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infer-web.py
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import os
import sys
from dotenv import load_dotenv
import requests
import wave
import zipfile
now_dir = os.getcwd()
sys.path.append(now_dir)
load_dotenv()
from infer.modules.vc.modules import VC
from infer.modules.uvr5.modules import UVRHANDLER
from infer.lib.train.process_ckpt import (
change_info,
extract_small_model,
merge,
show_info,
)
from i18n.i18n import I18nAuto
from configs.config import Config
from sklearn.cluster import MiniBatchKMeans
import torch
import numpy as np
import gradio as gr
import faiss
import fairseq
import librosa
import librosa.display
import pathlib
import json
from pydub import AudioSegment
from time import sleep
from subprocess import Popen
from random import shuffle
import warnings
import traceback
import threading
import shutil
import logging
import matplotlib.pyplot as plt
import soundfile as sf
from dotenv import load_dotenv
from tools import pretrain_helper
import edge_tts, asyncio
from infer.modules.vc.ilariatts import tts_order_voice
language_dict = tts_order_voice
ilariavoices = list(language_dict.keys())
now_dir = os.getcwd()
sys.path.append(now_dir)
load_dotenv()
logging.getLogger("numba").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % now_dir, ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
os.makedirs(os.path.join(now_dir, "models/pth"), exist_ok=True)
os.environ["TEMP"] = tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
config = Config()
vc = VC(config)
weight_root = os.getenv("weight_root")
weight_uvr5_root = os.getenv("weight_uvr5_root")
index_root = os.getenv("index_root")
names = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_paths = []
for root, dirs, files in os.walk(index_root, topdown=False):
for name in files:
if name.endswith(".index") and "trained" not in name:
index_paths.append("%s/%s" % (root, name))
uvr5_names = [
'5_HP-Karaoke-UVR.pth',
'Kim_Vocal_2.onnx',
'MDX23C-8KFFT-InstVoc_HQ_2.ckpt',
'UVR-DeEcho-DeReverb.pth',
'UVR-Denoise',
]
if config.dml:
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
return res
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
i18n = I18nAuto()
logger.info(i18n)
ngpu = torch.cuda.device_count()
gpu_infos = []
mem = []
if_gpu_ok = False
if torch.cuda.is_available() or ngpu != 0:
for i in range(ngpu):
gpu_name = torch.cuda.get_device_name(i)
if any(
value in gpu_name.upper()
for value in [
"10",
"16",
"20",
"30",
"40",
"A2",
"A3",
"A4",
"P4",
"A50",
"500",
"A60",
"70",
"80",
"90",
"M4",
"T4",
"TITAN",
]
):
if_gpu_ok = True
gpu_infos.append("%s\t%s" % (i, gpu_name))
mem.append(
int(
torch.cuda.get_device_properties(i).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
)
if if_gpu_ok and len(gpu_infos) > 0:
gpu_info = "\n".join(gpu_infos)
default_batch_size = ((min(mem) // 2 + 1) // 2) * 2
else:
gpu_info = i18n("Your GPU doesn't work for training")
default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])
class ToolButton(gr.Button, gr.components.FormComponent):
def __init__(self, **kwargs):
super().__init__(variant="tool", **kwargs)
def get_block_name(self):
return "button"
weight_root = os.getenv("weight_root")
index_root = os.getenv("index_root")
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))]
def get_pretrained_files(directory, keyword, filter_str):
file_paths = {}
for filename in os.listdir(directory):
if filename.endswith(".pth") and keyword in filename and filter_str in filename:
file_paths[filename] = os.path.join(directory, filename)
return file_paths
pretrained_directory = "assets/pretrained_v2"
pretrained_path = {filename: os.path.join(pretrained_directory, filename) for filename in os.listdir(pretrained_directory)}
pretrained_G_files = get_pretrained_files(pretrained_directory, "G", "f0")
pretrained_D_files = get_pretrained_files(pretrained_directory, "D", "f0")
def get_pretrained_models(path_str, f0_str, sr2):
sr_mapping = pretrain_helper.get_pretrained_models(f0_str)
pretrained_G_filename = sr_mapping.get(sr2, "")
pretrained_D_filename = pretrained_G_filename.replace("G", "D")
if not pretrained_G_filename or not pretrained_D_filename:
logging.warning(f"Pretrained models not found for sample rate {sr2}, will not use pretrained models")
return os.path.join(pretrained_directory, pretrained_G_filename), os.path.join(pretrained_directory, pretrained_D_filename)
names = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_paths = []
for root, dirs, files in os.walk(index_root, topdown=False):
for name in files:
if name.endswith(".index") and "trained" not in name:
index_paths.append("%s/%s" % (root, name))
def generate_spectrogram_and_get_info(audio_file):
y, sr = librosa.load(audio_file, sr=None)
S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=256)
log_S = librosa.amplitude_to_db(S, ref=np.max, top_db=256)
plt.figure(figsize=(12, 5.5))
librosa.display.specshow(log_S, sr=sr, x_axis='time')
plt.colorbar(format='%+2.0f dB', pad=0.01)
plt.tight_layout(pad=0.5)
plt.savefig('spectrogram.png', dpi=500)
audio_info = sf.info(audio_file)
bit_depth = {'PCM_16': 16, 'FLOAT': 32}.get(audio_info.subtype, 0)
minutes, seconds = divmod(audio_info.duration, 60)
seconds, milliseconds = divmod(seconds, 1)
milliseconds *= 1000
speed_in_kbps = audio_info.samplerate * bit_depth / 1000
filename_without_extension, _ = os.path.splitext(os.path.basename(audio_file))
info_table = f"""
| Information | Value |
| :---: | :---: |
| File Name | {filename_without_extension} |
| Duration | {int(minutes)} minutes - {int(seconds)} seconds - {int(milliseconds)} milliseconds |
| Bitrate | {speed_in_kbps} kbp/s |
| Audio Channels | {audio_info.channels} |
| Samples per second | {audio_info.samplerate} Hz |
| Bit per second | {audio_info.samplerate * audio_info.channels * bit_depth} bit/s |
"""
return info_table, "spectrogram.png"
def change_choices():
names = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_paths = []
for root, dirs, files in os.walk(index_root, topdown=False):
for name in files:
if name.endswith(".index") and "trained" not in name:
index_paths.append("%s/%s" % (root, name))
audios = [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(index_paths),"__type__": "update"},{
"choices": sorted(audios), "__type__": "update"
}
# Define the tts_and_convert function
def tts_and_convert(ttsvoice, text, spk_item, vc_transform, f0_file, f0method, file_index1, file_index2, index_rate, filter_radius, resample_sr, rms_mix_rate, protect):
# Perform TTS (we only need 1 function)
vo=language_dict[ttsvoice]
asyncio.run(edge_tts.Communicate(text, vo).save("./TEMP/temp_ilariatts.mp3"))
aud_path = './TEMP/temp_ilariatts.mp3'
# Update output Textbox
vc_output1.update("Text converted successfully!")
#Calls vc similar to any other inference.
#This is why we needed all the other shit in our call, otherwise we couldn't infer.
return vc.vc_single(spk_item , None,aud_path, vc_transform, f0_file, f0method, file_index1, file_index2, index_rate, filter_radius, resample_sr, rms_mix_rate, protect)
def import_files(file):
if file is not None:
file_name = file.name
if file_name.endswith('.zip'):
with zipfile.ZipFile(file.name, 'r') as zip_ref:
# Create a temporary directory to extract files
temp_dir = './TEMP'
zip_ref.extractall(temp_dir)
# Move .pth and .index files to their respective directories
for root, dirs, files in os.walk(temp_dir):
for file in files:
if file.endswith('.pth'):
destination = './models/pth/' + file
if not os.path.exists(destination):
shutil.move(os.path.join(root, file), destination)
else:
print(f"File {destination} already exists. Skipping.")
elif file.endswith('.index'):
destination = './models/index/' + file
if not os.path.exists(destination):
shutil.move(os.path.join(root, file), destination)
else:
print(f"File {destination} already exists. Skipping.")
# Remove the temporary directory
shutil.rmtree(temp_dir)
return "Zip file has been successfully extracted."
elif file_name.endswith('.pth'):
destination = './models/pth/' + os.path.basename(file.name)
if not os.path.exists(destination):
os.rename(file.name, destination)
else:
print(f"File {destination} already exists. Skipping.")
return "PTH file has been successfully imported."
elif file_name.endswith('.index'):
destination = './models/index/' + os.path.basename(file.name)
if not os.path.exists(destination):
os.rename(file.name, destination)
else:
print(f"File {destination} already exists. Skipping.")
return "Index file has been successfully imported."
else:
return "Unsupported file type."
else:
return "No file has been uploaded."
def import_button_click(file):
return import_files(file)
def calculate_remaining_time(epochs, seconds_per_epoch):
total_seconds = epochs * seconds_per_epoch
hours = total_seconds // 3600
minutes = (total_seconds % 3600) // 60
seconds = total_seconds % 60
if hours == 0:
return f"{int(minutes)} minutes"
elif hours == 1:
return f"{int(hours)} hour and {int(minutes)} minutes"
else:
return f"{int(hours)} hours and {int(minutes)} minutes"
def get_audio_duration(audio_file_path):
audio_info = sf.info(audio_file_path)
duration_minutes = audio_info.duration / 60
return duration_minutes
def clean():
return {"value": "", "__type__": "update"}
sr_dict = {
"32k": 32000, "40k": 40000, "48k": 48000, "OV2-32k": 32000, "OV2-40k": 40000, "RIN-40k": 40000, "Snowie-40k": 40000, "Snowie-48k": 48000, "SnowieV3.1-40k": 40000, "SnowieV3.1-32k": 32000, "SnowieV3.1-48k": 48000, "SnowieV3.1-RinE3-40K": 40000, "Italia-32k": 32000,
}
def durations(sample_rate, model_options, qualities, duration):
if duration <= 350:
return qualities['short']
else:
if sample_rate == 32000:
return model_options['32k']
elif sample_rate == 40000:
return model_options['40k']
elif sample_rate == 48000:
return model_options['48k']
else:
return qualities['other']
def get_training_info(audio_file):
if audio_file is None:
return 'Please provide an audio file!'
duration = get_audio_duration(audio_file)
sample_rate = wave.open(audio_file, 'rb').getframerate()
training_info = {
(0, 2): (150, 'OV2'),
(2, 3): (200, 'OV2'),
(3, 5): (250, 'OV2'),
(5, 10): (300, 'Normal'),
(10, 25): (500, 'Normal'),
(25, 45): (700, 'Normal'),
(45, 60): (1000, 'Normal')
}
for (min_duration, max_duration), (epochs, pretrain) in training_info.items():
if min_duration <= duration < max_duration:
break
else:
return 'Duration is not within the specified range!'
return f'You should use the **{pretrain}** pretrain with **{epochs}** epochs at **{sample_rate/1000}khz** sample rate.'
def if_done(done, p):
while 1:
if p.poll() is None:
sleep(0.5)
else:
break
done[0] = True
def on_button_click(audio_file_path):
return get_training_info(audio_file_path)
def download_from_url(url, model):
if url == '':
return "URL cannot be left empty."
if model == '':
return "You need to name your model. For example: Ilaria"
url = url.strip()
zip_dirs = ["zips", "unzips"]
for directory in zip_dirs:
if os.path.exists(directory):
shutil.rmtree(directory)
os.makedirs("zips", exist_ok=True)
os.makedirs("unzips", exist_ok=True)
zipfile = model + '.zip'
zipfile_path = './zips/' + zipfile
try:
if "drive.google.com" in url:
subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path])
elif "mega.nz" in url:
m = Mega()
m.download_url(url, './zips')
else:
response = requests.get(url)
response.raise_for_status() # Raise an exception for HTTP errors
with open(zipfile_path, 'wb') as file:
file.write(response.content)
shutil.unpack_archive(zipfile_path, "./unzips", 'zip')
for root, dirs, files in os.walk('./unzips'):
for file in files:
file_path = os.path.join(root, file)
if file.endswith(".index"):
os.makedirs(f'./models/index', exist_ok=True)
shutil.copy2(file_path, f'./models/index/{model}.index')
elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
os.makedirs(f'./models/pth', exist_ok=True)
shutil.copy(file_path, f'./models/pth/{model}.pth')
shutil.rmtree("zips")
shutil.rmtree("unzips")
return "Model downloaded, you can go back to the inference page!"
except subprocess.CalledProcessError as e:
return f"ERROR - Download failed (gdown): {str(e)}"
except requests.exceptions.RequestException as e:
return f"ERROR - Download failed (requests): {str(e)}"
except Exception as e:
return f"ERROR - The test failed: {str(e)}"
def transfer_files(filething, dataset_dir='dataset/'):
file_names = [f.name for f in filething]
for f in file_names:
filename = os.path.basename(f)
destination = os.path.join(dataset_dir, filename)
shutil.copyfile(f, destination)
return "Transferred files to dataset directory!"
def if_done_multi(done, ps):
while 1:
flag = 1
for p in ps:
if p.poll() is None:
flag = 0
sleep(0.5)
break
if flag == 1:
break
done[0] = True
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
sr = sr_dict[sr]
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
f.close()
per = 3.0 if config.is_half else 3.7
cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
config.python_cmd,
trainset_dir,
sr,
n_p,
now_dir,
exp_dir,
config.noparallel,
per,
)
logger.info(cmd)
p = Popen(cmd, shell=True)
done = [False]
threading.Thread(
target=if_done,
args=(
done,
p,
),
).start()
while 1:
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
yield f.read()
sleep(1)
if done[0]:
break
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
logger.info(log)
yield log
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvpe):
gpus = gpus.split("-")
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
f.close()
if if_f0:
if f0method != "rmvpe_gpu":
cmd = (
'"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
% (
config.python_cmd,
now_dir,
exp_dir,
n_p,
f0method,
)
)
logger.info(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
)
done = [False]
threading.Thread(
target=if_done,
args=(
done,
p,
),
).start()
else:
if gpus_rmvpe != "-":
gpus_rmvpe = gpus_rmvpe.split("-")
leng = len(gpus_rmvpe)
ps = []
for idx, n_g in enumerate(gpus_rmvpe):
cmd = (
'"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
% (
config.python_cmd,
leng,
idx,
n_g,
now_dir,
exp_dir,
config.is_half,
)
)
logger.info(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
)
ps.append(p)
done = [False]
threading.Thread(
target=if_done_multi, #
args=(
done,
ps,
),
).start()
else:
cmd = (
config.python_cmd
+ ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
% (
now_dir,
exp_dir,
)
)
logger.info(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
)
p.wait()
done = [True]
while 1:
with open(
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
) as f:
yield f.read()
sleep(1)
if done[0]:
break
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
logger.info(log)
yield log
leng = len(gpus)
ps = []
for idx, n_g in enumerate(gpus):
cmd = (
'"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s'
% (
config.python_cmd,
config.device,
leng,
idx,
n_g,
now_dir,
exp_dir,
version19,
)
)
logger.info(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 1:
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
yield f.read()
sleep(1)
if done[0]:
break
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
logger.info(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 ""
return get_pretrained_models(path_str, f0_str, sr2)
def change_version19(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
if sr2 == "32k" and version19 == "v1":
sr2 = "40k"
to_return_sr2 = (
{"choices": ["32k","40k", "48k"], "__type__": "update", "value": sr2}
if version19 == "v1"
else {"choices": ["32k", "40k", "48k", "OV2-32k", "OV2-40k", "RIN-40k","Snowie-40k","Snowie-48k","Italia-32k"], "__type__": "update", "value": sr2}
)
f0_str = "f0" if if_f0_3 else ""
return (
*get_pretrained_models(path_str, f0_str, sr2),
to_return_sr2,
)
def change_f0(if_f0_3, sr2, version19):
path_str = "" if version19 == "v1" else "_v2"
return (
{"visible": if_f0_3, "__type__": "update"},
{"visible": if_f0_3, "__type__": "update"},
*get_pretrained_models(path_str, "f0" if if_f0_3 is True else "", sr2),
)
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,
):
global f0_dir, f0nsf_dir
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
os.makedirs(exp_dir, exist_ok=True)
gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
feature_dir = (
"%s/3_feature256" % exp_dir
if version19 == "v1"
else "%s/3_feature768" % exp_dir
)
if if_f0_3:
f0_dir = "%s/2a_f0" % exp_dir
f0nsf_dir = "%s/2b-f0nsf" % exp_dir
names = (
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
)
else:
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
[name.split(".")[0] for name in os.listdir(feature_dir)]
)
opt = []
for name in names:
if if_f0_3:
opt.append(
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
f0_dir.replace("\\", "\\\\"),
name,
f0nsf_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
else:
opt.append(
"%s/%s.wav|%s/%s.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
fea_dim = 256 if version19 == "v1" else 768
if if_f0_3:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy"
"|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
)
else:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
f.write("\n".join(opt))
logger.debug("Write filelist done")
logger.info("Use gpus: %s", str(gpus16))
if pretrained_G14 == "":
logger.info("No pretrained Generator")
if pretrained_D15 == "":
logger.info("No pretrained Discriminator")
if version19 == "v1" or sr2 == "40k":
config_path = "v1/%s.json" % sr2
else:
config_path = "v2/%s.json" % sr2
config_save_path = os.path.join(exp_dir, "config.json")
if not pathlib.Path(config_save_path).exists():
with open(config_save_path, "w", encoding="utf-8") as f:
json.dump(
config.json_config[config_path],
f,
ensure_ascii=False,
indent=4,
sort_keys=True,
)
f.write("\n")
if gpus16:
cmd = (
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s '
"-sw %s -v %s"
% (
config.python_cmd,
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
gpus16,
total_epoch11,
save_epoch10,
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
1 if if_save_latest13 == i18n("是") else 0,
1 if if_cache_gpu17 == i18n("是") else 0,
1 if if_save_every_weights18 == i18n("是") else 0,
version19,
)
)
else:
cmd = (
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw '
"%s -v %s"
% (
config.python_cmd,
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
total_epoch11,
save_epoch10,
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
1 if if_save_latest13 == i18n("是") else 0,
1 if if_cache_gpu17 == i18n("是") else 0,
1 if if_save_every_weights18 == i18n("是") else 0,
version19,
)
)
logger.info(cmd)
p = Popen(cmd, shell=True, cwd=now_dir)
p.wait()
return "You can view console or train.log"
def train_index(exp_dir1, version19):
exp_dir = "logs/%s" % exp_dir1
os.makedirs(exp_dir, exist_ok=True)
feature_dir = (
"%s/3_feature256" % exp_dir
if version19 == "v1"
else "%s/3_feature768" % exp_dir
)
if not os.path.exists(feature_dir):
return "Please perform Feature Extraction First!"
listdir_res = list(os.listdir(feature_dir))
if len(listdir_res) == 0:
return "Please perform Feature Extraction First！"
infos = []
npys = []
for name in sorted(listdir_res):
phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone)
big_npy = np.concatenate(npys, 0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
if big_npy.shape[0] > 2e5:
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:
info = traceback.format_exc()
logger.info(info)
infos.append(info)
yield "\n".join(infos)
np.save("%s/total_fea.npy" % exp_dir, 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(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
infos.append("training")
yield "\n".join(infos)
index_ivf = faiss.extract_index_ivf(index) #
index_ivf.nprobe = 1
index.train(big_npy)
faiss.write_index(
index,
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
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])
faiss.write_index(
index,
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
infos.append(
"Success，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
)
yield "\n".join(infos)
F0GPUVisible = config.dml is False
def change_f0_method(f0method8):
if f0method8 == "rmvpe_gpu":
visible = F0GPUVisible
else:
visible = False
return {"visible": visible, "__type__": "update"}
vc_output1 = gr.Textbox(label=i18n("Console"))
vc_output2 = gr.Audio(label=i18n("Audio output"))
with gr.Blocks(title="Ilaria RVC 💖") as app:
gr.Markdown("<h1> Ilaria RVC 💖 </h1>")
gr.Markdown(value=i18n("Made with 💖 by Ilaria | Support her on [Ko-Fi](https://ko-fi.com/ilariaowo)"))
gr.Markdown(i18n("For voice models and support join [AI Hub](https://discord.gg/AIHUB)"))
with gr.Tabs():
with gr.TabItem(i18n("Inference")):
with gr.Row():
sid0= gr.Dropdown(label=i18n("Voice"), choices=sorted(names))
sid1= sid0
with gr.Column():
refresh_button = gr.Button(i18n("Refresh"), variant="primary")
clean_button = gr.Button(i18n("Unload Voice from VRAM"), variant="primary")
vc_transform0 = gr.inputs.Slider(
label=i18n(
"Pitch: -24 is lower (2 octaves) and 24 is higher (2 octaves)"),
minimum=-24,
maximum=24,
default=0,
step=1,
)
clean_button.click(
fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
)
with gr.TabItem(i18n("Inference")):
with gr.Group():
with gr.Row():
with gr.Column():
input_audio1 = gr.Audio(
label=i18n("Upload Audio file"),
type="filepath",
)
record_button = gr.Audio(source="microphone", label="Use your microphone",
type="filepath")
input_audio0 = gr.Dropdown(
label=i18n("Select a file from the audio folder"),
choices=sorted(audio_paths),
value='',
interactive=True,
)
record_button.change(
fn=lambda x: x,
inputs=[record_button],
outputs=[input_audio0],
)
file_index1 = gr.Textbox(
label=i18n("Path of index"),
placeholder=".\models\index",
interactive=True,
visible=False,
)
file_index2 = gr.Textbox(
label=i18n("Auto-detect index path"),
choices=sorted(index_paths),
interactive=True,
visible=False,
)
with gr.Column():
with gr.Accordion('Advanced Settings', open=False, visible=False):
with gr.Column():
f0method0 = gr.Radio(
label=i18n("Pitch Extraction, rmvpe is best"),
choices=["harvest", "crepe", "rmvpe"]
if config.dml is False
else ["harvest", "rmvpe"],
value="rmvpe",
interactive=True,
)
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n("Resampling, 0=none"),
value=0,
step=1,
interactive=True,
)
rms_mix_rate0 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("0=Input source volume, 1=Normalized Output"),
value=0.25,
interactive=True,
)
protect0 = gr.Slider(
minimum=0,
maximum=0.5,