diff --git "a/infer-web.py" "b/infer-web.py" new file mode 100644--- /dev/null +++ "b/infer-web.py" @@ -0,0 +1,2447 @@ +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���, 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) \ No newline at end of file