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infer-web.py
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import sys
from shutil import rmtree
import shutil
import json # Mangio fork using json for preset saving
import datetime
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 ''
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(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]
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,