diff --git "a/infer-web.py" "b/infer-web.py" deleted file mode 100644--- "a/infer-web.py" +++ /dev/null @@ -1,2447 +0,0 @@ -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