import sys from shutil import rmtree import shutil import json # Mangio fork using json for preset saving import datetime import yt_dlp import unicodedata from glob import glob1 from signal import SIGTERM import os now_dir = os.getcwd() sys.path.append(now_dir) import lib.globals.globals as rvc_globals from LazyImport import lazyload math = lazyload('math') import traceback import warnings tensorlowest = lazyload('tensorlowest') import faiss ffmpeg = lazyload('ffmpeg') np = lazyload("numpy") torch = lazyload('torch') re = lazyload('regex') os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' os.environ["OPENBLAS_NUM_THREADS"] = "1" os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" import logging from random import shuffle from subprocess import Popen import easy_infer gr = lazyload("gradio") SF = lazyload("soundfile") SFWrite = SF.write from config import Config from fairseq import checkpoint_utils from i18n import I18nAuto from lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM from infer_uvr5 import _audio_pre_, _audio_pre_new from MDXNet import MDXNetDereverb from my_utils import load_audio from train.process_ckpt import change_info, extract_small_model, merge, show_info from vc_infer_pipeline import VC from sklearn.cluster import MiniBatchKMeans import time import threading from shlex import quote as SQuote RQuote = lambda val: SQuote(str(val)) tmp = os.path.join(now_dir, "TEMP") runtime_dir = os.path.join(now_dir, "runtime/Lib/site-packages") directories = ['logs', 'audios', 'datasets', 'weights'] rmtree(tmp, ignore_errors=True) rmtree(os.path.join(runtime_dir, "infer_pack"), ignore_errors=True) rmtree(os.path.join(runtime_dir, "uvr5_pack"), ignore_errors=True) os.makedirs(tmp, exist_ok=True) for folder in directories: os.makedirs(os.path.join(now_dir, folder), exist_ok=True) os.environ["TEMP"] = tmp warnings.filterwarnings("ignore") torch.manual_seed(114514) logging.getLogger("numba").setLevel(logging.WARNING) try: file = open('csvdb/stop.csv', 'x') file.close() except FileExistsError: pass global DoFormant, Quefrency, Timbre DoFormant = rvc_globals.DoFormant Quefrency = rvc_globals.Quefrency Timbre = rvc_globals.Timbre config = Config() 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_gpu_ok = False keywords = ["10", "16", "20", "30", "40", "A2", "A3", "A4", "P4", "A50", "500", "A60", "70", "80", "90", "M4", "T4", "TITAN"] if torch.cuda.is_available() or ngpu != 0: for i in range(ngpu): gpu_name = torch.cuda.get_device_name(i).upper() if any(keyword in gpu_name for keyword in keywords): 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 / 1e9 + 0.4)) gpu_info = "\n".join(gpu_infos) if if_gpu_ok and gpu_infos else i18n("很遗憾您这没有能用的显卡来支持您训练") default_batch_size = min(mem) // 2 if if_gpu_ok and gpu_infos else 1 gpus = "-".join(i[0] for i in gpu_infos) 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].to(config.device) if config.is_half: hubert_model = hubert_model.half() hubert_model.eval() datasets_root = "datasets" weight_root = "weights" weight_uvr5_root = "uvr5_weights" index_root = "logs" fshift_root = "formantshiftcfg" audio_root = "audios" sup_audioext = {'wav', 'mp3', 'flac', 'ogg', 'opus', 'm4a', 'mp4', 'aac', 'alac', 'wma', 'aiff', 'webm', 'ac3'} names = [os.path.join(root, file) for root, _, files in os.walk(weight_root) for file in files if file.endswith((".pth", ".onnx"))] indexes_list = [os.path.join(root, name) for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name] audio_paths = [os.path.join(root, name) for root, _, files in os.walk(audio_root, topdown=False) for name in files if name.endswith(tuple(sup_audioext))] uvr5_names = [name.replace(".pth", "") for name in os.listdir(weight_uvr5_root) if name.endswith(".pth") or "onnx" in name] check_for_name = lambda: sorted(names)[0] if names else '' datasets=[] for foldername in os.listdir(os.path.join(now_dir, datasets_root)): if "." not in foldername: datasets.append(os.path.join(easy_infer.find_folder_parent(".","pretrained"),"datasets",foldername)) def get_dataset(): if len(datasets) > 0: return sorted(datasets)[0] else: return '' def update_dataset_list(name): new_datasets = [] for foldername in os.listdir(os.path.join(now_dir, datasets_root)): if "." not in foldername: new_datasets.append(os.path.join(easy_infer.find_folder_parent(".","pretrained"),"datasets",foldername)) return gr.Dropdown.update(choices=new_datasets) def get_indexes(): indexes_list = [ os.path.join(dirpath, filename) for dirpath, _, filenames in os.walk(index_root) for filename in filenames if filename.endswith(".index") and "trained" not in filename ] return indexes_list if indexes_list else '' def get_fshift_presets(): fshift_presets_list = [ os.path.join(dirpath, filename) for dirpath, _, filenames in os.walk(fshift_root) for filename in filenames if filename.endswith(".txt") ] return fshift_presets_list if fshift_presets_list else '' def vc_single( sid: str, input_audio_path0: str, input_audio_path1: str, f0_up_key: int, f0_file: str, f0_method: str, file_index: str, file_index2: str, index_rate: float, filter_radius: int, resample_sr: int, rms_mix_rate: float, protect: float, crepe_hop_length: int, f0_min: int, note_min: str, f0_max: int, note_max: str, ): global total_time total_time = 0 start_time = time.time() global tgt_sr, net_g, vc, hubert_model, version if not input_audio_path0 and not input_audio_path1: return "You need to upload an audio", None if (not os.path.exists(input_audio_path0)) and (not os.path.exists(os.path.join(now_dir, input_audio_path0))): return "Audio was not properly selected or doesn't exist", None # This might be jank, but I'm trying to make sure this gets the right file... input_audio_path1 = input_audio_path1 or input_audio_path0 print(f"\nStarting inference for '{os.path.basename(input_audio_path1)}'") print("-------------------") f0_up_key = int(f0_up_key) if rvc_globals.NotesOrHertz and f0_method != 'rmvpe': f0_min = note_to_hz(note_min) if note_min else 50 f0_max = note_to_hz(note_max) if note_max else 1100 print(f"Converted min pitch freq - {f0_min}\n" f"Converted max pitch freq - {f0_max}") else: f0_min = f0_min or 50 f0_max = f0_max or 1100 try: print(f"Attempting to load {input_audio_path1}....") audio = load_audio(input_audio_path1, 16000, DoFormant=rvc_globals.DoFormant, Quefrency=rvc_globals.Quefrency, Timbre=rvc_globals.Timbre) audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max times = [0, 0, 0] if not hubert_model: print("Loading HuBERT for the first time...") load_hubert() try: if_f0 = cpt.get("f0", 1) except NameError: message = "Model was not properly selected" print(message) return message, None file_index = ( file_index.strip(" ").strip('"').strip("\n").strip('"').strip(" ").replace("trained", "added") ) if file_index != "" else file_index2 try: audio_opt = vc.pipeline( hubert_model, net_g, sid, audio, input_audio_path1, times, f0_up_key, f0_method, file_index, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, crepe_hop_length, f0_file=f0_file, f0_min=f0_min, f0_max=f0_max ) except AssertionError: message = "Mismatching index version detected (v1 with v2, or v2 with v1)." print(message) return message, None except NameError: message = "RVC libraries are still loading. Please try again in a few seconds." print(message) return message, None if tgt_sr != resample_sr >= 16000: tgt_sr = resample_sr index_info = "Using index:%s." % file_index if os.path.exists(file_index) else "Index not used." end_time = time.time() total_time = end_time - start_time return f"Success.\n {index_info}\nTime:\n npy:{times[0]}, f0:{times[1]}, infer:{times[2]}\nTotal Time: {total_time} seconds", (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, f0_min, note_min, f0_max, note_max, ): if rvc_globals.NotesOrHertz and f0_method != 'rmvpe': f0_min = note_to_hz(note_min) if note_min else 50 f0_max = note_to_hz(note_max) if note_max else 1100 print(f"Converted min pitch freq - {f0_min}\n" f"Converted max pitch freq - {f0_max}") else: f0_min = f0_min or 50 f0_max = f0_max or 1100 try: dir_path, opt_root = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [dir_path, opt_root]] os.makedirs(opt_root, exist_ok=True) paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)] if dir_path else [path.name for path in paths] infos = [] for path in paths: info, opt = vc_single(sid, path, None, f0_up_key, None, f0_method, file_index, file_index2, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, crepe_hop_length, f0_min, note_min, f0_max, note_max) if "Success" in info: try: tgt_sr, audio_opt = opt #sys.stdout.write(f"\nTarget Sample Rate (tgt_sr): {tgt_sr}") # Debugging print base_name = os.path.splitext(os.path.basename(path))[0] output_path = f"{opt_root}/{base_name}.{format1}" path, extension = output_path, format1 path, extension = output_path if format1 in ["wav", "flac", "mp3", "ogg", "aac", "m4a"] else f"{output_path}.wav", format1 #sys.stdout.write(f"\nOutput Path: {path}") # Debugging print #sys.stdout.write(f"\nFile Extension: {extension}") # Debugging print SFWrite(path, audio_opt, tgt_sr) #sys.stdout.write("\nFile Written Successfully with SFWrite") # Debugging print if os.path.exists(path) and extension not in ["wav", "flac", "mp3", "ogg", "aac", "m4a"]: sys.stdout.write(f"Running command: ffmpeg -i {RQuote(path)} -vn {RQuote(path[:-4] + '.' + extension)} -q:a 2 -y") os.system(f"ffmpeg -i {RQuote(path)} -vn {RQuote(path[:-4] + '.' + extension)} -q:a 2 -y") #print(f"\nFile Converted to {extension} using ffmpeg") # Debugging print except: info += traceback.format_exc() print(f"\nException encountered: {info}") # Debugging print infos.append(f"{os.path.basename(path)}->{info}") yield "\n".join(infos) yield "\n".join(infos) except: yield traceback.format_exc() def uvr(input_url, output_path, model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): def format_title(title): formatted_title = title.replace(" ", "_") return formatted_title ydl_opts = { 'no-windows-filenames': True, 'restrict-filenames': True, 'extract_audio': True, 'format': 'bestaudio', } with yt_dlp.YoutubeDL(ydl_opts) as ydl: info_dict = ydl.extract_info(input_url, download=False) formatted_title = format_title(info_dict.get('title', 'default_title')) formatted_outtmpl = output_path + '/' + formatted_title + '.wav' ydl_opts['outtmpl'] = formatted_outtmpl ydl = yt_dlp.YoutubeDL(ydl_opts) ydl.download([input_url]) infos = [] try: inp_root, save_root_vocal, save_root_ins = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [inp_root, save_root_vocal, save_root_ins]] pre_fun = MDXNetDereverb(15) if model_name == "onnx_dereverb_By_FoxJoy" else (_audio_pre_ if "DeEcho" not in model_name else _audio_pre_new)( agg=int(agg), model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), device=config.device, is_half=config.is_half, ) paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] if inp_root else [path.name for path in paths] for path in paths: inp_path = os.path.join(inp_root, path) need_reformat, done = 1, 0 try: info = ffmpeg.probe(inp_path, cmd="ffprobe") if info["streams"][0]["channels"] == 2 and info["streams"][0]["sample_rate"] == "44100": need_reformat = 0 pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0) done = 1 except: traceback.print_exc() if need_reformat: tmp_path = f"{tmp}/{os.path.basename(RQuote(inp_path))}.reformatted.wav" os.system(f"ffmpeg -i {RQuote(inp_path)} -vn -acodec pcm_s16le -ac 2 -ar 44100 {RQuote(tmp_path)} -y") inp_path = tmp_path try: if not done: pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal, format0) infos.append(f"{os.path.basename(inp_path)}->Success") yield "\n".join(infos) except: infos.append(f"{os.path.basename(inp_path)}->{traceback.format_exc()}") yield "\n".join(infos) except: infos.append(traceback.format_exc()) yield "\n".join(infos) finally: try: if model_name == "onnx_dereverb_By_FoxJoy": del pre_fun.pred.model del pre_fun.pred.model_ else: del pre_fun.model del pre_fun except: traceback.print_exc() print("clean_empty_cache") if torch.cuda.is_available(): torch.cuda.empty_cache() yield "\n".join(infos) def get_vc(sid, to_return_protect0, to_return_protect1): global n_spk, tgt_sr, net_g, vc, cpt, version, hubert_model if not sid: if hubert_model is not None: print("clean_empty_cache") del net_g, n_spk, vc, hubert_model, tgt_sr hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None if torch.cuda.is_available(): torch.cuda.empty_cache() if_f0, version = cpt.get("f0", 1), cpt.get("version", "v1") net_g = (SynthesizerTrnMs256NSFsid if version == "v1" else SynthesizerTrnMs768NSFsid)( *cpt["config"], is_half=config.is_half) if if_f0 == 1 else (SynthesizerTrnMs256NSFsid_nono if version == "v1" else SynthesizerTrnMs768NSFsid_nono)(*cpt["config"]) del net_g, cpt if torch.cuda.is_available(): torch.cuda.empty_cache() cpt = None return ({"visible": False, "__type__": "update"},) * 3 print(f"loading {sid}") cpt = torch.load(sid, map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] if cpt.get("f0", 1) == 0: to_return_protect0 = to_return_protect1 = {"visible": False, "value": 0.5, "__type__": "update"} else: to_return_protect0 = {"visible": True, "value": to_return_protect0, "__type__": "update"} to_return_protect1 = {"visible": True, "value": to_return_protect1, "__type__": "update"} version = cpt.get("version", "v1") net_g = (SynthesizerTrnMs256NSFsid if version == "v1" else SynthesizerTrnMs768NSFsid)( *cpt["config"], is_half=config.is_half) if cpt.get("f0", 1) == 1 else (SynthesizerTrnMs256NSFsid_nono if version == "v1" else 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) net_g = net_g.half() if config.is_half else net_g.float() vc = VC(tgt_sr, config) n_spk = cpt["config"][-3] return ( {"visible": False, "maximum": n_spk, "__type__": "update"}, to_return_protect0, to_return_protect1 ) def change_choices(): names = [os.path.join(root, file) for root, _, files in os.walk(weight_root) for file in files if file.endswith((".pth", ".onnx"))] indexes_list = [os.path.join(root, name) for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name] audio_paths = [os.path.join(audio_root, file) for file in os.listdir(os.path.join(now_dir, "audios"))] return ( {"choices": sorted(names), "__type__": "update"}, {"choices": sorted(indexes_list), "__type__": "update"}, {"choices": sorted(audio_paths), "__type__": "update"} ) sr_dict = { "32k": 32000, "40k": 40000, "48k": 48000, } def if_done(done, p): while p.poll() is None: time.sleep(0.5) done[0] = True def if_done_multi(done, ps): while not all(p.poll() is not None for p in ps): time.sleep(0.5) done[0] = True def formant_enabled(cbox, qfrency, tmbre): global DoFormant, Quefrency, Timbre DoFormant = cbox Quefrency = qfrency Timbre = tmbre rvc_globals.DoFormant = cbox rvc_globals.Quefrency = qfrency rvc_globals.Timbre = tmbre visibility_update = {"visible": DoFormant, "__type__": "update"} return ( {"value": DoFormant, "__type__": "update"}, ) + (visibility_update,) * 6 def formant_apply(qfrency, tmbre): global Quefrency, Timbre, DoFormant Quefrency = qfrency Timbre = tmbre DoFormant = True rvc_globals.DoFormant = True rvc_globals.Quefrency = qfrency rvc_globals.Timbre = tmbre return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"}) def update_fshift_presets(preset, qfrency, tmbre): if preset: with open(preset, 'r') as p: content = p.readlines() qfrency, tmbre = content[0].strip(), content[1] formant_apply(qfrency, tmbre) else: qfrency, tmbre = preset_apply(preset, qfrency, tmbre) return ( {"choices": get_fshift_presets(), "__type__": "update"}, {"value": qfrency, "__type__": "update"}, {"value": tmbre, "__type__": "update"}, ) def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): sr = sr_dict[sr] log_dir = os.path.join(now_dir, "logs", exp_dir) log_file = os.path.join(log_dir, "preprocess.log") os.makedirs(log_dir, exist_ok=True) with open(log_file, "w") as f: pass cmd = ( f"{config.python_cmd} " "trainset_preprocess_pipeline_print.py " f"{trainset_dir} " f"{RQuote(sr)} " f"{RQuote(n_p)} " f"{log_dir} " f"{RQuote(config.noparallel)}" ) print(cmd) p = Popen(cmd, shell=True) done = [False] threading.Thread(target=if_done, args=(done,p,)).start() while not done[0]: with open(log_file, "r") as f: yield f.read() time.sleep(1) with open(log_file, "r") as f: log = f.read() print(log) yield log def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): gpus = gpus.split("-") log_dir = f"{now_dir}/logs/{exp_dir}" log_file = f"{log_dir}/extract_f0_feature.log" os.makedirs(log_dir, exist_ok=True) with open(log_file, "w") as f: pass if if_f0: cmd = ( f"{config.python_cmd} extract_f0_print.py {log_dir} " f"{RQuote(n_p)} {RQuote(f0method)} {RQuote(echl)}" ) print(cmd) p = Popen(cmd, shell=True, cwd=now_dir) done = [False] threading.Thread(target=if_done, args=(done, p)).start() while not done[0]: with open(log_file, "r") as f: yield f.read() time.sleep(1) leng = len(gpus) ps = [] for idx, n_g in enumerate(gpus): cmd = ( f"{config.python_cmd} extract_feature_print.py {RQuote(config.device)} " f"{RQuote(leng)} {RQuote(idx)} {RQuote(n_g)} {log_dir} {RQuote(version19)}" ) print(cmd) p = Popen(cmd, shell=True, cwd=now_dir) ps.append(p) done = [False] threading.Thread(target=if_done_multi, args=(done, ps)).start() while not done[0]: with open(log_file, "r") as f: yield f.read() time.sleep(1) with open(log_file, "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 "" model_paths = {"G": "", "D": ""} for model_type in model_paths: file_path = f"pretrained{path_str}/{f0_str}{model_type}{sr2}.pth" if os.access(file_path, os.F_OK): model_paths[model_type] = file_path else: print(f"{file_path} doesn't exist, will not use pretrained model.") return (model_paths["G"], model_paths["D"]) def change_version19(sr2, if_f0_3, version19): path_str = "" if version19 == "v1" else "_v2" sr2 = "40k" if (sr2 == "32k" and version19 == "v1") else sr2 choices_update = { "choices": ["40k", "48k"], "__type__": "update", "value": sr2 } if version19 == "v1" else { "choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2} f0_str = "f0" if if_f0_3 else "" model_paths = {"G": "", "D": ""} for model_type in model_paths: file_path = f"pretrained{path_str}/{f0_str}{model_type}{sr2}.pth" if os.access(file_path, os.F_OK): model_paths[model_type] = file_path else: print(f"{file_path} doesn't exist, will not use pretrained model.") return (model_paths["G"], model_paths["D"], choices_update) def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 path_str = "" if version19 == "v1" else "_v2" pth_format = "pretrained%s/f0%s%s.pth" model_desc = { "G": "", "D": "" } for model_type in model_desc: file_path = pth_format % (path_str, model_type, sr2) if os.access(file_path, os.F_OK): model_desc[model_type] = file_path else: print(file_path, "doesn't exist, will not use pretrained model") return ( {"visible": if_f0_3, "__type__": "update"}, model_desc["G"], model_desc["D"], {"visible": if_f0_3, "__type__": "update"} ) 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.isdir(folder_path): wav_files_num = len(glob1(folder_path,"*.wav")) if wav_files_num > 0: log_interval = math.ceil(wav_files_num / batch_size12) if log_interval > 1: log_interval += 1 return log_interval global PID, PROCESS 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, ): with open('csvdb/stop.csv', 'w+') as file: file.write("False") log_dir = os.path.join(now_dir, "logs", exp_dir1) os.makedirs(log_dir, exist_ok=True) gt_wavs_dir = os.path.join(log_dir, "0_gt_wavs") feature_dim = "256" if version19 == "v1" else "768" feature_dir = os.path.join(log_dir, f"3_feature{feature_dim}") log_interval = set_log_interval(log_dir, batch_size12) required_dirs = [gt_wavs_dir, feature_dir] if if_f0_3: f0_dir = f"{log_dir}/2a_f0" f0nsf_dir = f"{log_dir}/2b-f0nsf" required_dirs.extend([f0_dir, f0nsf_dir]) names = set(name.split(".")[0] for directory in required_dirs for name in os.listdir(directory)) def generate_paths(name): paths = [gt_wavs_dir, feature_dir] if if_f0_3: paths.extend([f0_dir, f0nsf_dir]) return '|'.join([path.replace('\\', '\\\\') + '/' + name + ('.wav.npy' if path in [f0_dir, f0nsf_dir] else '.wav' if path == gt_wavs_dir else '.npy') for path in paths]) opt = [f"{generate_paths(name)}|{spk_id5}" for name in names] mute_dir = f"{now_dir}/logs/mute" for _ in range(2): mute_string = f"{mute_dir}/0_gt_wavs/mute{sr2}.wav|{mute_dir}/3_feature{feature_dim}/mute.npy" if if_f0_3: mute_string += f"|{mute_dir}/2a_f0/mute.wav.npy|{mute_dir}/2b-f0nsf/mute.wav.npy" opt.append(mute_string+f"|{spk_id5}") shuffle(opt) with open(f"{log_dir}/filelist.txt", "w") as f: f.write("\n".join(opt)) print("write filelist done") print("use gpus:", gpus16) if pretrained_G14 == "": print("no pretrained Generator") if pretrained_D15 == "": print("no pretrained Discriminator") G_train = f"-pg {pretrained_G14}" if pretrained_G14 else "" D_train = f"-pd {pretrained_D15}" if pretrained_D15 else "" cmd = ( f"{config.python_cmd} train_nsf_sim_cache_sid_load_pretrain.py -e {exp_dir1} -sr {sr2} -f0 {int(if_f0_3)} -bs {batch_size12}" f" -g {gpus16 if gpus16 is not None else ''} -te {total_epoch11} -se {save_epoch10} {G_train} {D_train} -l {int(if_save_latest13)}" f" -c {int(if_cache_gpu17)} -sw {int(if_save_every_weights18)} -v {version19} -li {log_interval}" ) print(cmd) global p p = Popen(cmd, shell=True, cwd=now_dir) global PID PID = p.pid p.wait() return "Training is done, check train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"} def train_index(exp_dir1, version19): exp_dir = os.path.join(now_dir, 'logs', exp_dir1) os.makedirs(exp_dir, exist_ok=True) feature_dim = '256' if version19 == "v1" else '768' feature_dir = os.path.join(exp_dir, f"3_feature{feature_dim}") if not os.path.exists(feature_dir) or len(os.listdir(feature_dir)) == 0: return "请先进行特征提取!" npys = [np.load(os.path.join(feature_dir, name)) for name in sorted(os.listdir(feature_dir))] big_npy = np.concatenate(npys, 0) np.random.shuffle(big_npy) infos = [] if big_npy.shape[0] > 2*10**5: infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) yield "\n".join(infos) try: big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False,init="random").fit(big_npy).cluster_centers_ except Exception as e: infos.append(str(e)) yield "\n".join(infos) np.save(os.path.join(exp_dir, "total_fea.npy"), big_npy) n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) infos.append("%s,%s" % (big_npy.shape, n_ivf)) yield "\n".join(infos) index = faiss.index_factory(int(feature_dim), f"IVF{n_ivf},Flat") index_ivf = faiss.extract_index_ivf(index) index_ivf.nprobe = 1 index.train(big_npy) index_file_base = f"{exp_dir}/trained_IVF{n_ivf}_Flat_nprobe_{index_ivf.nprobe}_{exp_dir1}_{version19}.index" faiss.write_index(index, index_file_base) 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]) index_file_base = f"{exp_dir}/added_IVF{n_ivf}_Flat_nprobe_{index_ivf.nprobe}_{exp_dir1}_{version19}.index" faiss.write_index(index, index_file_base) infos.append(f"Successful Index Construction，added_IVF{n_ivf}_Flat_nprobe_{index_ivf.nprobe}_{exp_dir1}_{version19}.index") yield "\n".join(infos) #def setBoolean(status): #true to false and vice versa / not implemented yet, dont touch!!!!!!! # status = not status # return status def change_info_(ckpt_path): train_log_path = os.path.join(os.path.dirname(ckpt_path), "train.log") if not os.path.exists(train_log_path): return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} try: with open(train_log_path, "r") as f: info_line = next(f).strip() info = eval(info_line.split("\t")[-1]) sr, f0 = info.get("sample_rate"), info.get("if_f0") version = "v2" if info.get("version") == "v2" else "v1" return sr, str(f0), version except Exception as e: print(f"Exception occurred: {str(e)}, Traceback: {traceback.format_exc()}") return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} def export_onnx(model_path, exported_path): device = torch.device("cpu") checkpoint = torch.load(model_path, map_location=device) vec_channels = 256 if checkpoint.get("version", "v1") == "v1" else 768 test_inputs = { "phone": torch.rand(1, 200, vec_channels), "phone_lengths": torch.LongTensor([200]), "pitch": torch.randint(5, 255, (1, 200)), "pitchf": torch.rand(1, 200), "ds": torch.zeros(1).long(), "rnd": torch.rand(1, 192, 200) } checkpoint["config"][-3] = checkpoint["weight"]["emb_g.weight"].shape[0] net_g = SynthesizerTrnMsNSFsidM(*checkpoint["config"], is_half=False, version=checkpoint.get("version", "v1")) net_g.load_state_dict(checkpoint["weight"], strict=False) net_g = net_g.to(device) dynamic_axes = {"phone": [1], "pitch": [1], "pitchf": [1], "rnd": [2]} torch.onnx.export( net_g, tuple(value.to(device) for value in test_inputs.values()), exported_path, dynamic_axes=dynamic_axes, do_constant_folding=False, opset_version=13, verbose=False, input_names=list(test_inputs.keys()), output_names=["audio"], ) return "Finished" #region Mangio-RVC-Fork CLI App import scipy.io.wavfile as wavfile cli_current_page = "HOME" def cli_split_command(com): exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)' split_array = re.findall(exp, com) split_array = [group[0] if group[0] else group[1] for group in split_array] return split_array execute_generator_function = lambda genObject: all(x is not None for x in genObject) def cli_infer(com): model_name, source_audio_path, output_file_name, feature_index_path, speaker_id, transposition, f0_method, crepe_hop_length, harvest_median_filter, resample, mix, feature_ratio, protection_amnt, _, f0_min, f0_max, do_formant = cli_split_command(com)[:17] speaker_id, crepe_hop_length, harvest_median_filter, resample = map(int, [speaker_id, crepe_hop_length, harvest_median_filter, resample]) transposition, mix, feature_ratio, protection_amnt = map(float, [transposition, mix, feature_ratio, protection_amnt]) if do_formant.lower() == 'false': Quefrency = 1.0 Timbre = 1.0 else: Quefrency, Timbre = map(float, cli_split_command(com)[17:19]) rvc_globals.DoFormant = do_formant.lower() == 'true' rvc_globals.Quefrency = Quefrency rvc_globals.Timbre = Timbre output_message = 'Mangio-RVC-Fork Infer-CLI:' output_path = f'audio-outputs/{output_file_name}' print(f"{output_message} Starting the inference...") vc_data = get_vc(model_name, protection_amnt, protection_amnt) print(vc_data) print(f"{output_message} Performing inference...") conversion_data = vc_single( speaker_id, source_audio_path, source_audio_path, transposition, None, # f0 file support not implemented f0_method, feature_index_path, feature_index_path, feature_ratio, harvest_median_filter, resample, mix, protection_amnt, crepe_hop_length, f0_min=f0_min, note_min=None, f0_max=f0_max, note_max=None ) if "Success." in conversion_data[0]: print(f"{output_message} Inference succeeded. Writing to {output_path}...") wavfile.write(output_path, conversion_data[1][0], conversion_data[1][1]) print(f"{output_message} Finished! Saved output to {output_path}") else: print(f"{output_message} Inference failed. Here's the traceback: {conversion_data[0]}") def cli_pre_process(com): print("Mangio-RVC-Fork Pre-process: Starting...") execute_generator_function( preprocess_dataset( *cli_split_command(com)[:3], int(cli_split_command(com)[3]) ) ) print("Mangio-RVC-Fork Pre-process: Finished") def cli_extract_feature(com): model_name, gpus, num_processes, has_pitch_guidance, f0_method, crepe_hop_length, version = cli_split_command(com) num_processes = int(num_processes) has_pitch_guidance = bool(int(has_pitch_guidance)) crepe_hop_length = int(crepe_hop_length) print( f"Mangio-RVC-CLI: Extract Feature Has Pitch: {has_pitch_guidance}" f"Mangio-RVC-CLI: Extract Feature Version: {version}" "Mangio-RVC-Fork Feature Extraction: Starting..." ) generator = extract_f0_feature( gpus, num_processes, f0_method, has_pitch_guidance, model_name, version, crepe_hop_length ) execute_generator_function(generator) print("Mangio-RVC-Fork Feature Extraction: Finished") def cli_train(com): com = cli_split_command(com) model_name = com[0] sample_rate = com[1] bool_flags = [bool(int(i)) for i in com[2:11]] version = com[11] pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/" g_pretrained_path = f"{pretrained_base}f0G{sample_rate}.pth" d_pretrained_path = f"{pretrained_base}f0D{sample_rate}.pth" print("Mangio-RVC-Fork Train-CLI: Training...") click_train(model_name, sample_rate, *bool_flags, g_pretrained_path, d_pretrained_path, version) def cli_train_feature(com): output_message = 'Mangio-RVC-Fork Train Feature Index-CLI' print(f"{output_message}: Training... Please wait") execute_generator_function(train_index(*cli_split_command(com))) print(f"{output_message}: Done!") def cli_extract_model(com): extract_small_model_process = extract_small_model(*cli_split_command(com)) print( "Mangio-RVC-Fork Extract Small Model: Success!" if extract_small_model_process == "Success." else f"{extract_small_model_process}\nMangio-RVC-Fork Extract Small Model: Failed!" ) def preset_apply(preset, qfer, tmbr): if preset: try: with open(preset, 'r') as p: content = p.read().splitlines() qfer, tmbr = content[0], content[1] formant_apply(qfer, tmbr) except IndexError: print("Error: File does not have enough lines to read 'qfer' and 'tmbr'") except FileNotFoundError: print("Error: File does not exist") except Exception as e: print("An unexpected error occurred", e) return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) def print_page_details(): page_description = { 'HOME': "\n go home : Takes you back to home with a navigation list." "\n go infer : Takes you to inference command execution." "\n go pre-process : Takes you to training step.1) pre-process command execution." "\n go extract-feature : Takes you to training step.2) extract-feature command execution." "\n go train : Takes you to training step.3) being or continue training command execution." "\n go train-feature : Takes you to the train feature index command execution." "\n go extract-model : Takes you to the extract small model command execution." , 'INFER': "\n arg 1) model name with .pth in ./weights: mi-test.pth" "\n arg 2) source audio path: myFolder\\MySource.wav" "\n arg 3) output file name to be placed in './audio-outputs': MyTest.wav" "\n arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index" "\n arg 5) speaker id: 0" "\n arg 6) transposition: 0" "\n arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny, rmvpe)" "\n arg 8) crepe hop length: 160" "\n arg 9) harvest median filter radius: 3 (0-7)" "\n arg 10) post resample rate: 0" "\n arg 11) mix volume envelope: 1" "\n arg 12) feature index ratio: 0.78 (0-1)" "\n arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.)" "\n arg 14) Whether to formant shift the inference audio before conversion: False (if set to false, you can ignore setting the quefrency and timbre values for formanting)" "\n arg 15)* Quefrency for formanting: 8.0 (no need to set if arg14 is False/false)" "\n arg 16)* Timbre for formanting: 1.2 (no need to set if arg14 is False/false) \n" "\nExample: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33 0.45 True 8.0 1.2" , 'PRE-PROCESS': "\n arg 1) Model folder name in ./logs: mi-test" "\n arg 2) Trainset directory: mydataset (or) E:\\my-data-set" "\n arg 3) Sample rate: 40k (32k, 40k, 48k)" "\n arg 4) Number of CPU threads to use: 8 \n" "\nExample: mi-test mydataset 40k 24" , 'EXTRACT-FEATURE': "\n arg 1) Model folder name in ./logs: mi-test" "\n arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)" "\n arg 3) Number of CPU threads to use: 8" "\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)" "\n arg 5) f0 Method: harvest (pm, harvest, dio, crepe)" "\n arg 6) Crepe hop length: 128" "\n arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n" "\nExample: mi-test 0 24 1 harvest 128 v2" , 'TRAIN': "\n arg 1) Model folder name in ./logs: mi-test" "\n arg 2) Sample rate: 40k (32k, 40k, 48k)" "\n arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)" "\n arg 4) speaker id: 0" "\n arg 5) Save epoch iteration: 50" "\n arg 6) Total epochs: 10000" "\n arg 7) Batch size: 8" "\n arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)" "\n arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)" "\n arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)" "\n arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)" "\n arg 12) Model architecture version: v2 (use either v1 or v2)\n" "\nExample: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2" , 'TRAIN-FEATURE': "\n arg 1) Model folder name in ./logs: mi-test" "\n arg 2) Model architecture version: v2 (use either v1 or v2)\n" "\nExample: mi-test v2" , 'EXTRACT-MODEL': "\n arg 1) Model Path: logs/mi-test/G_168000.pth" "\n arg 2) Model save name: MyModel" "\n arg 3) Sample rate: 40k (32k, 40k, 48k)" "\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)" '\n arg 5) Model information: "My Model"' "\n arg 6) Model architecture version: v2 (use either v1 or v2)\n" '\nExample: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2' } print(page_description.get(cli_current_page, 'Invalid page')) def change_page(page): global cli_current_page cli_current_page = page return 0 def execute_command(com): command_to_page = { "go home": "HOME", "go infer": "INFER", "go pre-process": "PRE-PROCESS", "go extract-feature": "EXTRACT-FEATURE", "go train": "TRAIN", "go train-feature": "TRAIN-FEATURE", "go extract-model": "EXTRACT-MODEL", } page_to_function = { "INFER": cli_infer, "PRE-PROCESS": cli_pre_process, "EXTRACT-FEATURE": cli_extract_feature, "TRAIN": cli_train, "TRAIN-FEATURE": cli_train_feature, "EXTRACT-MODEL": cli_extract_model, } if com in command_to_page: return change_page(command_to_page[com]) if com[:3] == "go ": print(f"page '{com[3:]}' does not exist!") return 0 if cli_current_page in page_to_function: page_to_function[cli_current_page](com) def cli_navigation_loop(): while True: print(f"\nYou are currently in '{cli_current_page}':") print_page_details() print(f"{cli_current_page}: ", end="") try: execute_command(input()) except Exception as e: print(f"An error occurred: {traceback.format_exc()}") if(config.is_cli): print( "\n\nMangio-RVC-Fork v2 CLI App!\n" "Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n" ) cli_navigation_loop() #endregion #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 switch_pitch_controls(f0method0): is_visible = f0method0 != 'rmvpe' if rvc_globals.NotesOrHertz: return ( {"visible": False, "__type__": "update"}, {"visible": is_visible, "__type__": "update"}, {"visible": False, "__type__": "update"}, {"visible": is_visible, "__type__": "update"} ) else: return ( {"visible": is_visible, "__type__": "update"}, {"visible": False, "__type__": "update"}, {"visible": is_visible, "__type__": "update"}, {"visible": False, "__type__": "update"} ) def match_index(sid0: str) -> tuple: sid0strip = re.sub(r'\.pth|\.onnx$', '', sid0) sid0name = os.path.split(sid0strip)[-1] # Extract only the name, not the directory # Check if the sid0strip has the specific ending format _eXXX_sXXX if re.match(r'.+_e\d+_s\d+$', sid0name): base_model_name = sid0name.rsplit('_', 2)[0] else: base_model_name = sid0name sid_directory = os.path.join(index_root, base_model_name) directories_to_search = [sid_directory] if os.path.exists(sid_directory) else [] directories_to_search.append(index_root) matching_index_files = [] for directory in directories_to_search: for filename in os.listdir(directory): if filename.endswith('.index') and 'trained' not in filename: # Condition to match the name name_match = any(name.lower() in filename.lower() for name in [sid0name, base_model_name]) # If in the specific directory, it's automatically a match folder_match = directory == sid_directory if name_match or folder_match: index_path = os.path.join(directory, filename) if index_path in indexes_list: matching_index_files.append((index_path, os.path.getsize(index_path), ' ' not in filename)) if matching_index_files: # Sort by favoring files without spaces and by size (largest size first) matching_index_files.sort(key=lambda x: (-x[2], -x[1])) best_match_index_path = matching_index_files[0][0] return best_match_index_path, best_match_index_path return '', '' def stoptraining(mim): if mim: try: with open('csvdb/stop.csv', 'w+') as file: file.write("True") os.kill(PID, SIGTERM) except Exception as e: print(f"Couldn't click due to {e}") return ( {"visible": True , "__type__": "update"}, {"visible": False, "__type__": "update"}) return ( {"visible": False, "__type__": "update"}, {"visible": True , "__type__": "update"}) tab_faq = i18n("常见问题解答") faq_file = "docs/faq.md" if tab_faq == "常见问题解答" else "docs/faq_en.md" weights_dir = 'weights/' def note_to_hz(note_name): SEMITONES = {'C': -9, 'C#': -8, 'D': -7, 'D#': -6, 'E': -5, 'F': -4, 'F#': -3, 'G': -2, 'G#': -1, 'A': 0, 'A#': 1, 'B': 2} pitch_class, octave = note_name[:-1], int(note_name[-1]) semitone = SEMITONES[pitch_class] note_number = 12 * (octave - 4) + semitone frequency = 440.0 * (2.0 ** (1.0/12)) ** note_number return frequency 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_name def save_to_wav2(dropbox): file_path = dropbox.name target_path = os.path.join('audios', os.path.basename(file_path)) if os.path.exists(target_path): os.remove(target_path) print('Replacing old dropdown file...') shutil.move(file_path, target_path) return target_path def change_choices2(): return "" def GradioSetup(UTheme=gr.themes.Soft()): default_weight = names[0] if names else '' # Set the first found weight as the preloaded model with gr.Blocks(theme='JohnSmith9982/small_and_pretty', title="Applio") as app: gr.HTML("

🍏 Applio (Mangio-RVC-Fork)

") # gr.Markdown( # value=i18n( # "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录使用需遵守的协议-LICENSE.txt." # ) #) with gr.Tabs(): with gr.TabItem(i18n("模型推理")): with gr.Row(): sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value=default_weight) refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), variant="primary") clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") clean_button.click(fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0]) with gr.TabItem(i18n("单个")): with gr.Row(): 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=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0]) with gr.Group(): # Defines whole single inference option section with gr.Row(): with gr.Column(): # First column for audio-related inputs dropbox = gr.File(label=i18n("将音频拖到此处，然后点击刷新按钮")) record_button=gr.Audio(source="microphone", label=i18n("或录制音频"), type="filepath") input_audio0 = gr.Textbox( label=i18n("Manual path to the audio file to be processed"), value=os.path.join(now_dir, "audios", "someguy.mp3"), visible=False ) input_audio1 = gr.Dropdown( label=i18n("自动检测音频路径并从下拉菜单中选择："), choices=sorted(audio_paths), value='', interactive=True, ) input_audio1.select(fn=lambda:'',inputs=[],outputs=[input_audio0]) input_audio0.input(fn=lambda:'',inputs=[],outputs=[input_audio1]) dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0]) dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio1]) record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0]) record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio1]) best_match_index_path1, _ = match_index(sid0.value) # Get initial index from default sid0 (first voice model in list) with gr.Column(): # Second column for pitch shift and other options file_index2 = gr.Dropdown( label=i18n("自动检测index路径,下拉式选择(dropdown)"), choices=get_indexes(), value=best_match_index_path1, interactive=True, allow_custom_value=True, ) index_rate1 = gr.Slider( minimum=0, maximum=1, label=i18n("检索特征占比"), value=0.75, interactive=True, ) refresh_button.click( fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1] ) with gr.Column(): vc_transform0 = gr.Number( label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 ) # Create a checkbox for advanced settings advanced_settings_checkbox = gr.Checkbox( value=False, label=i18n("高级设置"), interactive=True, ) # Advanced settings container with gr.Column(visible=False) as advanced_settings: # Initially hidden with gr.Row(label = i18n("高级设置"), open = False): with gr.Column(): f0method0 = gr.Radio( label=i18n( "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" ), choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe", "rmvpe+"], value="rmvpe+", interactive=True, ) crepe_hop_length = gr.Slider( minimum=1, maximum=512, step=1, label=i18n("crepe_hop_length"), value=120, interactive=True, visible=False, ) 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, ) minpitch_slider = gr.Slider( label = i18n("音高最小值"), info = i18n("指定推断的最小音高 [HZ]"), step = 0.1, minimum = 1, scale = 0, value = 50, maximum = 16000, interactive = True, visible = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), ) minpitch_txtbox = gr.Textbox( label = i18n("音高最小值"), info = i18n("为推断指定最小音高 [音符][八度]"), placeholder = "C5", visible = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), interactive = True, ) maxpitch_slider = gr.Slider( label = i18n("音高最大值"), info = i18n("指定推断的最大音高 [HZ]"), step = 0.1, minimum = 1, scale = 0, value = 1100, maximum = 16000, interactive = True, visible = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), ) maxpitch_txtbox = gr.Textbox( label = i18n("音高最大值"), info = i18n("为推断指定最大音高 [音符][八度]"), placeholder = "C6", visible = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), interactive = True, ) with gr.Column(): file_index1 = 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, ) with gr.Accordion(label = i18n("自定义 f0 [根音] 文件"), open = False): 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")) f0method0.change( fn=lambda radio: ( { "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], "__type__": "update" } ), inputs=[f0method0], outputs=[crepe_hop_length] ) f0method0.change( fn=switch_pitch_controls, inputs=[f0method0], outputs=[minpitch_slider, minpitch_txtbox, maxpitch_slider, maxpitch_txtbox] ) with gr.Column(): 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, ) 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=0.25, 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=i18n("共振声移动推理音频"), info=i18n("用于将男性转换为女性，反之亦然"), interactive=True, visible=True, ) formant_preset = gr.Dropdown( value='', choices=get_fshift_presets(), label=i18n("浏览共振峰预设"), info=i18n("预设位于 formantshiftcfg/ 文件夹中"), visible=bool(DoFormant), ) formant_refresh_button = gr.Button( value='\U0001f504', visible=bool(DoFormant), variant='primary', ) qfrency = gr.Slider( value=Quefrency, info=i18n("默认值为 1.0"), label=i18n("用于共振峰变换的 Quefrency"), minimum=0.0, maximum=16.0, step=0.1, visible=bool(DoFormant), interactive=True, ) tmbre = gr.Slider( value=Timbre, info=i18n("默认值为 1.0"), label=i18n("用于共振峰变换的音色"), minimum=0.0, maximum=16.0, step=0.1, visible=bool(DoFormant), interactive=True, ) frmntbut = gr.Button(i18n("应用"), variant="primary", visible=bool(DoFormant)) formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre],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]) # Function to toggle advanced settings def toggle_advanced_settings(checkbox): return {"visible": checkbox, "__type__": "update"} # Attach the change event advanced_settings_checkbox.change( fn=toggle_advanced_settings, inputs=[advanced_settings_checkbox], outputs=[advanced_settings] ) but0 = gr.Button(i18n("转换"), variant="primary").style(full_width=True) with gr.Row(): # Defines output info + output audio download after conversion vc_output1 = gr.Textbox(label=i18n("输出信息")) vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) with gr.Group(): # I think this defines the big convert button with gr.Row(): but0.click( vc_single, [ spk_item, input_audio0, input_audio1, vc_transform0, f0_file, f0method0, file_index1, file_index2, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, crepe_hop_length, minpitch_slider, minpitch_txtbox, maxpitch_slider, maxpitch_txtbox, ], [vc_output1, vc_output2], ) with gr.TabItem(i18n("批处理")): with gr.Group(): # Markdown explanation of batch inference gr.Markdown( value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") ) 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") with gr.Column(): file_index4 = gr.Dropdown( label=i18n("自动检测index路径,下拉式选择(dropdown)"), choices=get_indexes(), value=best_match_index_path1, interactive=True, ) sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4]) refresh_button.click( fn=lambda: change_choices()[1], inputs=[], outputs=file_index4, ) index_rate2 = gr.Slider( minimum=0, maximum=1, label=i18n("检索特征占比"), value=0.75, interactive=True, ) with gr.Row(): 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=os.path.join(now_dir, "audios"), ) 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(): with gr.Column(): # Create a checkbox for advanced batch settings advanced_settings_batch_checkbox = gr.Checkbox( value=False, label=i18n("高级设置"), interactive=True, ) # Advanced batch settings container with gr.Row(visible=False) as advanced_settings_batch: # Initially hidden with gr.Row(label = i18n("高级设置[批量]"), open = False): 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, ) 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.Row(): format1 = gr.Radio( label=i18n("导出文件格式"), choices=["wav", "flac", "mp3", "m4a"], value="flac", 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, ) vc_output3 = gr.Textbox(label=i18n("输出信息")) but1 = gr.Button(i18n("转换"), variant="primary") but1.click( vc_multi, [ spk_item, dir_input, opt_input, inputs, vc_transform1, f0method1, file_index3, file_index4, index_rate2, filter_radius1, resample_sr1, rms_mix_rate1, protect1, format1, crepe_hop_length, minpitch_slider if (not rvc_globals.NotesOrHertz) else minpitch_txtbox, maxpitch_slider if (not rvc_globals.NotesOrHertz) else maxpitch_txtbox, ], [vc_output3], ) sid0.change( fn=get_vc, inputs=[sid0, protect0, protect1], outputs=[spk_item, protect0, protect1], ) spk_item, protect0, protect1 = get_vc(sid0.value, protect0, protect1) # Set VC parameters for the preloaded model # Function to toggle advanced settings def toggle_advanced_settings_batch(checkbox): return {"visible": checkbox, "__type__": "update"} # Attach the change event advanced_settings_batch_checkbox.change( fn=toggle_advanced_settings_batch, inputs=[advanced_settings_batch_checkbox], outputs=[advanced_settings_batch] ) # with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): # UVR section # with gr.Group(): # gr.Markdown( # value=i18n( # "人声伴奏分离批量处理， 使用UVR5模型。
" # "合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
" # "模型分为三类：
" # "1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
" # "2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
" # "3、去混响、去延迟模型（by FoxJoy）：
" # "  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
" # " (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
" # "去混响/去延迟，附：
" # "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
" # "2、MDX-Net-Dereverb模型挺慢的；
" # "3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" # ) # ) # with gr.Row(): # with gr.Column(): # dir_wav_input = gr.Textbox( # label=i18n("输入待处理音频文件夹路径"), # value=os.path.join(now_dir, "audios") # ) # wav_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.Column(): # model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) # agg = gr.Slider( # minimum=0, # maximum=20, # step=1, # label="人声提取激进程度", # value=10, # interactive=True, # visible=False, # ) # opt_vocal_root = gr.Textbox( # label=i18n("指定输出主人声文件夹"), value="opt" # ) # opt_ins_root = gr.Textbox( # label=i18n("指定输出非主人声文件夹"), value="opt" # ) # format0 = gr.Radio( # label=i18n("导出文件格式"), # choices=["wav", "flac", "mp3", "m4a"], # value="flac", # interactive=True, # ) # but2 = gr.Button(i18n("转换"), variant="primary") # vc_output4 = gr.Textbox(label=i18n("输出信息")) # but2.click( # uvr, # [ # model_choose, # dir_wav_input, # opt_vocal_root, # wav_inputs, # opt_ins_root, # agg, # format0, # ], # [vc_output4], # ) with gr.TabItem(i18n("训练")): gr.Markdown( value=i18n( "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " ) ) with gr.Row(): exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value=i18n("宓模型")) sr2 = gr.Radio( label=i18n("目标采样率"), choices=["40k", "48k", "32k"], value="40k", interactive=True, ) if_f0_3 = gr.Checkbox( label=i18n("模型是否具有俯仰引导功能"), value=True, interactive=True, ) version19 = gr.Radio( label=i18n("版本"), choices=["v1", "v2"], value="v2", interactive=True, visible=True, ) np7 = gr.Slider( minimum=0, maximum=config.n_cpu, step=1, label=i18n("提取音高和处理数据使用的CPU进程数"), value=int(np.ceil(config.n_cpu / 1.5)), interactive=True, ) with gr.Group(): gr.Markdown( value=i18n( "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " ) ) with gr.Row(): # trainset_dir4 = gr.Textbox( # label=i18n("输入训练文件夹路径"), value=os.path.join(now_dir, datasets_root) # ) trainset_dir4 = gr.Dropdown(choices=sorted(datasets), label=i18n("选择你的数据集。"), value=get_dataset()) btn_update_dataset_list = gr.Button(i18n("更新清单。"), variant="primary") spk_id5 = gr.Slider( minimum=0, maximum=4, step=1, label=i18n("请指定说话人id"), value=0, interactive=True, ) btn_update_dataset_list.click( easy_infer.update_dataset_list, [spk_id5], trainset_dir4 ) but1 = gr.Button(i18n("处理数据"), variant="primary") info1 = gr.Textbox(label=i18n("输出信息"), value="") but1.click( preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] ) with gr.Group(): gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) with gr.Row(): with gr.Column(): 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, ) gr.Textbox(label=i18n("显卡信息"), value=gpu_info) with gr.Column(): 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=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe"], # [ MANGIO ]: 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=64, interactive=True, visible=False, ) f0method8.change( fn=lambda radio: ( { "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], "__type__": "update" } ), inputs=[f0method8], outputs=[extraction_crepe_hop_length] ) but2 = gr.Button(i18n("特征提取"), variant="primary") info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8, interactive=False) but2.click( extract_f0_feature, [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], [info2], ) with gr.Group(): gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) with gr.Row(): save_epoch10 = gr.Slider( minimum=1, maximum=50, step=1, label=i18n("保存频率save_every_epoch"), value=10, interactive=True, visible=True, ) total_epoch11 = gr.Slider( minimum=1, maximum=10000, step=2, label=i18n("总训练轮数total_epoch"), value=750, interactive=True, ) batch_size12 = gr.Slider( minimum=1, maximum=40, step=1, label=i18n("每张显卡的batch_size"), #value=default_batch_size, value=20, interactive=True, ) if_save_latest13 = gr.Checkbox( label=i18n("是否只保存最新的 .ckpt 文件以节省硬盘空间"), value=True, interactive=True, ) if_cache_gpu17 = gr.Checkbox( label=i18n("将所有训练集缓存到 GPU 内存中。缓存小型数据集（少于 10 分钟）可以加快训练速度，但缓存大型数据集会消耗大量 GPU 内存，可能无法显著提高速度"), value=False, interactive=True, ) if_save_every_weights18 = gr.Checkbox( label=i18n("在每个保存点将一个小的最终模型保存到 权重 文件夹中"), value=True, interactive=True, ) with gr.Row(): pretrained_G14 = gr.Textbox( lines=2, label=i18n("加载预训练底模G路径"), value="pretrained_v2/f0G40k.pth", interactive=True, ) pretrained_D15 = gr.Textbox( lines=2, label=i18n("加载预训练底模D路径"), value="pretrained_v2/f0D40k.pth", interactive=True, ) sr2.change( change_sr2, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15], ) version19.change( change_version19, [sr2, if_f0_3, version19], [pretrained_G14, pretrained_D15, sr2], ) if_f0_3.change( fn=change_f0, inputs=[if_f0_3, sr2, version19], outputs=[f0method8, pretrained_G14, pretrained_D15], ) if_f0_3.change(fn=lambda radio: ( { "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], "__type__": "update" } ), inputs=[f0method8], outputs=[extraction_crepe_hop_length]) 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, ) butstop = gr.Button(i18n("停止培训"), variant='primary', visible=False, ) but3 = gr.Button(i18n("训练模型"), 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=[but3, butstop]) with gr.Column(scale=0): gr.Markdown(value="
") gr.Markdown(value="### " + i18n("保存前构建索引。")) but4 = gr.Button(i18n("训练特征索引"), variant="primary") gr.Markdown(value="### " + i18n("训练结束后保存您的模型。")) save_action = gr.Dropdown(label=i18n("存储类型"), choices=[i18n("保存所有"),i18n("保存 D 和 G"),i18n("保存声音")], value=i18n("选择模型保存方法"), interactive=True) but7 = gr.Button(i18n("保存模型"), variant="primary") # but4 = gr.Button(i18n("训练特征索引"), variant="primary") info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) if_save_every_weights18.change( fn=lambda if_save_every_weights: ( { "visible": if_save_every_weights, "__type__": "update" } ), inputs=[if_save_every_weights18], outputs=[save_epoch10] ) 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) but7.click(easy_infer.save_model, [exp_dir1, save_action], info3) with gr.Group(): gr.Markdown(value=i18n( '步骤4：单击模型的导出最低点后，在模型图上的导出最低点，新文件将位于logs/[yourmodelname]/lowestvals/folder中') ) with gr.Row(): with gr.Accordion(label=i18n("最低点导出")): lowestval_weight_dir = gr.Textbox(visible=False) ds = gr.Textbox(visible=False) weights_dir1 = gr.Textbox(visible=False, value=weights_dir) with gr.Row(): amntlastmdls = gr.Slider( minimum=1, maximum=25, label=i18n('保存多少个最低点'), value=3, step=1, interactive=True, ) lpexport = gr.Button( value=i18n('导出模型的最低点'), variant='primary', ) lw_mdls = gr.File( file_count="multiple", label=i18n("输出型号"), interactive=False, ) ##### with gr.Row(): infolpex = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) mdlbl = gr.Dataframe(label=i18n('所选模型的统计数据'), datatype='number', type='pandas') lpexport.click( lambda model_name: os.path.join("logs", model_name, "lowestvals"), inputs=[exp_dir1], outputs=[lowestval_weight_dir] ) lpexport.click(fn=tensorlowest.main, inputs=[exp_dir1, save_epoch10, amntlastmdls], outputs=[ds]) ds.change( fn=tensorlowest.selectweights, inputs=[exp_dir1, ds, weights_dir1, lowestval_weight_dir], outputs=[infolpex, lw_mdls, mdlbl], ) with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): # UVR section with gr.Group(): gr.Markdown( value=i18n( "人声伴奏分离批量处理， 使用UVR5模型。
" "合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
" "模型分为三类：
" "1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
" "2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
" "3、去混响、去延迟模型（by FoxJoy）：
" "  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
" " (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
" "去混响/去延迟，附：
" "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
" "2、MDX-Net-Dereverb模型挺慢的；
" "3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" ) ) with gr.Row(): with gr.Column(): dir_wav_input = gr.Textbox( label=i18n("输入待处理音频文件夹路径"), value=os.path.join(now_dir, "audios") ) wav_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.Column(): model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) agg = gr.Slider( minimum=0, maximum=20, step=1, label="人声提取激进程度", value=10, interactive=True, visible=False, ) opt_vocal_root = gr.Textbox( label=i18n("指定输出主人声文件夹"), value="opt" ) opt_ins_root = gr.Textbox( label=i18n("指定输出非主人声文件夹"), value="opt" ) format0 = gr.Radio( label=i18n("导出文件格式"), choices=["wav", "flac", "mp3", "m4a"], value="flac", interactive=True, ) but2 = gr.Button(i18n("转换"), variant="primary") vc_output4 = gr.Textbox(label=i18n("输出信息")) but2.click( uvr, [ model_choose, dir_wav_input, opt_vocal_root, wav_inputs, opt_ins_root, agg, format0, ], [vc_output4], ) with gr.TabItem(i18n("ckpt处理")): with gr.Group(): gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) with gr.Row(): ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True, placeholder="Path to your model A.") ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True, placeholder="Path to your model B.") alpha_a = gr.Slider( minimum=0, maximum=1, label=i18n("A模型权重"), value=0.5, interactive=True, ) with gr.Row(): sr_ = gr.Radio( label=i18n("目标采样率"), choices=["40k", "48k"], value="40k", interactive=True, ) if_f0_ = gr.Checkbox( label="Whether the model has pitch guidance.", value=True, interactive=True, ) info__ = gr.Textbox( label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model information to be placed." ) name_to_save0 = gr.Textbox( label=i18n("保存的模型名不带后缀"), value="", placeholder="Name for saving.", max_lines=1, interactive=True, ) version_2 = gr.Radio( label=i18n("模型版本型号"), choices=["v1", "v2"], value="v1", interactive=True, ) with gr.Row(): but6 = gr.Button(i18n("融合"), variant="primary") info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but6.click( merge, [ ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0, version_2, ], info4, ) # def merge(path1,path2,alpha1,sr,f0,info): with gr.Group(): gr.Markdown(value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)")) with gr.Row(): ###### ckpt_path0 = gr.Textbox( label=i18n("模型路径"), placeholder="Path to your Model.", value="", interactive=True ) info_ = gr.Textbox( label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model information to be changed." ) name_to_save1 = gr.Textbox( label=i18n("保存的文件名, 默认空为和源文件同名"), placeholder="Either leave empty or put in the Name of the Model to be saved.", value="", max_lines=8, interactive=True, ) with gr.Row(): but7 = gr.Button(i18n("修改"), variant="primary") info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5) with gr.Group(): gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")) with gr.Row(): ckpt_path1 = gr.Textbox( label=i18n("模型路径"), value="", interactive=True, placeholder="Model path here." ) but8 = gr.Button(i18n("查看"), variant="primary") info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but8.click(show_info, [ckpt_path1], info6) with gr.Group(): gr.Markdown( value=i18n( "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况" ) ) with gr.Row(): ckpt_path2 = gr.Textbox( lines=3, label=i18n("模型路径"), value=os.path.join(now_dir, "logs", "[YOUR_MODEL]", "G_23333.pth"), interactive=True, ) save_name = gr.Textbox( label=i18n("保存名"), value="", interactive=True, placeholder="Your filename here.", ) sr__ = gr.Radio( label=i18n("目标采样率"), choices=["32k", "40k", "48k"], value="40k", interactive=True, ) if_f0__ = gr.Checkbox( label="Whether the model has pitch guidance.", value=True, interactive=True, ) version_1 = gr.Radio( label=i18n("模型版本型号"), choices=["v1", "v2"], value="v2", interactive=True, ) info___ = gr.Textbox( label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model info here." ) but9 = gr.Button(i18n("提取"), variant="primary") info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) ckpt_path2.change( change_info_, [ckpt_path2], [sr__, if_f0__, version_1] ) but9.click( extract_small_model, [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], info7, ) # with gr.TabItem(i18n("Onnx导出")): # with gr.Row(): # ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True, placeholder="RVC model path.") # with gr.Row(): # onnx_dir = gr.Textbox( # label=i18n("Onnx输出路径"), value="", interactive=True, placeholder="Onnx model output path." # ) # with gr.Row(): # infoOnnx = gr.Label(label="info") # with gr.Row(): # butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") # butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) with gr.TabItem(i18n("资源")): easy_infer.download_model() easy_infer.download_backup() easy_infer.download_dataset(trainset_dir4) easy_infer.youtube_separator() with gr.TabItem(i18n("设置")): with gr.Row(): gr.Markdown(value= i18n("音调设置") ) noteshertz = gr.Checkbox( label = i18n("是否使用音符名称而不是它们的赫兹值。例如，使用[C5，D6]代替[523.25，1174.66]赫兹。"), value = rvc_globals.NotesOrHertz, interactive = True, ) noteshertz.change(fn=lambda nhertz: rvc_globals.__setattr__('NotesOrHertz', nhertz), inputs=[noteshertz], outputs=[]) noteshertz.change( fn=switch_pitch_controls, inputs=[f0method0], outputs=[ minpitch_slider, minpitch_txtbox, maxpitch_slider, maxpitch_txtbox,] ) #with gr.TabItem(tab_faq): #try: #with open(faq_file, "r", encoding="utf8") as f: #info = f.read() #gr.Markdown(value=info) #except: #gr.Markdown(traceback.format_exc()) return app def GradioRun(app): share_gradio_link = config.iscolab or config.paperspace concurrency_count = 511 max_size = 1022 if ( config.iscolab or config.paperspace ): app.queue(concurrency_count=concurrency_count, max_size=max_size).launch( server_name="0.0.0.0", inbrowser=not config.noautoopen, server_port=config.listen_port, quiet=True, favicon_path="./icon.png", share=share_gradio_link, ) else: app.queue(concurrency_count=concurrency_count, max_size=max_size).launch( server_name="0.0.0.0", inbrowser=not config.noautoopen, server_port=config.listen_port, quiet=True, favicon_path=".\icon.png", share=share_gradio_link, ) #endregion if __name__ == "__main__": if os.name == 'nt': # Weird Windows async error when replacing a file. print("Any ConnectionResetErrors post-conversion are irrelevant and purely visual; they can be ignored\n") app = GradioSetup(UTheme=config.grtheme) GradioRun(app)