["wav", "flac"]:
+                        sf.write(
+                            "%s/%s.%s" % (opt_root, os.path.basename(path), format1),
+                            audio_opt,
+                            tgt_sr,
+                        )
+                    else:
+                        path = "%s/%s.wav" % (opt_root, os.path.basename(path))
+                        sf.write(
+                            path,
+                            audio_opt,
+                            tgt_sr,
+                        )
+                        if os.path.exists(path):
+                            os.system(
+                                "ffmpeg -i %s -vn %s -q:a 2 -y"
+                                % (path, path[:-4] + ".%s" % format1)
+                            )
+                except:
+                    info += traceback.format_exc()
+            infos.append("%s->%s" % (os.path.basename(path), info))
+            yield "\n".join(infos)
+        yield "\n".join(infos)
+    except:
+        yield traceback.format_exc()
+
+# 一个选项卡全局只能有一个音色
+def get_vc(sid):
+    global n_spk, tgt_sr, net_g, vc, cpt, version
+    if sid == "" or sid == []:
+        global hubert_model
+        if hubert_model != None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+            print("clean_empty_cache")
+            del net_g, n_spk, vc, hubert_model, tgt_sr  # ,cpt
+            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            ###楼下不这么折腾清理不干净
+            if_f0 = cpt.get("f0", 1)
+            version = cpt.get("version", "v1")
+            if version == "v1":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs256NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            elif version == "v2":
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs768NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
+                else:
+                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+            del net_g, cpt
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            cpt = None
+        return {"visible": False, "__type__": "update"}
+    person = "%s/%s" % (weight_root, sid)
+    print("loading %s" % person)
+    cpt = torch.load(person, map_location="cpu")
+    tgt_sr = cpt["config"][-1]
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    if_f0 = cpt.get("f0", 1)
+    version = cpt.get("version", "v1")
+    if version == "v1":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    elif version == "v2":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+    del net_g.enc_q
+    print(net_g.load_state_dict(cpt["weight"], strict=False))
+    net_g.eval().to(config.device)
+    if config.is_half:
+        net_g = net_g.half()
+    else:
+        net_g = net_g.float()
+    vc = VC(tgt_sr, config)
+    n_spk = cpt["config"][-3]
+    return {"visible": False, "maximum": n_spk, "__type__": "update"}
+
+
+def change_choices():
+    names = []
+    for name in os.listdir(weight_root):
+        if name.endswith(".pth"):
+            names.append(name)
+    index_paths = []
+    for root, dirs, files in os.walk(index_root, topdown=False):
+        for name in files:
+            if name.endswith(".index") and "trained" not in name:
+                index_paths.append("%s/%s" % (root, name))
+    return {"choices": sorted(names), "__type__": "update"}, {
+        "choices": sorted(index_paths),
+        "__type__": "update",
+    }
+
+
+def clean():
+    return {"value": "", "__type__": "update"}
+
+
+sr_dict = {
+    "32k": 32000,
+    "40k": 40000,
+    "48k": 48000,
+}
+
+
+def if_done(done, p):
+    while 1:
+        if p.poll() == None:
+            sleep(0.5)
+        else:
+            break
+    done[0] = True
+
+
+def if_done_multi(done, ps):
+    while 1:
+        # poll==None代表进程未结束
+        # 只要有一个进程未结束都不停
+        flag = 1
+        for p in ps:
+            if p.poll() == None:
+                flag = 0
+                sleep(0.5)
+                break
+        if flag == 1:
+            break
+    done[0] = True
+
+
+def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
+    sr = sr_dict[sr]
+    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
+    f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
+    f.close()
+    cmd = (
+        config.python_cmd
+        + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
+        % (trainset_dir, sr, n_p, now_dir, exp_dir)
+        + str(config.noparallel)
+    )
+    print(cmd)
+    p = Popen(cmd, shell=True)  # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
+    ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+    done = [False]
+    threading.Thread(
+        target=if_done,
+        args=(
+            done,
+            p,
+        ),
+    ).start()
+    while 1:
+        with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
+            yield (f.read())
+        sleep(1)
+        if done[0] == True:
+            break
+    with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
+        log = f.read()
+    print(log)
+    yield log
+
+# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
+def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl):
+    gpus = gpus.split("-")
+    os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
+    f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
+    f.close()
+    if if_f0:
+        cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % (
+            now_dir,
+            exp_dir,
+            n_p,
+            f0method,
+            echl,
+        )
+        print(cmd)
+        p = Popen(cmd, shell=True, cwd=now_dir)  # , stdin=PIPE, stdout=PIPE,stderr=PIPE
+        ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+        done = [False]
+        threading.Thread(
+            target=if_done,
+            args=(
+                done,
+                p,
+            ),
+        ).start()
+        while 1:
+            with open(
+                "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
+            ) as f:
+                yield (f.read())
+            sleep(1)
+            if done[0] == True:
+                break
+        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+            log = f.read()
+        print(log)
+        yield log
+    ####对不同part分别开多进程
+    """
+    n_part=int(sys.argv[1])
+    i_part=int(sys.argv[2])
+    i_gpu=sys.argv[3]
+    exp_dir=sys.argv[4]
+    os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
+    """
+    leng = len(gpus)
+    ps = []
+    for idx, n_g in enumerate(gpus):
+        cmd = (
+            config.python_cmd
+            + " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
+            % (
+                config.device,
+                leng,
+                idx,
+                n_g,
+                now_dir,
+                exp_dir,
+                version19,
+            )
+        )
+        print(cmd)
+        p = Popen(
+            cmd, shell=True, cwd=now_dir
+        )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+        ps.append(p)
+    ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+    done = [False]
+    threading.Thread(
+        target=if_done_multi,
+        args=(
+            done,
+            ps,
+        ),
+    ).start()
+    while 1:
+        with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+            yield (f.read())
+        sleep(1)
+        if done[0] == True:
+            break
+    with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+        log = f.read()
+    print(log)
+    yield log
+
+
+def change_sr2(sr2, if_f0_3, version19):
+    path_str = "" if version19 == "v1" else "_v2"
+    f0_str = "f0" if if_f0_3 else ""
+    if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    return (
+        ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+        {"visible": True, "__type__": "update"}
+    )
+
+def change_version19(sr2, if_f0_3, version19):
+    path_str = "" if version19 == "v1" else "_v2"
+    f0_str = "f0" if if_f0_3 else ""
+    if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    return (
+        ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+    )
+
+
+def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
+    path_str = "" if version19 == "v1" else "_v2"
+    if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
+    if if_f0_3:
+        return (
+            {"visible": True, "__type__": "update"},
+            "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "",
+            "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "",
+        )
+    return (
+        {"visible": False, "__type__": "update"},
+        ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "",
+    )
+
+
+global log_interval
+
+
+def set_log_interval(exp_dir, batch_size12):
+    log_interval = 1
+
+    folder_path = os.path.join(exp_dir, "1_16k_wavs")
+
+    if os.path.exists(folder_path) and os.path.isdir(folder_path):
+        wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")]
+        if wav_files:
+            sample_size = len(wav_files)
+            log_interval = math.ceil(sample_size / batch_size12)
+            if log_interval > 1:
+                log_interval += 1
+    return log_interval
+
+# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
+def click_train(
+    exp_dir1,
+    sr2,
+    if_f0_3,
+    spk_id5,
+    save_epoch10,
+    total_epoch11,
+    batch_size12,
+    if_save_latest13,
+    pretrained_G14,
+    pretrained_D15,
+    gpus16,
+    if_cache_gpu17,
+    if_save_every_weights18,
+    version19,
+):
+    CSVutil('csvdb/stop.csv', 'w+', 'formanting', False)
+    # 生成filelist
+    exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+    os.makedirs(exp_dir, exist_ok=True)
+    gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
+    feature_dir = (
+        "%s/3_feature256" % (exp_dir)
+        if version19 == "v1"
+        else "%s/3_feature768" % (exp_dir)
+    )
+    
+    log_interval = set_log_interval(exp_dir, batch_size12)
+    
+    if if_f0_3:
+        f0_dir = "%s/2a_f0" % (exp_dir)
+        f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
+        names = (
+            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
+            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
+            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
+            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
+        )
+    else:
+        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
+            [name.split(".")[0] for name in os.listdir(feature_dir)]
+        )
+    opt = []
+    for name in names:
+        if if_f0_3:
+            opt.append(
+                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
+                % (
+                    gt_wavs_dir.replace("\\", "\\\\"),
+                    name,
+                    feature_dir.replace("\\", "\\\\"),
+                    name,
+                    f0_dir.replace("\\", "\\\\"),
+                    name,
+                    f0nsf_dir.replace("\\", "\\\\"),
+                    name,
+                    spk_id5,
+                )
+            )
+        else:
+            opt.append(
+                "%s/%s.wav|%s/%s.npy|%s"
+                % (
+                    gt_wavs_dir.replace("\\", "\\\\"),
+                    name,
+                    feature_dir.replace("\\", "\\\\"),
+                    name,
+                    spk_id5,
+                )
+            )
+    fea_dim = 256 if version19 == "v1" else 768
+    if if_f0_3:
+        for _ in range(2):
+            opt.append(
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
+            )
+    else:
+        for _ in range(2):
+            opt.append(
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
+                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
+            )
+    shuffle(opt)
+    with open("%s/filelist.txt" % exp_dir, "w") as f:
+        f.write("\n".join(opt))
+    print("write filelist done")
+    # 生成config#无需生成config
+    # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
+    print("use gpus:", gpus16)
+    if pretrained_G14 == "":
+        print("no pretrained Generator")
+    if pretrained_D15 == "":
+        print("no pretrained Discriminator")
+    if gpus16:
+        cmd = (
+            config.python_cmd
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s"
+            % (
+                exp_dir1,
+                sr2,
+                1 if if_f0_3 else 0,
+                batch_size12,
+                gpus16,
+                total_epoch11,
+                save_epoch10,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+                1 if if_save_latest13 == True else 0,
+                1 if if_cache_gpu17 == True else 0,
+                1 if if_save_every_weights18 == True else 0,
+                version19,
+                log_interval,
+            )
+        )
+    else:
+        cmd = (
+            config.python_cmd
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s"
+            % (
+                exp_dir1,
+                sr2,
+                1 if if_f0_3 else 0,
+                batch_size12,
+                total_epoch11,
+                save_epoch10,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b",
+                1 if if_save_latest13 == True else 0,
+                1 if if_cache_gpu17 == True else 0,
+                1 if if_save_every_weights18 == True else 0,
+                version19,
+                log_interval,
+            )
+        )
+    print(cmd)
+    p = Popen(cmd, shell=True, cwd=now_dir)
+    global PID
+    PID = p.pid
+    p.wait()
+    return ("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"})
+
+
+# but4.click(train_index, [exp_dir1], info3)
+def train_index(exp_dir1, version19):
+    exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+    os.makedirs(exp_dir, exist_ok=True)
+    feature_dir = (
+        "%s/3_feature256" % (exp_dir)
+        if version19 == "v1"
+        else "%s/3_feature768" % (exp_dir)
+    )
+    if os.path.exists(feature_dir) == False:
+        return "请先进行特征提取!"
+    listdir_res = list(os.listdir(feature_dir))
+    if len(listdir_res) == 0:
+        return "请先进行特征提取！"
+    npys = []
+    for name in sorted(listdir_res):
+        phone = np.load("%s/%s" % (feature_dir, name))
+        npys.append(phone)
+    big_npy = np.concatenate(npys, 0)
+    big_npy_idx = np.arange(big_npy.shape[0])
+    np.random.shuffle(big_npy_idx)
+    big_npy = big_npy[big_npy_idx]
+    np.save("%s/total_fea.npy" % exp_dir, big_npy)
+    # n_ivf =  big_npy.shape[0] // 39
+    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+    infos = []
+    infos.append("%s,%s" % (big_npy.shape, n_ivf))
+    yield "\n".join(infos)
+    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
+    # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
+    infos.append("training")
+    yield "\n".join(infos)
+    index_ivf = faiss.extract_index_ivf(index)  #
+    index_ivf.nprobe = 1
+    index.train(big_npy)
+    faiss.write_index(
+        index,
+        "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (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")
+    yield "\n".join(infos)
+    batch_size_add = 8192
+    for i in range(0, big_npy.shape[0], batch_size_add):
+        index.add(big_npy[i : i + batch_size_add])
+    faiss.write_index(
+        index,
+        "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+        % (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)
+    )
+    # 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))
+    yield "\n".join(infos)
+
+
+# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
+def train1key(
+    exp_dir1,
+    sr2,
+    if_f0_3,
+    trainset_dir4,
+    spk_id5,
+    np7,
+    f0method8,
+    save_epoch10,
+    total_epoch11,
+    batch_size12,
+    if_save_latest13,
+    pretrained_G14,
+    pretrained_D15,
+    gpus16,
+    if_cache_gpu17,
+    if_save_every_weights18,
+    version19,
+    echl
+):
+    infos = []
+
+    def get_info_str(strr):
+        infos.append(strr)
+        return "\n".join(infos)
+
+    model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+    preprocess_log_path = "%s/preprocess.log" % model_log_dir
+    extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
+    gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
+    feature_dir = (
+        "%s/3_feature256" % model_log_dir
+        if version19 == "v1"
+        else "%s/3_feature768" % model_log_dir
+    )
+
+    os.makedirs(model_log_dir, exist_ok=True)
+    #########step1:处理数据
+    open(preprocess_log_path, "w").close()
+    cmd = (
+        config.python_cmd
+        + " trainset_preprocess_pipeline_print.py %s %s %s %s "
+        % (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
+        + str(config.noparallel)
+    )
+    yield get_info_str(i18n("step1:正在处理数据"))
+    yield get_info_str(cmd)
+    p = Popen(cmd, shell=True)
+    p.wait()
+    with open(preprocess_log_path, "r") as f:
+        print(f.read())
+    #########step2a:提取音高
+    open(extract_f0_feature_log_path, "w")
+    if if_f0_3:
+        yield get_info_str("step2a:正在提取音高")
+        cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % (
+            model_log_dir,
+            np7,
+            f0method8,
+            echl
+        )
+        yield get_info_str(cmd)
+        p = Popen(cmd, shell=True, cwd=now_dir)
+        p.wait()
+        with open(extract_f0_feature_log_path, "r") as f:
+            print(f.read())
+    else:
+        yield get_info_str(i18n("step2a:无需提取音高"))
+    #######step2b:提取特征
+    yield get_info_str(i18n("step2b:正在提取特征"))
+    gpus = gpus16.split("-")
+    leng = len(gpus)
+    ps = []
+    for idx, n_g in enumerate(gpus):
+        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
+            config.device,
+            leng,
+            idx,
+            n_g,
+            model_log_dir,
+            version19,
+        )
+        yield get_info_str(cmd)
+        p = Popen(
+            cmd, shell=True, cwd=now_dir
+        )  # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+        ps.append(p)
+    for p in ps:
+        p.wait()
+    with open(extract_f0_feature_log_path, "r") as f:
+        print(f.read())
+    #######step3a:训练模型
+    yield get_info_str(i18n("step3a:正在训练模型"))
+    # 生成filelist
+    if if_f0_3:
+        f0_dir = "%s/2a_f0" % model_log_dir
+        f0nsf_dir = "%s/2b-f0nsf" % model_log_dir
+        names = (
+            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
+            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
+            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
+            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
+        )
+    else:
+        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
+            [name.split(".")[0] for name in os.listdir(feature_dir)]
+        )
+    opt = []
+    for name in names:
+        if if_f0_3:
+            opt.append(
+                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
+                % (
+                    gt_wavs_dir.replace("\\", "\\\\"),
+                    name,
+                    feature_dir.replace("\\", "\\\\"),
+                    name,
+                    f0_dir.replace("\\", "\\\\"),
+                    name,
+                    f0nsf_dir.replace("\\", "\\\\"),
+                    name,
+                    spk_id5,
+                )
+            )
+        else:
+            opt.append(
+                "%s/%s.wav|%s/%s.npy|%s"
+                % (
+                    gt_wavs_dir.replace("\\", "\\\\"),
+                    name,
+                    feature_dir.replace("\\", "\\\\"),
+                    name,
+                    spk_id5,
+                )
+            )
+    fea_dim = 256 if version19 == "v1" else 768
+    if if_f0_3:
+        for _ in range(2):
+            opt.append(
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
+            )
+    else:
+        for _ in range(2):
+            opt.append(
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
+                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
+            )
+    shuffle(opt)
+    with open("%s/filelist.txt" % model_log_dir, "w") as f:
+        f.write("\n".join(opt))
+    yield get_info_str("write filelist done")
+    if gpus16:
+        cmd = (
+            config.python_cmd
+            +" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+            % (
+                exp_dir1,
+                sr2,
+                1 if if_f0_3 else 0,
+                batch_size12,
+                gpus16,
+                total_epoch11,
+                save_epoch10,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+                1 if if_save_latest13 == True else 0,
+                1 if if_cache_gpu17 == True else 0,
+                1 if if_save_every_weights18 == True else 0,
+                version19,
+            )
+        )
+    else:
+        cmd = (
+            config.python_cmd
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+            % (
+                exp_dir1,
+                sr2,
+                1 if if_f0_3 else 0,
+                batch_size12,
+                total_epoch11,
+                save_epoch10,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+                1 if if_save_latest13 == True else 0,
+                1 if if_cache_gpu17 == True else 0,
+                1 if if_save_every_weights18 == True else 0,
+                version19,
+            )
+        )
+    yield get_info_str(cmd)
+    p = Popen(cmd, shell=True, cwd=now_dir)
+    p.wait()
+    yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"))
+    #######step3b:训练索引
+    npys = []
+    listdir_res = list(os.listdir(feature_dir))
+    for name in sorted(listdir_res):
+        phone = np.load("%s/%s" % (feature_dir, name))
+        npys.append(phone)
+    big_npy = np.concatenate(npys, 0)
+
+    big_npy_idx = np.arange(big_npy.shape[0])
+    np.random.shuffle(big_npy_idx)
+    big_npy = big_npy[big_npy_idx]
+    np.save("%s/total_fea.npy" % model_log_dir, big_npy)
+
+    # n_ivf =  big_npy.shape[0] // 39
+    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+    yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
+    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
+    yield get_info_str("training index")
+    index_ivf = faiss.extract_index_ivf(index)  #
+    index_ivf.nprobe = 1
+    index.train(big_npy)
+    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),
+    )
+    yield get_info_str("adding index")
+    batch_size_add = 8192
+    for i in range(0, big_npy.shape[0], batch_size_add):
+        index.add(big_npy[i : i + batch_size_add])
+    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),
+    )
+    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)
+    )
+    yield get_info_str(i18n("全流程结束！"))
+
+
+def whethercrepeornah(radio):
+    mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False
+    return ({"visible": mango, "__type__": "update"})
+
+#                    ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
+def change_info_(ckpt_path):
+    if (
+        os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log"))
+        == False
+    ):
+        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
+    try:
+        with open(
+            ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
+        ) as f:
+            info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
+            sr, f0 = info["sample_rate"], info["if_f0"]
+            version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
+            return sr, str(f0), version
+    except:
+        traceback.print_exc()
+        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
+
+
+from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
+
+
+def export_onnx(ModelPath, ExportedPath, MoeVS=True):
+    cpt = torch.load(ModelPath, map_location="cpu")
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2]  # hidden_channels，为768Vec做准备
+
+    test_phone = torch.rand(1, 200, hidden_channels)  # hidden unit
+    test_phone_lengths = torch.tensor([200]).long()  # hidden unit 长度（貌似没啥用）
+    test_pitch = torch.randint(size=(1, 200), low=5, high=255)  # 基频（单位赫兹）
+    test_pitchf = torch.rand(1, 200)  # nsf基频
+    test_ds = torch.LongTensor([0])  # 说话人ID
+    test_rnd = torch.rand(1, 192, 200)  # 噪声（加入随机因子）
+
+    device = "cpu"  # 导出时设备（不影响使用模型）
+
+
+    net_g = SynthesizerTrnMsNSFsidM(
+        *cpt["config"], is_half=False,version=cpt.get("version","v1")
+    )  # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
+    net_g.load_state_dict(cpt["weight"], strict=False)
+    input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
+    output_names = [
+        "audio",
+    ]
+    # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
+    torch.onnx.export(
+        net_g,
+        (
+            test_phone.to(device),
+            test_phone_lengths.to(device),
+            test_pitch.to(device),
+            test_pitchf.to(device),
+            test_ds.to(device),
+            test_rnd.to(device),
+        ),
+        ExportedPath,
+        dynamic_axes={
+            "phone": [1],
+            "pitch": [1],
+            "pitchf": [1],
+            "rnd": [2],
+        },
+        do_constant_folding=False,
+        opset_version=16,
+        verbose=False,
+        input_names=input_names,
+        output_names=output_names,
+    )
+    return "Finished"
+
+#region RVC WebUI App
+
+def get_presets():
+    data = None
+    with open('../inference-presets.json', 'r') as file:
+        data = json.load(file)
+    preset_names = []
+    for preset in data['presets']:
+        preset_names.append(preset['name'])
+    
+    return preset_names
+
+def change_choices2():
+    audio_files=[]
+    for filename in os.listdir("./audios"):
+        if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
+            audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
+    return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"}
+    
+audio_files=[]
+for filename in os.listdir("./audios"):
+    if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
+        audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
+        
+def get_index():
+    if check_for_name() != '':
+        chosen_model=sorted(names)[0].split(".")[0]
+        logs_path="./logs/"+chosen_model
+        if os.path.exists(logs_path):
+            for file in os.listdir(logs_path):
+                if file.endswith(".index"):
+                    return os.path.join(logs_path, file)
+            return ''
+        else:
+            return ''
+        
+def get_indexes():
+    indexes_list=[]
+    for dirpath, dirnames, filenames in os.walk("./logs/"):
+        for filename in filenames:
+            if filename.endswith(".index"):
+                indexes_list.append(os.path.join(dirpath,filename))
+    if len(indexes_list) > 0:
+        return indexes_list
+    else:
+        return ''
+        
+def get_name():
+    if len(audio_files) > 0:
+        return sorted(audio_files)[0]
+    else:
+        return ''
+        
+def save_to_wav(record_button):
+    if record_button is None:
+        pass
+    else:
+        path_to_file=record_button
+        new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
+        new_path='./audios/'+new_name
+        shutil.move(path_to_file,new_path)
+        return new_path
+    
+def save_to_wav2(dropbox):
+    file_path=dropbox.name
+    shutil.move(file_path,'./audios')
+    return os.path.join('./audios',os.path.basename(file_path))
+    
+def match_index(sid0):
+    folder=sid0.split(".")[0]
+    parent_dir="./logs/"+folder
+    if os.path.exists(parent_dir):
+        for filename in os.listdir(parent_dir):
+            if filename.endswith(".index"):
+                index_path=os.path.join(parent_dir,filename)
+                return index_path
+    else:
+        return ''
+                
+def check_for_name():
+    if len(names) > 0:
+        return sorted(names)[0]
+    else:
+        return ''
+            
+def download_from_url(url, model):
+    if url == '':
+        return "URL cannot be left empty."
+    if model =='':
+        return "You need to name your model. For example: My-Model"
+    url = url.strip()
+    zip_dirs = ["zips", "unzips"]
+    for directory in zip_dirs:
+        if os.path.exists(directory):
+            shutil.rmtree(directory)
+    os.makedirs("zips", exist_ok=True)
+    os.makedirs("unzips", exist_ok=True)
+    zipfile = model + '.zip'
+    zipfile_path = './zips/' + zipfile
+    try:
+        if "drive.google.com" in url:
+            subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path])
+        elif "mega.nz" in url:
+            m = Mega()
+            m.download_url(url, './zips')
+        else:
+            subprocess.run(["wget", url, "-O", zipfile_path])
+        for filename in os.listdir("./zips"):
+            if filename.endswith(".zip"):
+                zipfile_path = os.path.join("./zips/",filename)
+                shutil.unpack_archive(zipfile_path, "./unzips", 'zip')
+            else:
+                return "No zipfile found."
+        for root, dirs, files in os.walk('./unzips'):
+            for file in files:
+                file_path = os.path.join(root, file)
+                if file.endswith(".index"):
+                    os.mkdir(f'./logs/{model}')
+                    shutil.copy2(file_path,f'./logs/{model}')
+                elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
+                    shutil.copy(file_path,f'./weights/{model}.pth')
+        shutil.rmtree("zips")
+        shutil.rmtree("unzips")
+        return "Success."
+    except:
+        return "There's been an error."
+def success_message(face):
+    return f'{face.name} has been uploaded.', 'None'
+def mouth(size, face, voice, faces):
+    if size == 'Half':
+        size = 2
+    else:
+        size = 1
+    if faces == 'None':
+        character = face.name
+    else:
+        if faces == 'Ben Shapiro':
+            character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4'
+        elif faces == 'Andrew Tate':
+            character = '/content/wav2lip-HD/inputs/tate-7.mp4'
+    command = "python inference.py " \
+            "--checkpoint_path checkpoints/wav2lip.pth " \
+            f"--face {character} " \
+            f"--audio {voice} " \
+            "--pads 0 20 0 0 " \
+            "--outfile /content/wav2lip-HD/outputs/result.mp4 " \
+            "--fps 24 " \
+            f"--resize_factor {size}"
+    process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master')
+    stdout, stderr = process.communicate()
+    return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.'
+eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli']
+eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O']
+chosen_voice = dict(zip(eleven_voices, eleven_voices_ids))
+
+def stoptraining(mim): 
+    if int(mim) == 1:
+        try:
+            CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True')
+            os.kill(PID, signal.SIGTERM)
+        except Exception as e:
+            print(f"Couldn't click due to {e}")
+    return (
+        {"visible": False, "__type__": "update"}, 
+        {"visible": True, "__type__": "update"},
+    )
+
+
+def elevenTTS(xiapi, text, id, lang):
+    if xiapi!= '' and id !='': 
+        choice = chosen_voice[id]
+        CHUNK_SIZE = 1024
+        url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}"
+        headers = {
+        "Accept": "audio/mpeg",
+        "Content-Type": "application/json",
+        "xi-api-key": xiapi
+        }
+        if lang == 'en':
+            data = {
+            "text": text,
+            "model_id": "eleven_monolingual_v1",
+            "voice_settings": {
+            "stability": 0.5,
+            "similarity_boost": 0.5
+            }
+            }
+        else:
+            data = {
+            "text": text,
+            "model_id": "eleven_multilingual_v1",
+            "voice_settings": {
+            "stability": 0.5,
+            "similarity_boost": 0.5
+            }
+            }
+
+        response = requests.post(url, json=data, headers=headers)
+        with open('./temp_eleven.mp3', 'wb') as f:
+          for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
+              if chunk:
+                  f.write(chunk)
+        aud_path = save_to_wav('./temp_eleven.mp3')
+        return aud_path, aud_path
+    else:
+        tts = gTTS(text, lang=lang)
+        tts.save('./temp_gTTS.mp3')
+        aud_path = save_to_wav('./temp_gTTS.mp3')
+        return aud_path, aud_path
+
+def upload_to_dataset(files, dir):
+    if dir == '':
+        dir = './dataset'
+    if not os.path.exists(dir):
+        os.makedirs(dir)
+    count = 0
+    for file in files:
+        path=file.name
+        shutil.copy2(path,dir)
+        count += 1
+    return f' {count} files uploaded to {dir}.'     
+    
+def zip_downloader(model):
+    if not os.path.exists(f'./weights/{model}.pth'):
+        return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth'
+    index_found = False
+    for file in os.listdir(f'./logs/{model}'):
+        if file.endswith('.index') and 'added' in file:
+            log_file = file
+            index_found = True
+    if index_found:
+        return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done"
+    else:
+        return f'./weights/{model}.pth', "Could not find Index file."
+
+with gr.Blocks(theme=gr.themes.Base(), title='Mangio-RVC-Web 💻') as app:
+    with gr.Tabs():
+        with gr.TabItem("Inference"):
+            gr.HTML("<h1>  RVC V2 Huggingface Version </h1>")      
+
+            # Inference Preset Row
+            # with gr.Row():
+            #     mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets()))
+            #     mangio_preset_name_save = gr.Textbox(
+            #         label="Your preset name"
+            #     )
+            #     mangio_preset_save_btn = gr.Button('Save Preset', variant="primary")
+
+            # Other RVC stuff
+            with gr.Row():
+                sid0 = gr.Dropdown(label="1.Choose your Model.", choices=sorted(names), value=check_for_name())
+                refresh_button = gr.Button("Refresh", variant="primary")
+                if check_for_name() != '':
+                    get_vc(sorted(names)[0])
+                vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 0.", value=0)
+                #clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
+                spk_item = gr.Slider(
+                    minimum=0,
+                    maximum=2333,
+                    step=1,
+                    label=i18n("请选择说话人id"),
+                    value=0,
+                    visible=False,
+                    interactive=True,
+                )
+                #clean_button.click(fn=clean, inputs=[], outputs=[sid0])
+                sid0.change(
+                    fn=get_vc,
+                    inputs=[sid0],
+                    outputs=[spk_item],
+                )
+                but0 = gr.Button("Convert", variant="primary")
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        dropbox = gr.File(label="Drop your audio here & hit the Reload button.")
+                    with gr.Row():
+                        record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath")
+                    with gr.Row():
+                        input_audio0 = gr.Dropdown(
+                            label="2.Choose your audio.",
+                            value="./audios/someguy.mp3",
+                            choices=audio_files
+                            )
+                        dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
+                        dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0])
+                        refresh_button2 = gr.Button("Refresh", variant="primary", size='sm')
+                        record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
+                        record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0])
+                    with gr.Row():
+                        with gr.Accordion('Text To Speech', open=False):
+                            with gr.Column():
+                                lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en')
+                                api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='')
+                                elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices)
+                            with gr.Column():
+                                tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.")
+                                tts_button = gr.Button(value="Speak")
+                                tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0])
+                    with gr.Row():
+                        with gr.Accordion('Wav2Lip', open=False):
+                            with gr.Row():
+                                size = gr.Radio(label='Resolution:',choices=['Half','Full'])
+                                face = gr.UploadButton("Upload A Character",type='file')
+                                faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate'])
+                            with gr.Row():
+                                preview = gr.Textbox(label="Status:",interactive=False)
+                                face.upload(fn=success_message,inputs=[face], outputs=[preview, faces])
+                            with gr.Row():
+                                animation = gr.Video(type='filepath')
+                                refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation])
+                            with gr.Row():
+                                animate_button = gr.Button('Animate')
+
+                with gr.Column():
+                    with gr.Accordion("Index Settings", open=False):
+                        file_index1 = gr.Dropdown(
+                            label="3. Path to your added.index file (if it didn't automatically find it.)",
+                            choices=get_indexes(),
+                            value=get_index(),
+                            interactive=True,
+                            )
+                        sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1])
+                        refresh_button.click(
+                            fn=change_choices, inputs=[], outputs=[sid0, file_index1]
+                            )
+                        # file_big_npy1 = gr.Textbox(
+                        #     label=i18n("特征文件路径"),
+                        #     value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
+                        #     interactive=True,
+                        # )
+                        index_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("检索特征占比"),
+                            value=0.66,
+                            interactive=True,
+                            )
+                    vc_output2 = gr.Audio(
+                        label="Output Audio (Click on the Three Dots in the Right Corner to Download)",
+                        type='filepath',
+                        interactive=False,
+                    )
+                    animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview])
+                    with gr.Accordion("Advanced Settings", open=False):
+                        f0method0 = gr.Radio(
+                            label="Optional: Change the Pitch Extraction Algorithm.\nExtraction methods are sorted from 'worst quality' to 'best quality'.\nmangio-crepe may or may not be better than rmvpe in cases where 'smoothness' is more important, but rmvpe is the best overall.",
+                            choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        
+                        crepe_hop_length = gr.Slider(
+                            minimum=1,
+                            maximum=512,
+                            step=1,
+                            label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.",
+                            value=120,
+                            interactive=True,
+                            visible=False,
+                            )
+                        f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length])
+                        filter_radius0 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+                            value=3,
+                            step=1,
+                            interactive=True,
+                            )
+                        resample_sr0 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+                            value=0,
+                            step=1,
+                            interactive=True,
+                            visible=False
+                            )
+                        rms_mix_rate0 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            value=0.21,
+                            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,
+                            )
+                        formanting = gr.Checkbox(
+                            value=bool(DoFormant),
+                            label="[EXPERIMENTAL] Formant shift inference audio",
+                            info="Used for male to female and vice-versa conversions",
+                            interactive=True,
+                            visible=True,
+                        )
+                        
+                        formant_preset = gr.Dropdown(
+                            value='',
+                            choices=get_fshift_presets(),
+                            label="browse presets for formanting",
+                            visible=bool(DoFormant),
+                        )
+                        formant_refresh_button = gr.Button(
+                            value='\U0001f504',
+                            visible=bool(DoFormant),
+                            variant='primary',
+                        )
+                        #formant_refresh_button = ToolButton( elem_id='1')
+                        #create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets")
+                        
+                        qfrency = gr.Slider(
+                                value=Quefrency,
+                                info="Default value is 1.0",
+                                label="Quefrency for formant shifting",
+                                minimum=0.0,
+                                maximum=16.0,
+                                step=0.1,
+                                visible=bool(DoFormant),
+                                interactive=True,
+                            )
+                        tmbre = gr.Slider(
+                            value=Timbre,
+                            info="Default value is 1.0",
+                            label="Timbre for formant shifting",
+                            minimum=0.0,
+                            maximum=16.0,
+                            step=0.1,
+                            visible=bool(DoFormant),
+                            interactive=True,
+                        )
+                        
+                        formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre])
+                        frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant))
+                        formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button])
+                        frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre])
+                        formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre])
+            with gr.Row():
+                vc_output1 = gr.Textbox("")
+                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"), visible=False)
+                
+                but0.click(
+                    vc_single,
+                    [
+                        spk_item,
+                        input_audio0,
+                        vc_transform0,
+                        f0_file,
+                        f0method0,
+                        file_index1,
+                        # file_index2,
+                        # file_big_npy1,
+                        index_rate1,
+                        filter_radius0,
+                        resample_sr0,
+                        rms_mix_rate0,
+                        protect0,
+                        crepe_hop_length
+                    ],
+                    [vc_output1, vc_output2],
+                )
+                        
+            with gr.Accordion("Batch Conversion",open=False):
+                with gr.Row():
+                    with gr.Column():
+                        vc_transform1 = gr.Number(
+                            label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
+                        )
+                        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,crepe效果好但吃GPU"
+                            ),
+                            choices=["pm", "harvest", "crepe", "rmvpe"],
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        filter_radius1 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+                            value=3,
+                            step=1,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        file_index3 = gr.Textbox(
+                            label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
+                            value="",
+                            interactive=True,
+                        )
+                        file_index4 = gr.Dropdown(
+                            label=i18n("自动检测index路径,下拉式选择(dropdown)"),
+                            choices=sorted(index_paths),
+                            interactive=True,
+                        )
+                        refresh_button.click(
+                            fn=lambda: change_choices()[1],
+                            inputs=[],
+                            outputs=file_index4,
+                        )
+                        # file_big_npy2 = gr.Textbox(
+                        #     label=i18n("特征文件路径"),
+                        #     value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
+                        #     interactive=True,
+                        # )
+                        index_rate2 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("检索特征占比"),
+                            value=1,
+                            interactive=True,
+                        )
+                    with gr.Column():
+                        resample_sr1 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+                            value=0,
+                            step=1,
+                            interactive=True,
+                        )
+                        rms_mix_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            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)"),
+                            value="E:\codes\py39\\test-20230416b\\todo-songs",
+                        )
+                        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")
+                        )
+                    with gr.Row():
+                        format1 = gr.Radio(
+                            label=i18n("导出文件格式"),
+                            choices=["wav", "flac", "mp3", "m4a"],
+                            value="flac",
+                            interactive=True,
+                        )
+                        but1 = gr.Button(i18n("转换"), variant="primary")
+                        vc_output3 = gr.Textbox(label=i18n("输出信息"))
+                    but1.click(
+                        vc_multi,
+                        [
+                            spk_item,
+                            dir_input,
+                            opt_input,
+                            inputs,
+                            vc_transform1,
+                            f0method1,
+                            file_index3,
+                            file_index4,
+                            # file_big_npy2,
+                            index_rate2,
+                            filter_radius1,
+                            resample_sr1,
+                            rms_mix_rate1,
+                            protect1,
+                            format1,
+                            crepe_hop_length,
+                        ],
+                        [vc_output3],
+                    )
+                    but1.click(fn=lambda: easy_uploader.clear())
+        with gr.TabItem("Download Model"):
+            with gr.Row():
+                url=gr.Textbox(label="Enter the URL to the Model:")
+            with gr.Row():
+                model = gr.Textbox(label="Name your model:")
+                download_button=gr.Button("Download")
+            with gr.Row():
+                status_bar=gr.Textbox(label="")
+                download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar])
+            with gr.Row():
+                gr.Markdown(
+                """
+                Made with ❤️ by [Alice Oliveira](https://github.com/aliceoq) | Hosted with ❤️ by [Mateus Elias](https://github.com/mateuseap)
+                """
+                )
+
+        def has_two_files_in_pretrained_folder():
+            pretrained_folder = "./pretrained/"
+            if not os.path.exists(pretrained_folder):
+                return False
+
+            files_in_folder = os.listdir(pretrained_folder)
+            num_files = len(files_in_folder)
+            return num_files >= 2
+
+        if has_two_files_in_pretrained_folder():    
+            print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------")       
+            with gr.TabItem("Train", visible=False):
+                with gr.Row():
+                    with gr.Column():
+                        exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice")
+                        sr2 = gr.Radio(
+                            label=i18n("目标采样率"),
+                            choices=["40k", "48k"],
+                            value="40k",
+                            interactive=True,
+                            visible=False
+                        )
+                        if_f0_3 = gr.Radio(
+                            label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"),
+                            choices=[True, False],
+                            value=True,
+                            interactive=True,
+                            visible=False
+                        )
+                        version19 = gr.Radio(
+                            label="RVC version",
+                            choices=["v1", "v2"],
+                            value="v2",
+                            interactive=True,
+                            visible=False,
+                        )
+                        np7 = gr.Slider(
+                            minimum=0,
+                            maximum=config.n_cpu,
+                            step=1,
+                            label="# of CPUs for data processing (Leave as it is)",
+                            value=config.n_cpu,
+                            interactive=True,
+                            visible=True
+                        )
+                        trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset")
+                        easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio'])
+                        but1 = gr.Button("1. Process The Dataset", variant="primary")
+                        info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", value="")
+                        easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1])
+                        but1.click(
+                            preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
+                        )
+                    with gr.Column():
+                        spk_id5 = gr.Slider(
+                            minimum=0,
+                            maximum=4,
+                            step=1,
+                            label=i18n("请指定说话人id"),
+                            value=0,
+                            interactive=True,
+                            visible=False
+                        )
+                        with gr.Accordion('GPU Settings', open=False, visible=False):
+                            gpus6 = gr.Textbox(
+                                label=i18n("以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"),
+                                value=gpus,
+                                interactive=True,
+                                visible=False
+                            )
+                            gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
+                        f0method8 = gr.Radio(
+                            label=i18n(
+                                "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢"
+                            ),
+                            choices=["harvest","crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training.
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        
+                        extraction_crepe_hop_length = gr.Slider(
+                            minimum=1,
+                            maximum=512,
+                            step=1,
+                            label=i18n("crepe_hop_length"),
+                            value=128,
+                            interactive=True,
+                            visible=False,
+                        )
+                        f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length])
+                        but2 = gr.Button("2. Pitch Extraction", variant="primary")
+                        info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=8)
+                        but2.click(
+                                extract_f0_feature,
+                                [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length],
+                                [info2],
+                            )
+                    with gr.Row():      
+                        with gr.Column():
+                            total_epoch11 = gr.Slider(
+                                minimum=1,
+                                maximum=5000,
+                                step=10,
+                                label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):",
+                                value=250,
+                                interactive=True,
+                            )
+                            butstop = gr.Button(
+                                "Stop Training",
+                                variant='primary',
+                                visible=False,
+                            )
+                            but3 = gr.Button("3. Train Model", variant="primary", visible=True)
+                            
+                            but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop])
+                            butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3])
+                            
+                            
+                            but4 = gr.Button("4.Train Index", variant="primary")
+                            info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10)
+                            with gr.Accordion("Training Preferences (You can leave these as they are)", open=False):
+                                #gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
+                                with gr.Column():
+                                    save_epoch10 = gr.Slider(
+                                        minimum=1,
+                                        maximum=200,
+                                        step=1,
+                                        label="Backup every X amount of epochs:",
+                                        value=10,
+                                        interactive=True,
+                                    )
+                                    batch_size12 = gr.Slider(
+                                        minimum=1,
+                                        maximum=40,
+                                        step=1,
+                                        label="Batch Size (LEAVE IT unless you know what you're doing!):",
+                                        value=default_batch_size,
+                                        interactive=True,
+                                    )
+                                    if_save_latest13 = gr.Checkbox(
+                                        label="Save only the latest '.ckpt' file to save disk space.",
+                                        value=True,
+                                        interactive=True,
+                                    )
+                                    if_cache_gpu17 = gr.Checkbox(
+                                        label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.",
+                                        value=False,
+                                        interactive=True,
+                                    )
+                                    if_save_every_weights18 = gr.Checkbox(
+                                        label="Save a small final model to the 'weights' folder at each save point.",
+                                        value=True,
+                                        interactive=True,
+                                    )
+                            zip_model = gr.Button('5. Download Model')
+                            zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:')
+                            zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3])
+                with gr.Group():
+                    with gr.Accordion("Base Model Locations:", open=False, visible=False):
+                        pretrained_G14 = gr.Textbox(
+                            label=i18n("加载预训练底模G路径"),
+                            value="pretrained_v2/f0G40k.pth",
+                            interactive=True,
+                        )
+                        pretrained_D15 = gr.Textbox(
+                            label=i18n("加载预训练底模D路径"),
+                            value="pretrained_v2/f0D40k.pth",
+                            interactive=True,
+                        )
+                        gpus16 = gr.Textbox(
+                            label=i18n("以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"),
+                            value=gpus,
+                            interactive=True,
+                        )
+                    sr2.change(
+                        change_sr2,
+                        [sr2, if_f0_3, version19],
+                        [pretrained_G14, pretrained_D15, version19],
+                    )
+                    version19.change(
+                        change_version19,
+                        [sr2, if_f0_3, version19],
+                        [pretrained_G14, pretrained_D15],
+                    )
+                    if_f0_3.change(
+                        change_f0,
+                        [if_f0_3, sr2, version19],
+                        [f0method8, pretrained_G14, pretrained_D15],
+                    )
+                    but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False)
+                    but3.click(
+                        click_train,
+                        [
+                            exp_dir1,
+                            sr2,
+                            if_f0_3,
+                            spk_id5,
+                            save_epoch10,
+                            total_epoch11,
+                            batch_size12,
+                            if_save_latest13,
+                            pretrained_G14,
+                            pretrained_D15,
+                            gpus16,
+                            if_cache_gpu17,
+                            if_save_every_weights18,
+                            version19,
+                        ],
+                        [
+                            info3,
+                            butstop,
+                            but3,
+                        ],
+                    )
+                    but4.click(train_index, [exp_dir1, version19], info3)
+                    but5.click(
+                        train1key,
+                        [
+                            exp_dir1,
+                            sr2,
+                            if_f0_3,
+                            trainset_dir4,
+                            spk_id5,
+                            np7,
+                            f0method8,
+                            save_epoch10,
+                            total_epoch11,
+                            batch_size12,
+                            if_save_latest13,
+                            pretrained_G14,
+                            pretrained_D15,
+                            gpus16,
+                            if_cache_gpu17,
+                            if_save_every_weights18,
+                            version19,
+                            extraction_crepe_hop_length
+                        ],
+                        info3,
+                    )
+
+        else:
+            print(
+                "Pretrained weights not downloaded. Disabling training tab.\n"
+                "Wondering how to train a voice? Visit here for the RVC model training guide: https://t.ly/RVC_Training_Guide\n"
+                "-------------------------------\n"
+            )
+                
+    app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=True)
+#endregion

config.py

@@ -0,0 +1,204 @@
+import argparse
+import sys
+import torch
+import json
+from multiprocessing import cpu_count
+
+global usefp16
+usefp16 = False
+
+
+def use_fp32_config():
+    usefp16 = False
+    device_capability = 0
+    if torch.cuda.is_available():
+        device = torch.device("cuda:0")  # Assuming you have only one GPU (index 0).
+        device_capability = torch.cuda.get_device_capability(device)[0]
+        if device_capability >= 7:
+            usefp16 = True
+            for config_file in ["32k.json", "40k.json", "48k.json"]:
+                with open(f"configs/{config_file}", "r") as d:
+                    data = json.load(d)
+
+                if "train" in data and "fp16_run" in data["train"]:
+                    data["train"]["fp16_run"] = True
+
+                with open(f"configs/{config_file}", "w") as d:
+                    json.dump(data, d, indent=4)
+
+                print(f"Set fp16_run to true in {config_file}")
+
+            with open(
+                "trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
+            ) as f:
+                strr = f.read()
+
+            strr = strr.replace("3.0", "3.7")
+
+            with open(
+                "trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
+            ) as f:
+                f.write(strr)
+        else:
+            for config_file in ["32k.json", "40k.json", "48k.json"]:
+                with open(f"configs/{config_file}", "r") as f:
+                    data = json.load(f)
+
+                if "train" in data and "fp16_run" in data["train"]:
+                    data["train"]["fp16_run"] = False
+
+                with open(f"configs/{config_file}", "w") as d:
+                    json.dump(data, d, indent=4)
+
+                print(f"Set fp16_run to false in {config_file}")
+
+            with open(
+                "trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
+            ) as f:
+                strr = f.read()
+
+            strr = strr.replace("3.7", "3.0")
+
+            with open(
+                "trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
+            ) as f:
+                f.write(strr)
+    else:
+        print(
+            "CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed."
+        )
+    return (usefp16, device_capability)
+
+
+class Config:
+    def __init__(self):
+        self.device = "cuda:0"
+        self.is_half = True
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.gpu_mem = None
+        (
+            self.python_cmd,
+            self.listen_port,
+            self.iscolab,
+            self.noparallel,
+            self.noautoopen,
+            self.paperspace,
+            self.is_cli,
+        ) = self.arg_parse()
+
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    @staticmethod
+    def arg_parse() -> tuple:
+        exe = sys.executable or "python"
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--port", type=int, default=7865, help="Listen port")
+        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
+        parser.add_argument("--colab", action="store_true", help="Launch in colab")
+        parser.add_argument(
+            "--noparallel", action="store_true", help="Disable parallel processing"
+        )
+        parser.add_argument(
+            "--noautoopen",
+            action="store_true",
+            help="Do not open in browser automatically",
+        )
+        parser.add_argument(  # Fork Feature. Paperspace integration for web UI
+            "--paperspace",
+            action="store_true",
+            help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
+        )
+        parser.add_argument(  # Fork Feature. Embed a CLI into the infer-web.py
+            "--is_cli",
+            action="store_true",
+            help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
+        )
+        cmd_opts = parser.parse_args()
+
+        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
+
+        return (
+            cmd_opts.pycmd,
+            cmd_opts.port,
+            cmd_opts.colab,
+            cmd_opts.noparallel,
+            cmd_opts.noautoopen,
+            cmd_opts.paperspace,
+            cmd_opts.is_cli,
+        )
+
+    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+    # check `getattr` and try it for compatibility
+    @staticmethod
+    def has_mps() -> bool:
+        if not torch.backends.mps.is_available():
+            return False
+        try:
+            torch.zeros(1).to(torch.device("mps"))
+            return True
+        except Exception:
+            return False
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            i_device = int(self.device.split(":")[-1])
+            self.gpu_name = torch.cuda.get_device_name(i_device)
+            if (
+                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
+                or "P40" in self.gpu_name.upper()
+                or "1060" in self.gpu_name
+                or "1070" in self.gpu_name
+                or "1080" in self.gpu_name
+            ):
+                print("Found GPU", self.gpu_name, ", force to fp32")
+                self.is_half = False
+            else:
+                print("Found GPU", self.gpu_name)
+                use_fp32_config()
+            self.gpu_mem = int(
+                torch.cuda.get_device_properties(i_device).total_memory
+                / 1024
+                / 1024
+                / 1024
+                + 0.4
+            )
+            if self.gpu_mem <= 4:
+                with open("trainset_preprocess_pipeline_print.py", "r") as f:
+                    strr = f.read().replace("3.7", "3.0")
+                with open("trainset_preprocess_pipeline_print.py", "w") as f:
+                    f.write(strr)
+        elif self.has_mps():
+            print("No supported Nvidia GPU found, use MPS instead")
+            self.device = "mps"
+            self.is_half = False
+            use_fp32_config()
+        else:
+            print("No supported Nvidia GPU found, use CPU instead")
+            self.device = "cpu"
+            self.is_half = False
+            use_fp32_config()
+
+        if self.n_cpu == 0:
+            self.n_cpu = cpu_count()
+
+        if self.is_half:
+            # 6G显存配置
+            x_pad = 3
+            x_query = 10
+            x_center = 60
+            x_max = 65
+        else:
+            # 5G显存配置
+            x_pad = 1
+            x_query = 6
+            x_center = 38
+            x_max = 41
+
+        if self.gpu_mem != None and self.gpu_mem <= 4:
+            x_pad = 1
+            x_query = 5
+            x_center = 30
+            x_max = 32
+
+        return x_pad, x_query, x_center, x_max

gitattributes.txt

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

gitignore.txt

@@ -0,0 +1,12 @@
+__pycache__/
+weights/
+TEMP/
+logs/
+csvdb/
+
+# Environment
+venv/
+
+# Models
+hubert_base.pt
+rmvpe.pt

i18n.py

@@ -0,0 +1,28 @@
+import locale
+import json
+import os
+
+
+def load_language_list(language):
+    with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
+        language_list = json.load(f)
+    return language_list
+
+
+class I18nAuto:
+    def __init__(self, language=None):
+        if language in ["Auto", None]:
+            language = locale.getdefaultlocale()[
+                0
+            ]  # getlocale can't identify the system's language ((None, None))
+        if not os.path.exists(f"./i18n/{language}.json"):
+            language = "en_US"
+        self.language = language
+        # print("Use Language:", language)
+        self.language_map = load_language_list(language)
+
+    def __call__(self, key):
+        return self.language_map.get(key, key)
+
+    def print(self):
+        print("Use Language:", self.language)

packages.txt

@@ -0,0 +1,3 @@
+build-essential
+ffmpeg
+aria2

requirements.txt

@@ -0,0 +1,22 @@
+gTTS
+elevenlabs
+stftpitchshift==1.5.1
+torchcrepe
+setuptools
+wheel
+httpx==0.23.0
+faiss-gpu
+fairseq
+gradio==3.34.0
+ffmpeg-python
+praat-parselmouth
+pyworld
+numpy==1.23.5
+i18n
+numba==0.56.4
+librosa==0.9.2
+mega.py
+gdown
+onnxruntime
+pyngrok==4.1.12
+torch

rmvpe.py

@@ -0,0 +1,432 @@
+import sys, torch, numpy as np, traceback, pdb
+import torch.nn as nn
+from time import time as ttime
+import torch.nn.functional as F
+
+
+class BiGRU(nn.Module):
+    def __init__(self, input_features, hidden_features, num_layers):
+        super(BiGRU, self).__init__()
+        self.gru = nn.GRU(
+            input_features,
+            hidden_features,
+            num_layers=num_layers,
+            batch_first=True,
+            bidirectional=True,
+        )
+
+    def forward(self, x):
+        return self.gru(x)[0]
+
+
+class ConvBlockRes(nn.Module):
+    def __init__(self, in_channels, out_channels, momentum=0.01):
+        super(ConvBlockRes, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=(3, 3),
+                stride=(1, 1),
+                padding=(1, 1),
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+            nn.Conv2d(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                kernel_size=(3, 3),
+                stride=(1, 1),
+                padding=(1, 1),
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+        )
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
+            self.is_shortcut = True
+        else:
+            self.is_shortcut = False
+
+    def forward(self, x):
+        if self.is_shortcut:
+            return self.conv(x) + self.shortcut(x)
+        else:
+            return self.conv(x) + x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        in_size,
+        n_encoders,
+        kernel_size,
+        n_blocks,
+        out_channels=16,
+        momentum=0.01,
+    ):
+        super(Encoder, self).__init__()
+        self.n_encoders = n_encoders
+        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
+        self.layers = nn.ModuleList()
+        self.latent_channels = []
+        for i in range(self.n_encoders):
+            self.layers.append(
+                ResEncoderBlock(
+                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
+                )
+            )
+            self.latent_channels.append([out_channels, in_size])
+            in_channels = out_channels
+            out_channels *= 2
+            in_size //= 2
+        self.out_size = in_size
+        self.out_channel = out_channels
+
+    def forward(self, x):
+        concat_tensors = []
+        x = self.bn(x)
+        for i in range(self.n_encoders):
+            _, x = self.layers[i](x)
+            concat_tensors.append(_)
+        return x, concat_tensors
+
+
+class ResEncoderBlock(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
+    ):
+        super(ResEncoderBlock, self).__init__()
+        self.n_blocks = n_blocks
+        self.conv = nn.ModuleList()
+        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
+        for i in range(n_blocks - 1):
+            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
+        self.kernel_size = kernel_size
+        if self.kernel_size is not None:
+            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
+
+    def forward(self, x):
+        for i in range(self.n_blocks):
+            x = self.conv[i](x)
+        if self.kernel_size is not None:
+            return x, self.pool(x)
+        else:
+            return x
+
+
+class Intermediate(nn.Module):  #
+    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
+        super(Intermediate, self).__init__()
+        self.n_inters = n_inters
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
+        )
+        for i in range(self.n_inters - 1):
+            self.layers.append(
+                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
+            )
+
+    def forward(self, x):
+        for i in range(self.n_inters):
+            x = self.layers[i](x)
+        return x
+
+
+class ResDecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
+        super(ResDecoderBlock, self).__init__()
+        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
+        self.n_blocks = n_blocks
+        self.conv1 = nn.Sequential(
+            nn.ConvTranspose2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=(3, 3),
+                stride=stride,
+                padding=(1, 1),
+                output_padding=out_padding,
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+        )
+        self.conv2 = nn.ModuleList()
+        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
+        for i in range(n_blocks - 1):
+            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
+
+    def forward(self, x, concat_tensor):
+        x = self.conv1(x)
+        x = torch.cat((x, concat_tensor), dim=1)
+        for i in range(self.n_blocks):
+            x = self.conv2[i](x)
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
+        super(Decoder, self).__init__()
+        self.layers = nn.ModuleList()
+        self.n_decoders = n_decoders
+        for i in range(self.n_decoders):
+            out_channels = in_channels // 2
+            self.layers.append(
+                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
+            )
+            in_channels = out_channels
+
+    def forward(self, x, concat_tensors):
+        for i in range(self.n_decoders):
+            x = self.layers[i](x, concat_tensors[-1 - i])
+        return x
+
+
+class DeepUnet(nn.Module):
+    def __init__(
+        self,
+        kernel_size,
+        n_blocks,
+        en_de_layers=5,
+        inter_layers=4,
+        in_channels=1,
+        en_out_channels=16,
+    ):
+        super(DeepUnet, self).__init__()
+        self.encoder = Encoder(
+            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
+        )
+        self.intermediate = Intermediate(
+            self.encoder.out_channel // 2,
+            self.encoder.out_channel,
+            inter_layers,
+            n_blocks,
+        )
+        self.decoder = Decoder(
+            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
+        )
+
+    def forward(self, x):
+        x, concat_tensors = self.encoder(x)
+        x = self.intermediate(x)
+        x = self.decoder(x, concat_tensors)
+        return x
+
+
+class E2E(nn.Module):
+    def __init__(
+        self,
+        n_blocks,
+        n_gru,
+        kernel_size,
+        en_de_layers=5,
+        inter_layers=4,
+        in_channels=1,
+        en_out_channels=16,
+    ):
+        super(E2E, self).__init__()
+        self.unet = DeepUnet(
+            kernel_size,
+            n_blocks,
+            en_de_layers,
+            inter_layers,
+            in_channels,
+            en_out_channels,
+        )
+        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
+        if n_gru:
+            self.fc = nn.Sequential(
+                BiGRU(3 * 128, 256, n_gru),
+                nn.Linear(512, 360),
+                nn.Dropout(0.25),
+                nn.Sigmoid(),
+            )
+        else:
+            self.fc = nn.Sequential(
+                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
+            )
+
+    def forward(self, mel):
+        mel = mel.transpose(-1, -2).unsqueeze(1)
+        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
+        x = self.fc(x)
+        return x
+
+
+from librosa.filters import mel
+
+
+class MelSpectrogram(torch.nn.Module):
+    def __init__(
+        self,
+        is_half,
+        n_mel_channels,
+        sampling_rate,
+        win_length,
+        hop_length,
+        n_fft=None,
+        mel_fmin=0,
+        mel_fmax=None,
+        clamp=1e-5,
+    ):
+        super().__init__()
+        n_fft = win_length if n_fft is None else n_fft
+        self.hann_window = {}
+        mel_basis = mel(
+            sr=sampling_rate,
+            n_fft=n_fft,
+            n_mels=n_mel_channels,
+            fmin=mel_fmin,
+            fmax=mel_fmax,
+            htk=True,
+        )
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer("mel_basis", mel_basis)
+        self.n_fft = win_length if n_fft is None else n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.sampling_rate = sampling_rate
+        self.n_mel_channels = n_mel_channels
+        self.clamp = clamp
+        self.is_half = is_half
+
+    def forward(self, audio, keyshift=0, speed=1, center=True):
+        factor = 2 ** (keyshift / 12)
+        n_fft_new = int(np.round(self.n_fft * factor))
+        win_length_new = int(np.round(self.win_length * factor))
+        hop_length_new = int(np.round(self.hop_length * speed))
+        keyshift_key = str(keyshift) + "_" + str(audio.device)
+        if keyshift_key not in self.hann_window:
+            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
+                audio.device
+            )
+        fft = torch.stft(
+            audio,
+            n_fft=n_fft_new,
+            hop_length=hop_length_new,
+            win_length=win_length_new,
+            window=self.hann_window[keyshift_key],
+            center=center,
+            return_complex=True,
+        )
+        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
+        if keyshift != 0:
+            size = self.n_fft // 2 + 1
+            resize = magnitude.size(1)
+            if resize < size:
+                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
+            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
+        mel_output = torch.matmul(self.mel_basis, magnitude)
+        if self.is_half == True:
+            mel_output = mel_output.half()
+        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
+        return log_mel_spec
+
+
+class RMVPE:
+    def __init__(self, model_path, is_half, device=None):
+        self.resample_kernel = {}
+        model = E2E(4, 1, (2, 2))
+        ckpt = torch.load(model_path, map_location="cpu")
+        model.load_state_dict(ckpt)
+        model.eval()
+        if is_half == True:
+            model = model.half()
+        self.model = model
+        self.resample_kernel = {}
+        self.is_half = is_half
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = device
+        self.mel_extractor = MelSpectrogram(
+            is_half, 128, 16000, 1024, 160, None, 30, 8000
+        ).to(device)
+        self.model = self.model.to(device)
+        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
+        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
+
+    def mel2hidden(self, mel):
+        with torch.no_grad():
+            n_frames = mel.shape[-1]
+            mel = F.pad(
+                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
+            )
+            hidden = self.model(mel)
+            return hidden[:, :n_frames]
+
+    def decode(self, hidden, thred=0.03):
+        cents_pred = self.to_local_average_cents(hidden, thred=thred)
+        f0 = 10 * (2 ** (cents_pred / 1200))
+        f0[f0 == 10] = 0
+        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
+        return f0
+
+    def infer_from_audio(self, audio, thred=0.03):
+        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
+        # torch.cuda.synchronize()
+        # t0=ttime()
+        mel = self.mel_extractor(audio, center=True)
+        # torch.cuda.synchronize()
+        # t1=ttime()
+        hidden = self.mel2hidden(mel)
+        # torch.cuda.synchronize()
+        # t2=ttime()
+        hidden = hidden.squeeze(0).cpu().numpy()
+        if self.is_half == True:
+            hidden = hidden.astype("float32")
+        f0 = self.decode(hidden, thred=thred)
+        # torch.cuda.synchronize()
+        # t3=ttime()
+        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
+        return f0
+
+    def to_local_average_cents(self, salience, thred=0.05):
+        # t0 = ttime()
+        center = np.argmax(salience, axis=1)  # 帧长#index
+        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
+        # t1 = ttime()
+        center += 4
+        todo_salience = []
+        todo_cents_mapping = []
+        starts = center - 4
+        ends = center + 5
+        for idx in range(salience.shape[0]):
+            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
+            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
+        # t2 = ttime()
+        todo_salience = np.array(todo_salience)  # 帧长，9
+        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
+        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
+        weight_sum = np.sum(todo_salience, 1)  # 帧长
+        devided = product_sum / weight_sum  # 帧长
+        # t3 = ttime()
+        maxx = np.max(salience, axis=1)  # 帧长
+        devided[maxx <= thred] = 0
+        # t4 = ttime()
+        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
+        return devided
+
+
+# if __name__ == '__main__':
+#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
+#     if len(audio.shape) > 1:
+#         audio = librosa.to_mono(audio.transpose(1, 0))
+#     audio_bak = audio.copy()
+#     if sampling_rate != 16000:
+#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
+#     thred = 0.03  # 0.01
+#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
+#     rmvpe = RMVPE(model_path,is_half=False, device=device)
+#     t0=ttime()
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     t1=ttime()
+#     print(f0.shape,t1-t0)

run.sh

@@ -0,0 +1,16 @@
+# Install Debian packages
+sudo apt-get update
+sudo apt-get install -qq -y build-essential ffmpeg aria2
+
+# Upgrade pip and setuptools
+pip install --upgrade pip
+pip install --upgrade setuptools
+
+# Install wheel package (built-package format for Python)
+pip install wheel
+
+# Install Python packages using pip
+pip install -r requirements.txt
+
+# Run application locally at http://127.0.0.1:7860
+python app.py

utils.py

@@ -0,0 +1,151 @@
+import ffmpeg
+import numpy as np
+
+# import praatio
+# import praatio.praat_scripts
+import os
+import sys
+
+import random
+
+import csv
+
+platform_stft_mapping = {
+    "linux": "stftpitchshift",
+    "darwin": "stftpitchshift",
+    "win32": "stftpitchshift.exe",
+}
+
+stft = platform_stft_mapping.get(sys.platform)
+# praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe")
+
+
+def CSVutil(file, rw, type, *args):
+    if type == "formanting":
+        if rw == "r":
+            with open(file) as fileCSVread:
+                csv_reader = list(csv.reader(fileCSVread))
+                return (
+                    (csv_reader[0][0], csv_reader[0][1], csv_reader[0][2])
+                    if csv_reader is not None
+                    else (lambda: exec('raise ValueError("No data")'))()
+                )
+        else:
+            if args:
+                doformnt = args[0]
+            else:
+                doformnt = False
+            qfr = args[1] if len(args) > 1 else 1.0
+            tmb = args[2] if len(args) > 2 else 1.0
+            with open(file, rw, newline="") as fileCSVwrite:
+                csv_writer = csv.writer(fileCSVwrite, delimiter=",")
+                csv_writer.writerow([doformnt, qfr, tmb])
+    elif type == "stop":
+        stop = args[0] if args else False
+        with open(file, rw, newline="") as fileCSVwrite:
+            csv_writer = csv.writer(fileCSVwrite, delimiter=",")
+            csv_writer.writerow([stop])
+
+
+def load_audio(file, sr, DoFormant, Quefrency, Timbre):
+    converted = False
+    DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting")
+    try:
+        # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        file = (
+            file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+        )  # 防止小白拷路径头尾带了空格和"和回车
+        file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+
+        # print(f"dofor={bool(DoFormant)} timbr={Timbre} quef={Quefrency}\n")
+
+        if (
+            lambda DoFormant: True
+            if DoFormant.lower() == "true"
+            else (False if DoFormant.lower() == "false" else DoFormant)
+        )(DoFormant):
+            numerator = round(random.uniform(1, 4), 4)
+            # os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}")
+            # print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted))
+
+            if not file.endswith(".wav"):
+                if not os.path.isfile(f"{file_formanted}.wav"):
+                    converted = True
+                    # print(f"\nfile = {file}\n")
+                    # print(f"\nfile_formanted = {file_formanted}\n")
+                    converting = (
+                        ffmpeg.input(file_formanted, threads=0)
+                        .output(f"{file_formanted}.wav")
+                        .run(
+                            cmd=["ffmpeg", "-nostdin"],
+                            capture_stdout=True,
+                            capture_stderr=True,
+                        )
+                    )
+                else:
+                    pass
+
+            file_formanted = (
+                f"{file_formanted}.wav"
+                if not file_formanted.endswith(".wav")
+                else file_formanted
+            )
+
+            print(f" · Formanting {file_formanted}...\n")
+
+            os.system(
+                '%s -i "%s" -q "%s" -t "%s" -o "%sFORMANTED_%s.wav"'
+                % (
+                    stft,
+                    file_formanted,
+                    Quefrency,
+                    Timbre,
+                    file_formanted,
+                    str(numerator),
+                )
+            )
+
+            print(f" · Formanted {file_formanted}!\n")
+
+            # filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\')
+            # file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\')
+            # print("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
+
+            out, _ = (
+                ffmpeg.input(
+                    "%sFORMANTED_%s.wav" % (file_formanted, str(numerator)), threads=0
+                )
+                .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
+                .run(
+                    cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
+                )
+            )
+
+            try:
+                os.remove("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
+            except Exception:
+                pass
+                print("couldn't remove formanted type of file")
+
+        else:
+            out, _ = (
+                ffmpeg.input(file, threads=0)
+                .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
+                .run(
+                    cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
+                )
+            )
+    except Exception as e:
+        raise RuntimeError(f"Failed to load audio: {e}")
+
+    if converted:
+        try:
+            os.remove(file_formanted)
+        except Exception:
+            pass
+            print("couldn't remove converted type of file")
+        converted = False
+
+    return np.frombuffer(out, np.float32).flatten()

vc_infer_pipeline.py

@@ -0,0 +1,646 @@
+import numpy as np, parselmouth, torch, pdb, sys, os
+from time import time as ttime
+import torch.nn.functional as F
+import torchcrepe  # Fork feature. Use the crepe f0 algorithm. New dependency (pip install torchcrepe)
+from torch import Tensor
+import scipy.signal as signal
+import pyworld, os, traceback, faiss, librosa, torchcrepe
+from scipy import signal
+from functools import lru_cache
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+
+bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
+
+input_audio_path2wav = {}
+
+
+@lru_cache
+def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
+    audio = input_audio_path2wav[input_audio_path]
+    f0, t = pyworld.harvest(
+        audio,
+        fs=fs,
+        f0_ceil=f0max,
+        f0_floor=f0min,
+        frame_period=frame_period,
+    )
+    f0 = pyworld.stonemask(audio, f0, t, fs)
+    return f0
+
+
+def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
+    # print(data1.max(),data2.max())
+    rms1 = librosa.feature.rms(
+        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
+    )  # 每半秒一个点
+    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
+    rms1 = torch.from_numpy(rms1)
+    rms1 = F.interpolate(
+        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.from_numpy(rms2)
+    rms2 = F.interpolate(
+        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
+    data2 *= (
+        torch.pow(rms1, torch.tensor(1 - rate))
+        * torch.pow(rms2, torch.tensor(rate - 1))
+    ).numpy()
+    return data2
+
+
+class VC(object):
+    def __init__(self, tgt_sr, config):
+        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
+            config.x_pad,
+            config.x_query,
+            config.x_center,
+            config.x_max,
+            config.is_half,
+        )
+        self.sr = 16000  # hubert输入采样率
+        self.window = 160  # 每帧点数
+        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
+        self.t_pad_tgt = tgt_sr * self.x_pad
+        self.t_pad2 = self.t_pad * 2
+        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
+        self.t_center = self.sr * self.x_center  # 查询切点位置
+        self.t_max = self.sr * self.x_max  # 免查询时长阈值
+        self.device = config.device
+
+    # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
+    def get_optimal_torch_device(self, index: int = 0) -> torch.device:
+        # Get cuda device
+        if torch.cuda.is_available():
+            return torch.device(
+                f"cuda:{index % torch.cuda.device_count()}"
+            )  # Very fast
+        elif torch.backends.mps.is_available():
+            return torch.device("mps")
+        # Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
+        # Else wise return the "cpu" as a torch device,
+        return torch.device("cpu")
+
+    # Fork Feature: Compute f0 with the crepe method
+    def get_f0_crepe_computation(
+        self,
+        x,
+        f0_min,
+        f0_max,
+        p_len,
+        hop_length=160,  # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
+        model="full",  # Either use crepe-tiny "tiny" or crepe "full". Default is full
+    ):
+        x = x.astype(
+            np.float32
+        )  # fixes the F.conv2D exception. We needed to convert double to float.
+        x /= np.quantile(np.abs(x), 0.999)
+        torch_device = self.get_optimal_torch_device()
+        audio = torch.from_numpy(x).to(torch_device, copy=True)
+        audio = torch.unsqueeze(audio, dim=0)
+        if audio.ndim == 2 and audio.shape[0] > 1:
+            audio = torch.mean(audio, dim=0, keepdim=True).detach()
+        audio = audio.detach()
+        print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
+        pitch: Tensor = torchcrepe.predict(
+            audio,
+            self.sr,
+            hop_length,
+            f0_min,
+            f0_max,
+            model,
+            batch_size=hop_length * 2,
+            device=torch_device,
+            pad=True,
+        )
+        p_len = p_len or x.shape[0] // hop_length
+        # Resize the pitch for final f0
+        source = np.array(pitch.squeeze(0).cpu().float().numpy())
+        source[source < 0.001] = np.nan
+        target = np.interp(
+            np.arange(0, len(source) * p_len, len(source)) / p_len,
+            np.arange(0, len(source)),
+            source,
+        )
+        f0 = np.nan_to_num(target)
+        return f0  # Resized f0
+
+    def get_f0_official_crepe_computation(
+        self,
+        x,
+        f0_min,
+        f0_max,
+        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()
+        return f0
+
+    # Fork Feature: Compute pYIN f0 method
+    def get_f0_pyin_computation(self, x, f0_min, f0_max):
+        y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
+        f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
+        f0 = f0[1:]  # Get rid of extra first frame
+        return f0
+
+    # Fork Feature: Acquire median hybrid f0 estimation calculation
+    def get_f0_hybrid_computation(
+        self,
+        methods_str,
+        input_audio_path,
+        x,
+        f0_min,
+        f0_max,
+        p_len,
+        filter_radius,
+        crepe_hop_length,
+        time_step,
+    ):
+        # Get various f0 methods from input to use in the computation stack
+        s = methods_str
+        s = s.split("hybrid")[1]
+        s = s.replace("[", "").replace("]", "")
+        methods = s.split("+")
+        f0_computation_stack = []
+
+        print("Calculating f0 pitch estimations for methods: %s" % str(methods))
+        x = x.astype(np.float32)
+        x /= np.quantile(np.abs(x), 0.999)
+        # Get f0 calculations for all methods specified
+        for method in methods:
+            f0 = None
+            if method == "pm":
+                f0 = (
+                    parselmouth.Sound(x, self.sr)
+                    .to_pitch_ac(
+                        time_step=time_step / 1000,
+                        voicing_threshold=0.6,
+                        pitch_floor=f0_min,
+                        pitch_ceiling=f0_max,
+                    )
+                    .selected_array["frequency"]
+                )
+                pad_size = (p_len - len(f0) + 1) // 2
+                if pad_size > 0 or p_len - len(f0) - pad_size > 0:
+                    f0 = np.pad(
+                        f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
+                    )
+            elif method == "crepe":
+                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
+                f0 = f0[1:]  # Get rid of extra first frame
+            elif method == "crepe-tiny":
+                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
+                f0 = f0[1:]  # Get rid of extra first frame
+            elif method == "mangio-crepe":
+                f0 = self.get_f0_crepe_computation(
+                    x, f0_min, f0_max, p_len, crepe_hop_length
+                )
+            elif method == "mangio-crepe-tiny":
+                f0 = self.get_f0_crepe_computation(
+                    x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
+                )
+            elif method == "harvest":
+                f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
+                if filter_radius > 2:
+                    f0 = signal.medfilt(f0, 3)
+                f0 = f0[1:]  # Get rid of first frame.
+            elif method == "dio":  # Potentially buggy?
+                f0, t = pyworld.dio(
+                    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 = f0[1:]
+            # elif method == "pyin": Not Working just yet
+            #    f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
+            # Push method to the stack
+            f0_computation_stack.append(f0)
+
+        for fc in f0_computation_stack:
+            print(len(fc))
+
+        print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
+        f0_median_hybrid = None
+        if len(f0_computation_stack) == 1:
+            f0_median_hybrid = f0_computation_stack[0]
+        else:
+            f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
+        return f0_median_hybrid
+
+    def get_f0(
+        self,
+        input_audio_path,
+        x,
+        p_len,
+        f0_up_key,
+        f0_method,
+        filter_radius,
+        crepe_hop_length,
+        inp_f0=None,
+    ):
+        global input_audio_path2wav
+        time_step = self.window / self.sr * 1000
+        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)
+        if f0_method == "pm":
+            f0 = (
+                parselmouth.Sound(x, self.sr)
+                .to_pitch_ac(
+                    time_step=time_step / 1000,
+                    voicing_threshold=0.6,
+                    pitch_floor=f0_min,
+                    pitch_ceiling=f0_max,
+                )
+                .selected_array["frequency"]
+            )
+            pad_size = (p_len - len(f0) + 1) // 2
+            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
+                f0 = np.pad(
+                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
+                )
+        elif f0_method == "harvest":
+            input_audio_path2wav[input_audio_path] = x.astype(np.double)
+            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 == "dio":  # Potentially Buggy?
+            f0, t = pyworld.dio(
+                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)
+        elif f0_method == "crepe":
+            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
+        elif f0_method == "crepe-tiny":
+            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
+        elif f0_method == "mangio-crepe":
+            f0 = self.get_f0_crepe_computation(
+                x, f0_min, f0_max, p_len, crepe_hop_length
+            )
+        elif f0_method == "mangio-crepe-tiny":
+            f0 = self.get_f0_crepe_computation(
+                x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
+            )
+        elif f0_method == "rmvpe":
+            if hasattr(self, "model_rmvpe") == False:
+                from rmvpe import RMVPE
+
+                print("loading rmvpe model")
+                self.model_rmvpe = RMVPE(
+                    "rmvpe.pt", is_half=self.is_half, device=self.device
+                )
+            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+
+        elif "hybrid" in f0_method:
+            # Perform hybrid median pitch estimation
+            input_audio_path2wav[input_audio_path] = x.astype(np.double)
+            f0 = self.get_f0_hybrid_computation(
+                f0_method,
+                input_audio_path,
+                x,
+                f0_min,
+                f0_max,
+                p_len,
+                filter_radius,
+                crepe_hop_length,
+                time_step,
+            )
+
+        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[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
+            f0[self.x_pad * tf0 : self.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 vc(
+        self,
+        model,
+        net_g,
+        sid,
+        audio0,
+        pitch,
+        pitchf,
+        times,
+        index,
+        big_npy,
+        index_rate,
+        version,
+        protect,
+    ):  # ,file_index,file_big_npy
+        feats = torch.from_numpy(audio0)
+        if self.is_half:
+            feats = feats.half()
+        else:
+            feats = feats.float()
+        if feats.dim() == 2:  # double channels
+            feats = feats.mean(-1)
+        assert feats.dim() == 1, feats.dim()
+        feats = feats.view(1, -1)
+        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
+
+        inputs = {
+            "source": feats.to(self.device),
+            "padding_mask": padding_mask,
+            "output_layer": 9 if version == "v1" else 12,
+        }
+        t0 = ttime()
+        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 and pitch != None and pitchf != None:
+            feats0 = feats.clone()
+        if (
+            isinstance(index, type(None)) == False
+            and isinstance(big_npy, type(None)) == False
+            and index_rate != 0
+        ):
+            npy = feats[0].cpu().numpy()
+            if self.is_half:
+                npy = npy.astype("float32")
+
+            # _, I = index.search(npy, 1)
+            # npy = big_npy[I.squeeze()]
+
+            score, ix = index.search(npy, k=8)
+            weight = np.square(1 / score)
+            weight /= weight.sum(axis=1, keepdims=True)
+            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
+
+            if self.is_half:
+                npy = npy.astype("float16")
+            feats = (
+                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+                + (1 - index_rate) * feats
+            )
+
+        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+        if protect < 0.5 and pitch != None and pitchf != None:
+            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:
+            p_len = feats.shape[1]
+            if pitch != None and pitchf != None:
+                pitch = pitch[:, :p_len]
+                pitchf = pitchf[:, :p_len]
+
+        if protect < 0.5 and pitch != None and pitchf != None:
+            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:
+                audio1 = (
+                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
+                    .data.cpu()
+                    .float()
+                    .numpy()
+                )
+            else:
+                audio1 = (
+                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
+                )
+        del feats, p_len, padding_mask
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        t2 = ttime()
+        times[0] += t1 - t0
+        times[2] += t2 - t1
+        return audio1
+
+    def pipeline(
+        self,
+        model,
+        net_g,
+        sid,
+        audio,
+        input_audio_path,
+        times,
+        f0_up_key,
+        f0_method,
+        file_index,
+        # file_big_npy,
+        index_rate,
+        if_f0,
+        filter_radius,
+        tgt_sr,
+        resample_sr,
+        rms_mix_rate,
+        version,
+        protect,
+        crepe_hop_length,
+        f0_file=None,
+    ):
+        if (
+            file_index != ""
+            # and file_big_npy != ""
+            # and os.path.exists(file_big_npy) == True
+            and os.path.exists(file_index) == True
+            and index_rate != 0
+        ):
+            try:
+                index = faiss.read_index(file_index)
+                # big_npy = np.load(file_big_npy)
+                big_npy = index.reconstruct_n(0, index.ntotal)
+            except:
+                traceback.print_exc()
+                index = big_npy = None
+        else:
+            index = big_npy = None
+        audio = signal.filtfilt(bh, ah, audio)
+        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
+        opt_ts = []
+        if audio_pad.shape[0] > self.t_max:
+            audio_sum = np.zeros_like(audio)
+            for i in range(self.window):
+                audio_sum += audio_pad[i : i - self.window]
+            for t in range(self.t_center, audio.shape[0], self.t_center):
+                opt_ts.append(
+                    t
+                    - self.t_query
+                    + np.where(
+                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
+                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
+                    )[0][0]
+                )
+        s = 0
+        audio_opt = []
+        t = None
+        t1 = ttime()
+        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
+        p_len = audio_pad.shape[0] // self.window
+        inp_f0 = None
+        if hasattr(f0_file, "name") == True:
+            try:
+                with open(f0_file.name, "r") as f:
+                    lines = f.read().strip("\n").split("\n")
+                inp_f0 = []
+                for line in lines:
+                    inp_f0.append([float(i) for i in line.split(",")])
+                inp_f0 = np.array(inp_f0, dtype="float32")
+            except:
+                traceback.print_exc()
+        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
+        pitch, pitchf = None, None
+        if if_f0 == 1:
+            pitch, pitchf = self.get_f0(
+                input_audio_path,
+                audio_pad,
+                p_len,
+                f0_up_key,
+                f0_method,
+                filter_radius,
+                crepe_hop_length,
+                inp_f0,
+            )
+            pitch = pitch[:p_len]
+            pitchf = pitchf[:p_len]
+            if self.device == "mps":
+                pitchf = pitchf.astype(np.float32)
+            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
+            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
+        t2 = ttime()
+        times[1] += t2 - t1
+        for t in opt_ts:
+            t = t // self.window * self.window
+            if if_f0 == 1:
+                audio_opt.append(
+                    self.vc(
+                        model,
+                        net_g,
+                        sid,
+                        audio_pad[s : t + self.t_pad2 + self.window],
+                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
+                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
+                        times,
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt : -self.t_pad_tgt]
+                )
+            else:
+                audio_opt.append(
+                    self.vc(
+                        model,
+                        net_g,
+                        sid,
+                        audio_pad[s : t + self.t_pad2 + self.window],
+                        None,
+                        None,
+                        times,
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt : -self.t_pad_tgt]
+                )
+            s = t
+        if if_f0 == 1:
+            audio_opt.append(
+                self.vc(
+                    model,
+                    net_g,
+                    sid,
+                    audio_pad[t:],
+                    pitch[:, t // self.window :] if t is not None else pitch,
+                    pitchf[:, t // self.window :] if t is not None else pitchf,
+                    times,
+                    index,
+                    big_npy,
+                    index_rate,
+                    version,
+                    protect,
+                )[self.t_pad_tgt : -self.t_pad_tgt]
+            )
+        else:
+            audio_opt.append(
+                self.vc(
+                    model,
+                    net_g,
+                    sid,
+                    audio_pad[t:],
+                    None,
+                    None,
+                    times,
+                    index,
+                    big_npy,
+                    index_rate,
+                    version,
+                    protect,
+                )[self.t_pad_tgt : -self.t_pad_tgt]
+            )
+        audio_opt = np.concatenate(audio_opt)
+        if rms_mix_rate != 1:
+            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
+        if resample_sr >= 16000 and tgt_sr != resample_sr:
+            audio_opt = librosa.resample(
+                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
+            )
+        audio_max = np.abs(audio_opt).max() / 0.99
+        max_int16 = 32768
+        if audio_max > 1:
+            max_int16 /= audio_max
+        audio_opt = (audio_opt * max_int16).astype(np.int16)
+        del pitch, pitchf, sid
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio_opt