From 18dd5aa10242abc531d077b5bf900334bef71124 Mon Sep 17 00:00:00 2001 From: kalomaze Date: Wed, 2 Aug 2023 01:13:54 +0000 Subject: [PATCH] Apply Code Formatter Change --- LazyImport.py | 3 +- i18n.py | 6 +- infer-web.py | 1335 +++++++++++++++++++++++++++++++---------------- my_utils.py | 95 ++-- tensorlowest.py | 79 +-- train/utils.py | 6 +- 6 files changed, 994 insertions(+), 530 deletions(-) diff --git a/LazyImport.py b/LazyImport.py index 5bdb05d..3f6e653 100644 --- a/LazyImport.py +++ b/LazyImport.py @@ -1,6 +1,7 @@ from importlib.util import find_spec, LazyLoader, module_from_spec from sys import modules + def lazyload(name): if name in modules: return modules[name] @@ -10,4 +11,4 @@ def lazyload(name): module = module_from_spec(spec) modules[name] = module loader.exec_module(module) - return module \ No newline at end of file + return module diff --git a/i18n.py b/i18n.py index 4e3536b..c874c28 100644 --- a/i18n.py +++ b/i18n.py @@ -1,5 +1,6 @@ import json + def load_language_list(language): try: with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f: @@ -20,8 +21,10 @@ class I18nAuto: >>> i18n.print() Using Language: en_US """ + def __init__(self, language=None): from locale import getdefaultlocale + language = language or getdefaultlocale()[0] if not self._language_exists(language): language = "en_US" @@ -32,6 +35,7 @@ class I18nAuto: @staticmethod def _language_exists(language): from os.path import exists + return exists(f"./i18n/{language}.json") def __call__(self, key): @@ -40,4 +44,4 @@ class I18nAuto: def print(self): """Prints the language currently in use.""" - print(f"Using Language: {self.language}") \ No newline at end of file + print(f"Using Language: {self.language}") diff --git a/infer-web.py b/infer-web.py index 4506875..a470093 100644 --- a/infer-web.py +++ b/infer-web.py @@ -1,27 +1,30 @@ import sys from shutil import rmtree -import json # Mangio fork using json for preset saving +import json # Mangio fork using json for preset saving from glob import glob1 from signal import SIGTERM import os + now_dir = os.getcwd() sys.path.append(now_dir) from LazyImport import lazyload -math = lazyload('math') +math = lazyload("math") import traceback import warnings -tensorlowest = lazyload('tensorlowest') + +tensorlowest = lazyload("tensorlowest") import faiss -ffmpeg = lazyload('ffmpeg') + +ffmpeg = lazyload("ffmpeg") np = lazyload("numpy") -torch = lazyload('torch') -re = lazyload('regex') +torch = lazyload("torch") +re = lazyload("regex") os.environ["OPENBLAS_NUM_THREADS"] = "1" os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" import logging @@ -59,7 +62,7 @@ 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'] +directories = ["logs", "audios", "datasets", "weights"] rmtree(tmp, ignore_errors=True) rmtree(os.path.join(runtime_dir, "infer_pack"), ignore_errors=True) @@ -74,18 +77,20 @@ warnings.filterwarnings("ignore") torch.manual_seed(114514) logging.getLogger("numba").setLevel(logging.WARNING) -os.makedirs('csvdb/', exist_ok=True) -with open('csvdb/formanting.csv', 'a'): pass -with open('csvdb/stop.csv', 'a'): pass +os.makedirs("csvdb/", exist_ok=True) +with open("csvdb/formanting.csv", "a"): + pass +with open("csvdb/stop.csv", "a"): + pass global DoFormant, Quefrency, Timbre try: - DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting') - DoFormant = DoFormant.lower() == 'true' + DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting") + DoFormant = DoFormant.lower() == "true" except (ValueError, TypeError, IndexError): DoFormant, Quefrency, Timbre = False, 1.0, 1.0 - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre) + CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre) config = Config() i18n = I18nAuto() @@ -96,8 +101,26 @@ 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"] +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): @@ -105,19 +128,26 @@ if torch.cuda.is_available() or ngpu != 0: 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)) + 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") +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="") + 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() @@ -131,40 +161,49 @@ audio_root = "audios" names = [name for name in os.listdir(weight_root) if name.endswith((".pth", ".onnx"))] -indexes_list = [f"{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] +indexes_list = [ + f"{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 = [f"{root}/{name}" - for root, _, files in os.walk(audio_root, topdown=False) - for name in files] +audio_paths = [ + f"{root}/{name}" + for root, _, files in os.walk(audio_root, topdown=False) + for name in files +] -uvr5_names = [name.replace(".pth", "") - for name in os.listdir(weight_uvr5_root) - if name.endswith(".pth") or "onnx" in name] +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 "" -check_for_name = lambda: sorted(names)[0] if names else '' def get_indexes(): indexes_list = [ - os.path.join(dirpath, filename).replace('\\', '/') + os.path.join(dirpath, filename).replace("\\", "/") for dirpath, _, filenames in os.walk("./logs/") for filename in filenames if filename.endswith(".index") and "trained" not in filename ] - - return indexes_list if indexes_list else '' + + return indexes_list if indexes_list else "" + def get_fshift_presets(): fshift_presets_list = [ - os.path.join(dirpath, filename).replace('\\', '/') + os.path.join(dirpath, filename).replace("\\", "/") for dirpath, _, filenames in os.walk("./formantshiftcfg/") for filename in filenames if filename.endswith(".txt") ] - - return fshift_presets_list if fshift_presets_list else '' + + return fshift_presets_list if fshift_presets_list else "" + def vc_single( sid, @@ -186,26 +225,37 @@ def vc_single( 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 - + f0_up_key = int(f0_up_key) - + try: - reliable_path = input_audio_path1 if input_audio_path0 == '' else input_audio_path0 + reliable_path = ( + input_audio_path1 if input_audio_path0 == "" else input_audio_path0 + ) audio = load_audio(reliable_path, 16000, DoFormant, Quefrency, Timbre) - + audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max - + times = [0, 0, 0] if not hubert_model: load_hubert() - + if_f0 = cpt.get("f0", 1) file_index = ( - file_index.strip(" ").strip('"').strip("\n").strip('"').strip(" ").replace("trained", "added") - ) if file_index != "" else file_index2 - + ( + file_index.strip(" ") + .strip('"') + .strip("\n") + .strip('"') + .strip(" ") + .replace("trained", "added") + ) + if file_index != "" + else file_index2 + ) + audio_opt = vc.pipeline( hubert_model, net_g, @@ -227,13 +277,20 @@ def vc_single( crepe_hop_length, f0_file=f0_file, ) - + 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." - - return f"Success.\n {index_info}\nTime:\n npy:{times[0]}, f0:{times[1]}, infer:{times[2]}", (tgt_sr, audio_opt) + + index_info = ( + "Using index:%s." % file_index + if os.path.exists(file_index) + else "Index not used." + ) + + return ( + f"Success.\n {index_info}\nTime:\n npy:{times[0]}, f0:{times[1]}, infer:{times[2]}", + (tgt_sr, audio_opt), + ) except: info = traceback.format_exc() print(info) @@ -259,21 +316,56 @@ def vc_multi( crepe_hop_length, ): try: - dir_path, opt_root = [x.strip(" ").strip('"').strip("\n").strip('"').strip(" ") for x in [dir_path, opt_root]] + 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] + 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) + 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, + ) if "Success" in info: try: tgt_sr, audio_opt = opt output_path = f"{opt_root}/{os.path.basename(path)}" - path, extension = output_path if format1 in ["wav", "flac", "mp3", "ogg", "aac"] else f"{output_path}.wav", format1 + path, extension = ( + output_path + if format1 in ["wav", "flac", "mp3", "ogg", "aac"] + else f"{output_path}.wav", + format1, + ) SFWrite(path, audio_opt, tgt_sr) - if os.path.exists(path) and extension not in ["wav", "flac", "mp3", "ogg", "aac"]: - os.system(f"ffmpeg -i {RQuote(path)} -vn {RQuote(path[:-4])}.{RQuote(extension)} -q:a 2 -y") + if os.path.exists(path) and extension not in [ + "wav", + "flac", + "mp3", + "ogg", + "aac", + ]: + os.system( + f"ffmpeg -i {RQuote(path)} -vn {RQuote(path[:-4])}.{RQuote(extension)} -q:a 2 -y" + ) except: info += traceback.format_exc() infos.append(f"{os.path.basename(path)}->{info}") @@ -286,16 +378,27 @@ def vc_multi( def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): 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] + 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) @@ -303,21 +406,30 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format try: info = ffmpeg.probe(inp_path, cmd="ffprobe") - if info["streams"][0]["channels"] == 2 and info["streams"][0]["sample_rate"] == "44100": + 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) + 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") + 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) + 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: @@ -335,14 +447,17 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format del pre_fun.model del pre_fun - except: traceback.print_exc() + except: + traceback.print_exc() print("clean_empty_cache") - if torch.cuda.is_available(): torch.cuda.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: @@ -353,8 +468,19 @@ def get_vc(sid, to_return_protect0, to_return_protect1): 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"]) + 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() @@ -368,14 +494,35 @@ def get_vc(sid, to_return_protect0, to_return_protect1): 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"} + 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"} + 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"]) + 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)) @@ -388,27 +535,37 @@ def get_vc(sid, to_return_protect0, to_return_protect1): return ( {"visible": True, "maximum": n_spk, "__type__": "update"}, to_return_protect0, - to_return_protect1 + to_return_protect1, ) def change_choices(): names = [name for name in os.listdir(weight_root) if name.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(os.getcwd(), "audios"))] + 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(os.getcwd(), "audios")) + ] return ( - {"choices": sorted(names), "__type__": "update"}, - {"choices": sorted(indexes_list), "__type__": "update"}, - {"choices": sorted(audio_paths), "__type__": "update"} + {"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, } + @jit def if_done(done, p): while p.poll() is None: @@ -416,23 +573,24 @@ def if_done(done, p): 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, frmntapply, formantpreset, formant_refresh_button): +def formant_enabled( + cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button +): global DoFormant DoFormant = cbox - - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) + + CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, qfrency, tmbre) visibility_update = {"visible": DoFormant, "__type__": "update"} - return ( - {"value": DoFormant, "__type__": "update"}, - ) + (visibility_update,) * 6 - + return ({"value": DoFormant, "__type__": "update"},) + (visibility_update,) * 6 + def formant_apply(qfrency, tmbre): global Quefrency, Timbre, DoFormant @@ -441,37 +599,41 @@ def formant_apply(qfrency, tmbre): Timbre = tmbre DoFormant = True - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre) + CSVutil("csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre) + + return ( + {"value": Quefrency, "__type__": "update"}, + {"value": Timbre, "__type__": "update"}, + ) - return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"}) def update_fshift_presets(preset, qfrency, tmbre): - - if preset: - with open(preset, 'r') as p: + 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 + with open(log_file, "w") as f: + pass cmd = ( f"{config.python_cmd} " @@ -487,29 +649,37 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): p = Popen(cmd, shell=True) done = [False] - threading.Thread(target=if_done, args=(done,p,)).start() + 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 + with open(log_file, "w") as f: + pass if if_f0: cmd = ( - f"{config.python_cmd} extract_f0_print.py {log_dir} " + f"{config.python_cmd} extract_f0_print.py {log_dir} " f"{RQuote(n_p)} {RQuote(f0method)} {RQuote(echl)}" ) print(cmd) @@ -541,13 +711,14 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): 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 "" @@ -559,14 +730,18 @@ def change_sr2(sr2, if_f0_3, version19): 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} + 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": ""} @@ -583,10 +758,10 @@ def change_version19(sr2, if_f0_3, version19): 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": "" } - + 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): @@ -598,18 +773,19 @@ def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D {"visible": if_f0_3, "__type__": "update"}, model_desc["G"], model_desc["D"], - {"visible": if_f0_3, "__type__": "update"} + {"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")) + wav_files_num = len(glob1(folder_path, "*.wav")) if wav_files_num > 0: log_interval = math.ceil(wav_files_num / batch_size12) @@ -618,6 +794,7 @@ def set_log_interval(exp_dir, batch_size12): return log_interval + def click_train( exp_dir1, sr2, @@ -634,11 +811,10 @@ def click_train( if_save_every_weights18, version19, ): - - CSVutil('csvdb/stop.csv', 'w+', 'formanting', False) + CSVutil("csvdb/stop.csv", "w+", "formanting", 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") @@ -649,28 +825,48 @@ def click_train( 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)) + 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]) + 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}") + 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: @@ -686,7 +882,7 @@ def click_train( 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)}" @@ -702,30 +898,47 @@ def click_train( p.wait() - return "Training is done, check train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"} + 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) + 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_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))] - + 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: + 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_ + 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) @@ -751,21 +964,25 @@ def train_index(exp_dir1, version19): 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.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") + 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!!!!!!! + +# 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"} @@ -773,7 +990,7 @@ def change_info_(ckpt_path): 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" @@ -783,23 +1000,26 @@ def change_info_(ckpt_path): 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) + "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 = 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) @@ -819,37 +1039,67 @@ def export_onnx(model_path, exported_path): return "Finished" -#region Mangio-RVC-Fork CLI App +# 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, _, do_formant = cli_split_command(com)[:15] + ( + 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, + _, + do_formant, + ) = cli_split_command(com)[:15] - 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]) + 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': + if do_formant.lower() == "false": Quefrency = 1.0 Timbre = 1.0 else: Quefrency, Timbre = map(float, cli_split_command(com)[15:17]) - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', do_formant.lower() == 'true', Quefrency, Timbre) - - output_message = 'Mangio-RVC-Fork Infer-CLI:' - output_path = f'audio-outputs/{output_file_name}' - + CSVutil( + "csvdb/formanting.csv", + "w+", + "formanting", + do_formant.lower() == "true", + Quefrency, + 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) @@ -860,7 +1110,7 @@ def cli_infer(com): source_audio_path, source_audio_path, transposition, - None, # f0 file support not implemented + None, # f0 file support not implemented f0_method, feature_index_path, feature_index_path, @@ -869,31 +1119,40 @@ def cli_infer(com): resample, mix, protection_amnt, - crepe_hop_length - ) + crepe_hop_length, + ) 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]}") - + 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]) - ) + 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) + ( + 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)) + has_pitch_guidance = bool(int(has_pitch_guidance)) crepe_hop_length = int(crepe_hop_length) print( @@ -902,17 +1161,18 @@ def cli_extract_feature(com): "Mangio-RVC-Fork Feature Extraction: Starting..." ) generator = extract_f0_feature( - gpus, - num_processes, - f0_method, - has_pitch_guidance, - model_name, - version, - crepe_hop_length + 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] @@ -921,120 +1181,130 @@ def cli_train(com): 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) + 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' + 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." + "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() + 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: + except Exception as e: print("An unexpected error occurred", e) - return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) + return ( + {"value": qfer, "__type__": "update"}, + {"value": tmbr, "__type__": "update"}, + ) + + @jit(nopython=True) 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' + "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')) + print(page_description.get(cli_current_page, "Invalid page")) def change_page(page): global cli_current_page cli_current_page = page return 0 + + @jit def execute_command(com): command_to_page = { @@ -1046,7 +1316,7 @@ def execute_command(com): "go train-feature": "TRAIN-FEATURE", "go extract-model": "EXTRACT-MODEL", } - + page_to_function = { "INFER": cli_infer, "PRE-PROCESS": cli_pre_process, @@ -1058,7 +1328,7 @@ def execute_command(com): 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 @@ -1066,25 +1336,29 @@ def execute_command(com): 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()}") + try: + execute_command(input()) + except Exception as e: + print(f"An error occurred: {traceback.format_exc()}") -if(config.is_cli): + +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 +# endregion -#region RVC WebUI App -''' +# region RVC WebUI App +""" def get_presets(): data = None with open('../inference-presets.json', 'r') as file: @@ -1094,45 +1368,51 @@ def get_presets(): preset_names.append(preset['name']) return preset_names -''' +""" + def match_index(sid0): - folder = sid0.split('.')[0].split('_')[0] + folder = sid0.split(".")[0].split("_")[0] parent_dir = "./logs/" + folder if not os.path.exists(parent_dir): - return '', '' - - for filename in os.listdir(parent_dir): + return "", "" + + for filename in os.listdir(parent_dir): if filename.endswith(".index"): - index_path = os.path.join(parent_dir, filename).replace('\\','/') + index_path = os.path.join(parent_dir, filename).replace("\\", "/") print(index_path) if index_path in indexes_list: return index_path, index_path - lowered_index_path = os.path.join(parent_dir.lower(), filename).replace('\\','/') + lowered_index_path = os.path.join(parent_dir.lower(), filename).replace( + "\\", "/" + ) if lowered_index_path in indexes_list: return lowered_index_path, lowered_index_path - return '', '' + return "", "" -def stoptraining(mim): + +def stoptraining(mim): if mim: try: - CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True') + CSVutil("csvdb/stop.csv", "w+", "stop", "True") os.kill(PID, SIGTERM) except Exception as e: print(f"Couldn't click due to {e}") return ( - {"visible": False, "__type__": "update"}, + {"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/' +weights_dir = "weights/" + def GradioSetup(): - #Change your Gradio Theme here. 👇 👇 👇 👇 Example: " theme='HaleyCH/HaleyCH_Theme' " - with gr.Blocks(theme=gr.themes.Soft(), title='Mangio-RVC-Web 💻') as app: + # Change your Gradio Theme here. 👇 👇 👇 👇 Example: " theme='HaleyCH/HaleyCH_Theme' " + with gr.Blocks(theme=gr.themes.Soft(), title="Mangio-RVC-Web 💻") as app: gr.HTML("

The Mangio-RVC-Fork 💻

") gr.Markdown( value=i18n( @@ -1140,7 +1420,6 @@ def GradioSetup(): ) ) with gr.Tabs(): - with gr.TabItem(i18n("模型推理")): # Inference Preset Row # with gr.Row(): @@ -1152,10 +1431,14 @@ def GradioSetup(): # Other RVC stuff with gr.Row(): - - sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value='') - - refresh_button = gr.Button(i18n("Refresh voice list, index path and audio files"), variant="primary") + sid0 = gr.Dropdown( + label=i18n("推理音色"), choices=sorted(names), value="" + ) + + refresh_button = gr.Button( + i18n("Refresh voice list, index path and audio files"), + variant="primary", + ) clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") spk_item = gr.Slider( minimum=0, @@ -1166,11 +1449,17 @@ def GradioSetup(): visible=False, interactive=True, ) - clean_button.click(fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0]) + clean_button.click( + fn=lambda: ({"value": "", "__type__": "update"}), + inputs=[], + outputs=[sid0], + ) with gr.Group(): gr.Markdown( - value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") + value=i18n( + "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. " + ) ) with gr.Row(): with gr.Column(): @@ -1178,21 +1467,38 @@ def GradioSetup(): label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 ) input_audio0 = gr.Textbox( - label=i18n("Add audio's name to the path to the audio file to be processed (default is the correct format example) Remove the path to use an audio from the dropdown list:"), - value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/" + "audio.wav", + label=i18n( + "Add audio's name to the path to the audio file to be processed (default is the correct format example) Remove the path to use an audio from the dropdown list:" + ), + value=os.path.abspath(os.getcwd()).replace("\\", "/") + + "/audios/" + + "audio.wav", ) input_audio1 = gr.Dropdown( - label=i18n("Auto detect audio path and select from the dropdown:"), + label=i18n( + "Auto detect audio path and select from the dropdown:" + ), choices=sorted(audio_paths), - value='', + value="", interactive=True, ) - input_audio1.change(fn=lambda:'',inputs=[],outputs=[input_audio0]) + input_audio1.change( + fn=lambda: "", inputs=[], outputs=[input_audio0] + ) 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"], # Fork Feature. Add Crepe-Tiny + choices=[ + "pm", + "harvest", + "dio", + "crepe", + "crepe-tiny", + "mangio-crepe", + "mangio-crepe-tiny", + "rmvpe", + ], # Fork Feature. Add Crepe-Tiny value="rmvpe", interactive=True, ) @@ -1208,17 +1514,20 @@ def GradioSetup(): f0method0.change( fn=lambda radio: ( { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" + "visible": radio + in ["mangio-crepe", "mangio-crepe-tiny"], + "__type__": "update", } ), inputs=[f0method0], - outputs=[crepe_hop_length] + outputs=[crepe_hop_length], ) filter_radius0 = gr.Slider( minimum=0, maximum=7, - label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), + 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, @@ -1229,21 +1538,20 @@ def GradioSetup(): value="", interactive=True, ) - + file_index2 = gr.Dropdown( label="3. Path to your added.index file (if it didn't automatically find it.)", choices=get_indexes(), interactive=True, allow_custom_value=True, - ) - #sid0.select(fn=match_index, inputs=sid0, outputs=file_index2) - - + ) + # sid0.select(fn=match_index, inputs=sid0, outputs=file_index2) - refresh_button.click( - fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1] - ) + fn=change_choices, + inputs=[], + outputs=[sid0, file_index2, input_audio1], + ) # file_big_npy1 = gr.Textbox( # label=i18n("特征文件路径"), # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", @@ -1289,32 +1597,32 @@ def GradioSetup(): interactive=True, visible=True, ) - + formant_preset = gr.Dropdown( - value='', + value="", choices=get_fshift_presets(), - label='Browse presets for formanting', - info='Presets are located in formantshiftcfg/ folder', + label="Browse presets for formanting", + info="Presets are located in formantshiftcfg/ folder", visible=bool(DoFormant), ) - + formant_refresh_button = gr.Button( - value='\U0001f504', + value="\U0001f504", visible=bool(DoFormant), - variant='primary', + variant="primary", ) - + qfrency = gr.Slider( - value=Quefrency, - info="Default value is 1.0", - label="Quefrency for formant shifting", - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, + value=Quefrency, + info="Default value is 1.0", + label="Quefrency for formant shifting", + minimum=0.0, + maximum=16.0, + step=0.1, + visible=bool(DoFormant), + interactive=True, ) - + tmbre = gr.Slider( value=Timbre, info="Default value is 1.0", @@ -1325,13 +1633,45 @@ def GradioSetup(): visible=bool(DoFormant), interactive=True, ) - - formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) - frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant)) - formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) - frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) - formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) - + + formant_preset.change( + fn=preset_apply, + inputs=[formant_preset, qfrency, tmbre], + outputs=[qfrency, tmbre], + ) + frmntbut = gr.Button( + "Apply", variant="primary", visible=bool(DoFormant) + ) + formanting.change( + fn=formant_enabled, + inputs=[ + formanting, + qfrency, + tmbre, + frmntbut, + formant_preset, + formant_refresh_button, + ], + outputs=[ + formanting, + qfrency, + tmbre, + frmntbut, + formant_preset, + formant_refresh_button, + ], + ) + frmntbut.click( + fn=formant_apply, + inputs=[qfrency, tmbre], + outputs=[qfrency, tmbre], + ) + formant_refresh_button.click( + fn=update_fshift_presets, + inputs=[formant_preset, qfrency, tmbre], + outputs=[formant_preset, qfrency, tmbre], + ) + f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) but0 = gr.Button(i18n("转换"), variant="primary") with gr.Row(): @@ -1354,13 +1694,15 @@ def GradioSetup(): resample_sr0, rms_mix_rate0, protect0, - crepe_hop_length + crepe_hop_length, ], [vc_output1, vc_output2], ) with gr.Group(): 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. ") + 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(): @@ -1376,11 +1718,13 @@ def GradioSetup(): 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"), + 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, @@ -1391,12 +1735,16 @@ def GradioSetup(): value="", interactive=True, ) - file_index4 = gr.Dropdown( #file index dropdown for batch + file_index4 = gr.Dropdown( # file index dropdown for batch label=i18n("自动检测index路径,下拉式选择(dropdown)"), choices=get_indexes(), interactive=True, ) - sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4]) + sid0.select( + fn=match_index, + inputs=[sid0], + outputs=[file_index2, file_index4], + ) refresh_button.click( fn=lambda: change_choices()[1], inputs=[], @@ -1443,10 +1791,12 @@ def GradioSetup(): with gr.Column(): dir_input = gr.Textbox( label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), - value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/", + value=os.path.abspath(os.getcwd()).replace("\\", "/") + + "/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") + file_count="multiple", + label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"), ) with gr.Row(): format1 = gr.Radio( @@ -1506,13 +1856,16 @@ def GradioSetup(): with gr.Column(): dir_wav_input = gr.Textbox( label=i18n("输入待处理音频文件夹路径"), - value=((os.getcwd()).replace('\\', '/') + "/audios/") + value=((os.getcwd()).replace("\\", "/") + "/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") - ) ##### + 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) + model_choose = gr.Dropdown( + label=i18n("模型"), choices=uvr5_names + ) agg = gr.Slider( minimum=0, maximum=20, @@ -1591,7 +1944,8 @@ def GradioSetup(): ) with gr.Row(): trainset_dir4 = gr.Textbox( - label=i18n("输入训练文件夹路径"), value=os.path.abspath(os.getcwd()) + "\\datasets\\" + label=i18n("输入训练文件夹路径"), + value=os.path.abspath(os.getcwd()) + "\\datasets\\", ) spk_id5 = gr.Slider( minimum=0, @@ -1604,10 +1958,14 @@ def GradioSetup(): 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] + preprocess_dataset, + [trainset_dir4, exp_dir1, sr2, np7], + [info1], ) with gr.Group(): - step2b = 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)")) + step2b = 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( @@ -1621,11 +1979,18 @@ def GradioSetup(): 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"], # Fork feature: Crepe on f0 extraction for training. + choices=[ + "pm", + "harvest", + "dio", + "crepe", + "mangio-crepe", + "rmvpe", + ], # Fork feature: Crepe on f0 extraction for training. value="rmvpe", interactive=True, ) - + extraction_crepe_hop_length = gr.Slider( minimum=1, maximum=512, @@ -1635,22 +2000,33 @@ def GradioSetup(): interactive=True, visible=False, ) - + f0method8.change( fn=lambda radio: ( { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" + "visible": radio + in ["mangio-crepe", "mangio-crepe-tiny"], + "__type__": "update", } ), inputs=[f0method8], - outputs=[extraction_crepe_hop_length] + 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) + 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], + [ + gpus6, + np7, + f0method8, + if_f0_3, + exp_dir1, + version19, + extraction_crepe_hop_length, + ], [info2], ) with gr.Group(): @@ -1720,46 +2096,55 @@ def GradioSetup(): [pretrained_G14, pretrained_D15, sr2], ) if_f0_3.change( - fn=change_f0, - inputs=[if_f0_3, sr2, version19], - outputs=[f0method8, pretrained_G14, pretrained_D15], + 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], ) - 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( - "Stop Training", - variant='primary', - visible=False, + "Stop Training", + 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=[butstop, but3]) - - + but3.click( + fn=stoptraining, + inputs=[gr.Number(value=0, visible=False)], + outputs=[but3, butstop], + ) + butstop.click( + fn=stoptraining, + inputs=[gr.Number(value=1, visible=False)], + outputs=[butstop, but3], + ) + but4 = gr.Button(i18n("训练特征索引"), variant="primary") - #but5 = gr.Button(i18n("一键训练"), variant="primary") + # but5 = gr.Button(i18n("一键训练"), 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" - } + {"visible": if_save_every_weights, "__type__": "update"} ), inputs=[if_save_every_weights18], - outputs=[save_epoch10] + outputs=[save_epoch10], ) - + but3.click( click_train, [ @@ -1780,12 +2165,10 @@ def GradioSetup(): ], [info3, butstop, but3], ) - + but4.click(train_index, [exp_dir1, version19], info3) - - - - #but5.click( + + # but5.click( # train1key, # [ # exp_dir1, @@ -1808,65 +2191,89 @@ def GradioSetup(): # extraction_crepe_hop_length # ], # info3, - #) + # ) with gr.Group(): - - gr.Markdown(value= - 'Step 4: Export lowest points on a graph of the model ' - 'After clicking on Export lowest points of a model, ' - 'The new files will be located in logs/[yourmodelname]/lowestvals/ folder' + gr.Markdown( + value="Step 4: Export lowest points on a graph of the model " + "After clicking on Export lowest points of a model, " + "The new files will be located in logs/[yourmodelname]/lowestvals/ folder" ) - + with gr.Row(): - with gr.Accordion(label='Lowest points export'): - + with gr.Accordion(label="Lowest points export"): 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='How many lowest points to save', + label="How many lowest points to save", value=3, step=1, interactive=True, ) lpexport = gr.Button( - value='Export lowest points of a model', - variant='primary', + value="Export lowest points of a model", + variant="primary", ) lw_mdls = gr.File( file_count="multiple", label="Output models", interactive=False, - ) ##### - + ) ##### + with gr.Row(): - infolpex = gr.Textbox(label="Output information:", value="", max_lines=10) - mdlbl = gr.Dataframe(label='Stats of selected models', datatype='number', type='pandas') - + infolpex = gr.Textbox( + label="Output information:", value="", max_lines=10 + ) + mdlbl = gr.Dataframe( + label="Stats of selected models", + datatype="number", + type="pandas", + ) + lpexport.click( - lambda model_name: os.path.join("logs", model_name, "lowestvals"), + lambda model_name: os.path.join( + "logs", model_name, "lowestvals" + ), inputs=[exp_dir1], - outputs=[lowestval_weight_dir] + outputs=[lowestval_weight_dir], ) - - lpexport.click(fn=tensorlowest.main, inputs=[exp_dir1, save_epoch10, amntlastmdls], outputs=[ds]) - + + 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], + inputs=[ + exp_dir1, + ds, + weights_dir1, + lowestval_weight_dir, + ], outputs=[infolpex, lw_mdls, mdlbl], - ) + ) 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.") + 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, @@ -1887,7 +2294,11 @@ def GradioSetup(): 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." + 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"), @@ -1921,12 +2332,19 @@ def GradioSetup(): ) # 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(): ###### + with gr.Row(): ###### ckpt_path0 = gr.Textbox( - label=i18n("模型路径"), placeholder="Path to your Model.", value="", interactive=True + 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." + 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"), @@ -1943,7 +2361,10 @@ def GradioSetup(): 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." + 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) @@ -1958,11 +2379,14 @@ def GradioSetup(): ckpt_path2 = gr.Textbox( lines=3, label=i18n("模型路径"), - value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/logs/[YOUR_MODEL]/G_23333.pth", + value=os.path.abspath(os.getcwd()).replace("\\", "/") + + "/logs/[YOUR_MODEL]/G_23333.pth", interactive=True, ) save_name = gr.Textbox( - label=i18n("保存名"), value="", interactive=True, + label=i18n("保存名"), + value="", + interactive=True, placeholder="Your filename here.", ) sr__ = gr.Radio( @@ -1983,7 +2407,11 @@ def GradioSetup(): 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." + 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) @@ -1998,10 +2426,18 @@ def GradioSetup(): 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.") + 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." + label=i18n("Onnx输出路径"), + value="", + interactive=True, + placeholder="Onnx model output path.", ) with gr.Row(): infoOnnx = gr.Label(label="info") @@ -2017,8 +2453,7 @@ def GradioSetup(): except: gr.Markdown(traceback.format_exc()) - - #region Mangio Preset Handler Region + # region Mangio Preset Handler Region def save_preset( preset_name, sid0, @@ -2034,45 +2469,44 @@ def GradioSetup(): resample_sr, rms_mix_rate, protect, - f0_file + f0_file, ): data = None - with open('../inference-presets.json', 'r') as file: + with open("../inference-presets.json", "r") as file: data = json.load(file) preset_json = { - 'name': preset_name, - 'model': sid0, - 'transpose': vc_transform, - 'audio_file': input_audio0, - 'auto_audio_file': input_audio1, - 'f0_method': f0method, - 'crepe_hop_length': crepe_hop_length, - 'median_filtering': filter_radius, - 'feature_path': file_index1, - 'auto_feature_path': file_index2, - 'search_feature_ratio': index_rate, - 'resample': resample_sr, - 'volume_envelope': rms_mix_rate, - 'protect_voiceless': protect, - 'f0_file_path': f0_file + "name": preset_name, + "model": sid0, + "transpose": vc_transform, + "audio_file": input_audio0, + "auto_audio_file": input_audio1, + "f0_method": f0method, + "crepe_hop_length": crepe_hop_length, + "median_filtering": filter_radius, + "feature_path": file_index1, + "auto_feature_path": file_index2, + "search_feature_ratio": index_rate, + "resample": resample_sr, + "volume_envelope": rms_mix_rate, + "protect_voiceless": protect, + "f0_file_path": f0_file, } - data['presets'].append(preset_json) - with open('../inference-presets.json', 'w') as file: + data["presets"].append(preset_json) + with open("../inference-presets.json", "w") as file: json.dump(data, file) file.flush() print("Saved Preset %s into inference-presets.json!" % preset_name) - def on_preset_changed(preset_name): print("Changed Preset to %s!" % preset_name) data = None - with open('../inference-presets.json', 'r') as file: + with open("../inference-presets.json", "r") as file: data = json.load(file) print("Searching for " + preset_name) returning_preset = None - for preset in data['presets']: - if(preset['name'] == preset_name): + for preset in data["presets"]: + if preset["name"] == preset_name: print("Found a preset") returning_preset = preset # return all new input values @@ -2092,11 +2526,11 @@ def GradioSetup(): # returning_preset['f0_file_path'] ) - # Preset State Changes - + # Preset State Changes + # This click calls save_preset that saves the preset into inference-presets.json with the preset name # mangio_preset_save_btn.click( - # fn=save_preset, + # fn=save_preset, # inputs=[ # mangio_preset_name_save, # sid0, @@ -2112,16 +2546,16 @@ def GradioSetup(): # rms_mix_rate0, # protect0, # f0_file - # ], + # ], # outputs=[] # ) # mangio_preset.change( - # on_preset_changed, + # on_preset_changed, # inputs=[ # # Pass inputs here # mangio_preset - # ], + # ], # outputs=[ # # Pass Outputs here. These refer to the gradio elements that we want to directly change # # sid0, @@ -2139,14 +2573,16 @@ def GradioSetup(): # # f0_file # ] # ) - #endregion + # endregion - # with gr.TabItem(i18n("招募音高曲线前端编辑器")): - # gr.Markdown(value=i18n("加开发群联系我xxxxx")) - # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): - # gr.Markdown(value=i18n("xxxxx")) + # with gr.TabItem(i18n("招募音高曲线前端编辑器")): + # gr.Markdown(value=i18n("加开发群联系我xxxxx")) + # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): + # gr.Markdown(value=i18n("xxxxx")) - if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE) + if ( + config.iscolab or config.paperspace + ): # Share gradio link for colab and paperspace (FORK FEATURE) app.queue(concurrency_count=511, max_size=1022).launch(share=True) else: app.queue(concurrency_count=511, max_size=1022).launch( @@ -2156,7 +2592,8 @@ def GradioSetup(): quiet=False, ) -#endregion + +# endregion if __name__ == "__main__": - GradioSetup() \ No newline at end of file + GradioSetup() diff --git a/my_utils.py b/my_utils.py index cc03613..ff49cfb 100644 --- a/my_utils.py +++ b/my_utils.py @@ -10,21 +10,24 @@ import random import csv platform_stft_mapping = { - 'linux': 'stftpitchshift', - 'darwin': 'stftpitchshift', - 'win32': 'stftpitchshift.exe', + "linux": "stftpitchshift", + "darwin": "stftpitchshift", + "win32": "stftpitchshift.exe", } stft = platform_stft_mapping.get(sys.platform) + def CSVutil(file, rw, type, *args): - if type == 'formanting': - if rw == 'r': + if type == "formanting": + if rw == "r": with open(file) as fileCSVread: csv_reader = list(csv.reader(fileCSVread)) return ( - csv_reader[0][0], csv_reader[0][1], csv_reader[0][2] - ) if csv_reader is not None else (lambda: exec('raise ValueError("No data")'))() + (csv_reader[0][0], csv_reader[0][1], csv_reader[0][2]) + if csv_reader is not None + else (lambda: exec('raise ValueError("No data")'))() + ) else: if args: doformnt = args[0] @@ -32,65 +35,74 @@ def CSVutil(file, rw, type, *args): doformnt = False qfr = args[1] if len(args) > 1 else 1.0 tmb = args[2] if len(args) > 2 else 1.0 - with open(file, rw, newline='') as fileCSVwrite: - csv_writer = csv.writer(fileCSVwrite, delimiter=',') + with open(file, rw, newline="") as fileCSVwrite: + csv_writer = csv.writer(fileCSVwrite, delimiter=",") csv_writer.writerow([doformnt, qfr, tmb]) - elif type == 'stop': + elif type == "stop": stop = args[0] if args else False - with open(file, rw, newline='') as fileCSVwrite: - csv_writer = csv.writer(fileCSVwrite, delimiter=',') + with open(file, rw, newline="") as fileCSVwrite: + csv_writer = csv.writer(fileCSVwrite, delimiter=",") csv_writer.writerow([stop]) + def load_audio(file, sr, DoFormant, Quefrency, Timbre): converted = False - DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting') + DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting") try: - file = ( - file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) + file = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - - if (DoFormant.lower() == 'true'): - numerator = round(random.uniform(1,4), 4) - + + if DoFormant.lower() == "true": + numerator = round(random.uniform(1, 4), 4) + if not file.endswith(".wav"): - if not os.path.isfile(f"{file_formanted}.wav"): converted = True - #print(f"\nfile = {file}\n") - #print(f"\nfile_formanted = {file_formanted}\n") + # print(f"\nfile = {file}\n") + # print(f"\nfile_formanted = {file_formanted}\n") converting = ( - ffmpeg.input(file_formanted, threads = 0) + ffmpeg.input(file_formanted, threads=0) .output(f"{RQuote(file_formanted)}.wav") .run( - cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True + cmd=["ffmpeg", "-nostdin"], + capture_stdout=True, + capture_stderr=True, ) ) else: pass - file_formanted = f"{file_formanted}.wav" if not file_formanted.endswith(".wav") else file_formanted - + file_formanted = ( + f"{file_formanted}.wav" + if not file_formanted.endswith(".wav") + else file_formanted + ) + print(f" · Formanting {file_formanted}...\n") - + command = ( f'{RQuote(stft)} -i "{RQuote(file_formanted)}" -q "{RQuote(Quefrency)}" ' f'-t "{RQuote(Timbre)}" -o "{RQuote(file_formanted)}FORMANTED_{RQuote(str(numerator))}.wav"' ) os.system(command) - + print(f" · Formanted {file_formanted}!\n") - + out, _ = ( - ffmpeg.input(f"{file_formanted}FORMANTED_{str(numerator)}.wav", threads=0) + ffmpeg.input( + f"{file_formanted}FORMANTED_{str(numerator)}.wav", threads=0 + ) .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr) .run( cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True ) ) - try: os.remove(f"{file_formanted}FORMANTED_{str(numerator)}.wav") - except Exception as e: pass; print(f"couldn't remove formanted type of file due to {e}") - + try: + os.remove(f"{file_formanted}FORMANTED_{str(numerator)}.wav") + except Exception as e: + pass + print(f"couldn't remove formanted type of file due to {e}") + else: out, _ = ( ffmpeg.input(file, threads=0) @@ -101,12 +113,15 @@ def load_audio(file, sr, DoFormant, Quefrency, Timbre): ) except Exception as e: raise RuntimeError(f"Failed to load audio: {e}") - + if converted: - try: os.remove(file_formanted) - except Exception as e: pass; print(f"Couldn't remove converted type of file due to {e}") + try: + os.remove(file_formanted) + except Exception as e: + pass + print(f"Couldn't remove converted type of file due to {e}") converted = False - + return np.frombuffer(out, np.float32).flatten() @@ -119,7 +134,7 @@ def check_audio_duration(file): probe = ffmpeg.probe(file) # Extract the duration from the probe result - duration = float(probe['streams'][0]['duration']) + duration = float(probe["streams"][0]["duration"]) # If the duration is less than 0.75 seconds, print the message and exit the loop if duration < 0.76: @@ -132,4 +147,4 @@ def check_audio_duration(file): return True except Exception as e: - raise RuntimeError(f"Failed to check audio duration: {e}") \ No newline at end of file + raise RuntimeError(f"Failed to check audio duration: {e}") diff --git a/tensorlowest.py b/tensorlowest.py index eccd4db..ba01f5a 100644 --- a/tensorlowest.py +++ b/tensorlowest.py @@ -6,11 +6,12 @@ from re import search as RSearch import pandas as pd from ast import literal_eval as LEval -weights_dir = 'weights/' +weights_dir = "weights/" + def find_biggest_tensorboard(tensordir): try: - files = [f for f in os.listdir(tensordir) if f.endswith('.0')] + files = [f for f in os.listdir(tensordir) if f.endswith(".0")] if not files: print("No files with the '.0' extension found!") return @@ -32,92 +33,96 @@ def find_biggest_tensorboard(tensordir): print("Couldn't find your model!") return + def main(model_name, save_freq, lastmdls): global lowestval_weight_dir, scl - tensordir = os.path.join('logs', model_name) + tensordir = os.path.join("logs", model_name) lowestval_weight_dir = os.path.join(tensordir, "lowestvals") - + latest_file = find_biggest_tensorboard(tensordir) - + if latest_file is None: print("Couldn't find a valid tensorboard file!") return - + tfile = os.path.join(tensordir, latest_file) - - ea = event_accumulator.EventAccumulator(tfile, + + ea = event_accumulator.EventAccumulator( + tfile, size_guidance={ - event_accumulator.COMPRESSED_HISTOGRAMS: 500, - event_accumulator.IMAGES: 4, - event_accumulator.AUDIO: 4, - event_accumulator.SCALARS: 0, - event_accumulator.HISTOGRAMS: 1, - }) + event_accumulator.COMPRESSED_HISTOGRAMS: 500, + event_accumulator.IMAGES: 4, + event_accumulator.AUDIO: 4, + event_accumulator.SCALARS: 0, + event_accumulator.HISTOGRAMS: 1, + }, + ) ea.Reload() ea.Tags() - scl = ea.Scalars('loss/g/total') + scl = ea.Scalars("loss/g/total") listwstep = {} - + for val in scl: if (val.step // save_freq) * save_freq in [val.step for val in scl]: listwstep[float(val.value)] = (val.step // save_freq) * save_freq lowest_vals = sorted(listwstep.keys())[:lastmdls] - sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals} - + sorted_dict = { + value: step for value, step in listwstep.items() if value in lowest_vals + } + return sorted_dict + def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir): os.makedirs(lowestval_weight_dir, exist_ok=True) logdir = [] files = [] - lbldict = { - 'Values': {}, - 'Names': {} - } + lbldict = {"Values": {}, "Names": {}} weights_dir_path = os.path.join(weights_dir, "") low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, "")) - + try: file_dict = LEval(file_dict) - except Exception as e: + except Exception as e: print(f"Error! {e}") return f"Couldn't load tensorboard file! {e}" - + weights = [f for f in os.scandir(weights_dir)] for key, value in file_dict.items(): - pattern = fr"^{model_name}_.*_s{value}\.pth$" - matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)] + pattern = rf"^{model_name}_.*_s{value}\.pth$" + matching_weights = [ + f.name for f in weights if f.is_file() and RSearch(pattern, f.name) + ] for weight in matching_weights: source_path = weights_dir_path + weight destination_path = os.path.join(lowestval_weight_dir, weight) - + copy2(source_path, destination_path) logdir.append(f"File = {weight} Value: {key}, Step: {value}") - lbldict['Names'][weight] = weight - lbldict['Values'][weight] = key + lbldict["Names"][weight] = weight + lbldict["Values"][weight] = key files.append(low_val_path + weight) print(f"File = {weight} Value: {key}, Step: {value}") - yield ('\n'.join(logdir), files, pd.DataFrame(lbldict)) - + yield ("\n".join(logdir), files, pd.DataFrame(lbldict)) + + return "".join(logdir), files, pd.DataFrame(lbldict) - return ''.join(logdir), files, pd.DataFrame(lbldict) - if __name__ == "__main__": model = str(input("Enter the name of the model: ")) sav_freq = int(input("Enter save frequency of the model: ")) ds = main(model, sav_freq) - - if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir) - \ No newline at end of file + + if ds: + selectweights(model, ds, weights_dir, lowestval_weight_dir) diff --git a/train/utils.py b/train/utils.py index 328e560..08b9e24 100644 --- a/train/utils.py +++ b/train/utils.py @@ -271,9 +271,11 @@ def load_wav_to_torch(full_path): def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: + with open(filename, encoding="utf-8") as f: filepaths_and_text = [line.strip().split(split) for line in f] - filepaths_and_text = [item for item in filepaths_and_text if len(item) == 5] # ensure there are 5 items. + filepaths_and_text = [ + item for item in filepaths_and_text if len(item) == 5 + ] # ensure there are 5 items. return filepaths_and_text