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core.py
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import sys, os
import subprocess
import torch
from functools import lru_cache
import shutil
from pedalboard import Pedalboard, Reverb
from pedalboard.io import AudioFile
from pydub import AudioSegment
from audio_separator.separator import Separator
import logging
import yaml
from programs.applio_code.rvc.infer.infer import VoiceConverter
from programs.applio_code.rvc.lib.tools.model_download import model_download_pipeline
from programs.music_separation_code.inference import proc_file
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
now_dir = os.getcwd()
sys.path.append(now_dir)
models_vocals = [
{
"name": "Mel-Roformer by KimberleyJSN",
"path": os.path.join(now_dir, "models", "mel-vocals"),
"model": os.path.join(now_dir, "models", "mel-vocals", "model.ckpt"),
"config": os.path.join(now_dir, "models", "mel-vocals", "config.yaml"),
"type": "mel_band_roformer",
"config_url": "https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/KimberleyJensen/config_vocals_mel_band_roformer_kj.yaml",
"model_url": "https://huggingface.co/KimberleyJSN/melbandroformer/resolve/main/MelBandRoformer.ckpt",
},
{
"name": "BS-Roformer by ViperX",
"path": os.path.join(now_dir, "models", "bs-vocals"),
"model": os.path.join(now_dir, "models", "bs-vocals", "model.ckpt"),
"config": os.path.join(now_dir, "models", "bs-vocals", "config.yaml"),
"type": "bs_roformer",
"config_url": "https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/viperx/model_bs_roformer_ep_317_sdr_12.9755.yaml",
"model_url": "/~https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_317_sdr_12.9755.ckpt",
},
{
"name": "MDX23C",
"path": os.path.join(now_dir, "models", "mdx23c-vocals"),
"model": os.path.join(now_dir, "models", "mdx23c-vocals", "model.ckpt"),
"config": os.path.join(now_dir, "models", "mdx23c-vocals", "config.yaml"),
"type": "mdx23c",
"config_url": "https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/config_vocals_mdx23c.yaml",
"model_url": "/~https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.0/model_vocals_mdx23c_sdr_10.17.ckpt",
},
]
karaoke_models = [
{
"name": "Mel-Roformer Karaoke by aufr33 and viperx",
"path": os.path.join(now_dir, "models", "mel-kara"),
"model": os.path.join(now_dir, "models", "mel-kara", "model.ckpt"),
"config": os.path.join(now_dir, "models", "mel-kara", "config.yaml"),
"type": "mel_band_roformer",
"config_url": "https://huggingface.co/shiromiya/audio-separation-models/resolve/main/mel_band_roformer_karaoke_aufr33_viperx/config_mel_band_roformer_karaoke.yaml",
"model_url": "https://huggingface.co/shiromiya/audio-separation-models/resolve/main/mel_band_roformer_karaoke_aufr33_viperx/mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt",
},
{
"name": "UVR-BVE",
"full_name": "UVR-BVE-4B_SN-44100-1.pth",
"arch": "vr",
},
]
denoise_models = [
{
"name": "Mel-Roformer Denoise Normal by aufr33",
"path": os.path.join(now_dir, "models", "mel-denoise"),
"model": os.path.join(now_dir, "models", "mel-denoise", "model.ckpt"),
"config": os.path.join(now_dir, "models", "mel-denoise", "config.yaml"),
"type": "mel_band_roformer",
"config_url": "https://huggingface.co/shiromiya/audio-separation-models/resolve/main/mel-denoise/model_mel_band_roformer_denoise.yaml",
"model_url": "https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt",
},
{
"name": "Mel-Roformer Denoise Aggressive by aufr33",
"path": os.path.join(now_dir, "models", "mel-denoise-aggr"),
"model": os.path.join(now_dir, "models", "mel-denoise-aggr", "model.ckpt"),
"config": os.path.join(now_dir, "models", "mel-denoise-aggr", "config.yaml"),
"type": "mel_band_roformer",
"config_url": "https://huggingface.co/shiromiya/audio-separation-models/resolve/main/mel-denoise/model_mel_band_roformer_denoise.yaml",
"model_url": "https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt",
},
{
"name": "UVR Denoise",
"full_name": "UVR-DeNoise.pth",
"arch": "vr",
},
]
dereverb_models = [
{
"name": "MDX23C DeReverb by aufr33 and jarredou",
"path": os.path.join(now_dir, "models", "mdx23c-dereveb"),
"model": os.path.join(now_dir, "models", "mdx23c-dereveb", "model.ckpt"),
"config": os.path.join(now_dir, "models", "mdx23c-dereveb", "config.yaml"),
"type": "mdx23c",
"config_url": "https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/config_dereverb_mdx23c.yaml",
"model_url": "https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/dereverb_mdx23c_sdr_6.9096.ckpt",
},
{
"name": "BS-Roformer Dereverb by anvuew",
"path": os.path.join(now_dir, "models", "mdx23c-dereveb"),
"model": os.path.join(now_dir, "models", "mdx23c-dereveb", "model.ckpt"),
"config": os.path.join(now_dir, "models", "mdx23c-dereveb", "config.yaml"),
"type": "bs_roformer",
"config_url": "https://huggingface.co/anvuew/deverb_bs_roformer/resolve/main/deverb_bs_roformer_8_384dim_10depth.yaml",
"model_url": "https://huggingface.co/anvuew/deverb_bs_roformer/resolve/main/deverb_bs_roformer_8_384dim_10depth.ckpt",
},
{
"name": "UVR-Deecho-Dereverb",
"full_name": "UVR-DeEcho-DeReverb.pth",
"arch": "vr",
},
{
"name": "MDX Reverb HQ by FoxJoy",
"full_name": "Reverb_HQ_By_FoxJoy.onnx",
"arch": "mdx",
},
]
deecho_models = [
{
"name": "UVR-Deecho-Normal",
"full_name": "UVR-De-Echo-Normal.pth",
"arch": "vr",
},
{
"name": "UVR-Deecho-Agggressive",
"full_name": "UVR-De-Echo-Aggressive.pth",
"arch": "vr",
},
]
@lru_cache(maxsize=None)
def import_voice_converter():
from programs.applio_code.rvc.infer.infer import VoiceConverter
return VoiceConverter()
@lru_cache(maxsize=1)
def get_config():
from programs.applio_code.rvc.configs.config import Config
return Config()
def download_file(url, path, filename):
os.makedirs(path, exist_ok=True)
file_path = os.path.join(path, filename)
if os.path.exists(file_path):
print(f"File '{filename}' already exists at '{path}'.")
return
try:
response = torch.hub.download_url_to_file(url, file_path)
print(f"File '{filename}' downloaded successfully")
except Exception as e:
print(f"Error downloading file '{filename}' from '{url}': {e}")
def get_model_info_by_name(model_name):
all_models = (
models_vocals
+ karaoke_models
+ dereverb_models
+ deecho_models
+ denoise_models
)
for model in all_models:
if model["name"] == model_name:
return model
return None
def get_last_modified_file(pasta):
if not os.path.isdir(pasta):
raise NotADirectoryError(f"{pasta} is not a valid directory.")
arquivos = [f for f in os.listdir(pasta) if os.path.isfile(os.path.join(pasta, f))]
if not arquivos:
return None
return max(arquivos, key=lambda x: os.path.getmtime(os.path.join(pasta, x)))
def search_with_word(folder, word):
if not os.path.isdir(folder):
raise NotADirectoryError(f"{folder} is not a valid directory.")
file_with_word = [file for file in os.listdir(folder) if word in file]
if not file_with_word:
return None
most_recent_file = max(
file_with_word, key=lambda file: os.path.getmtime(os.path.join(folder, file))
)
return most_recent_file
def search_with_two_words(folder, word1, word2):
if not os.path.isdir(folder):
raise NotADirectoryError(f"{folder} is not a valid directory.")
file_with_words = [
file for file in os.listdir(folder) if word1 in file and word2 in file
]
if not file_with_words:
return None
most_recent_file = max(
file_with_words, key=lambda file: os.path.getmtime(os.path.join(folder, file))
)
return most_recent_file
def get_last_modified_folder(path):
directories = [
os.path.join(path, d)
for d in os.listdir(path)
if os.path.isdir(os.path.join(path, d))
]
if not directories:
return None
last_modified_folder = max(directories, key=os.path.getmtime)
return last_modified_folder
def add_audio_effects(
audio_path,
reverb_size,
reverb_wet,
reverb_dry,
reverb_damping,
reverb_width,
output_path,
):
board = Pedalboard([])
board.append(
Reverb(
room_size=reverb_size,
dry_level=reverb_dry,
wet_level=reverb_wet,
damping=reverb_damping,
width=reverb_width,
)
)
with AudioFile(audio_path) as f:
with AudioFile(output_path, "w", f.samplerate, f.num_channels) as o:
while f.tell() < f.frames:
chunk = f.read(int(f.samplerate))
effected = board(chunk, f.samplerate, reset=False)
o.write(effected)
return output_path
def merge_audios(
vocals_path,
inst_path,
backing_path,
output_path,
main_gain,
inst_gain,
backing_Vol,
output_format,
):
main_vocal_audio = AudioSegment.from_file(vocals_path, format="flac") + main_gain
instrumental_audio = AudioSegment.from_file(inst_path, format="flac") + inst_gain
backing_vocal_audio = (
AudioSegment.from_file(backing_path, format="flac") + backing_Vol
)
combined_audio = main_vocal_audio.overlay(
instrumental_audio.overlay(backing_vocal_audio)
)
combined_audio.export(output_path, format=output_format)
return output_path
def check_fp16_support(device):
i_device = int(str(device).split(":")[-1])
gpu_name = torch.cuda.get_device_name(i_device)
low_end_gpus = ["16", "P40", "P10", "1060", "1070", "1080"]
if any(gpu in gpu_name for gpu in low_end_gpus) and "V100" not in gpu_name.upper():
print(f"Your GPU {gpu_name} not support FP16 inference. Using FP32 instead.")
return False
return True
def full_inference_program(
model_path,
index_path,
input_audio_path,
output_path,
export_format_rvc,
split_audio,
autotune,
vocal_model,
karaoke_model,
dereverb_model,
deecho,
deecho_model,
denoise,
denoise_model,
reverb,
vocals_volume,
instrumentals_volume,
backing_vocals_volume,
export_format_final,
devices,
pitch,
filter_radius,
index_rate,
rms_mix_rate,
protect,
pitch_extract,
hop_lenght,
reverb_room_size,
reverb_damping,
reverb_wet_gain,
reverb_dry_gain,
reverb_width,
embedder_model,
delete_audios,
use_tta,
batch_size,
infer_backing_vocals,
infer_backing_vocals_model,
infer_backing_vocals_index,
change_inst_pitch,
pitch_back,
filter_radius_back,
index_rate_back,
rms_mix_rate_back,
protect_back,
pitch_extract_back,
hop_length_back,
export_format_rvc_back,
split_audio_back,
autotune_back,
embedder_model_back,
):
if torch.cuda.is_available():
n_gpu = torch.cuda.device_count()
devices = devices.replace("-", " ")
print(f"Number of GPUs available: {n_gpu}")
first_device = devices.split()[0]
fp16 = check_fp16_support(first_device)
else:
devices = "cpu"
print("Using CPU")
fp16 = False
music_folder = os.path.splitext(os.path.basename(input_audio_path))[0]
# Vocals Separation
model_info = get_model_info_by_name(vocal_model)
model_ckpt_path = os.path.join(model_info["path"], "model.ckpt")
if not os.path.exists(model_ckpt_path):
download_file(
model_info["model_url"],
model_info["path"],
"model.ckpt",
)
config_json_path = os.path.join(model_info["path"], "config.yaml")
if not os.path.exists(config_json_path):
download_file(
model_info["config_url"],
model_info["path"],
"config.yaml",
)
if not fp16:
with open(model_info["config"], "r") as file:
config = yaml.safe_load(file)
config["training"]["use_amp"] = False
with open(model_info["config"], "w") as file:
yaml.safe_dump(config, file)
store_dir = os.path.join(now_dir, "audio_files", music_folder, "vocals")
inst_dir = os.path.join(now_dir, "audio_files", music_folder, "instrumentals")
os.makedirs(store_dir, exist_ok=True)
os.makedirs(inst_dir, exist_ok=True)
input_audio_basename = os.path.splitext(os.path.basename(input_audio_path))[0]
search_result = search_with_word(store_dir, "vocals")
if search_result:
print("Vocals already separated..."),
else:
print("Separating vocals...")
command = [
"python",
os.path.join(now_dir, "programs", "music_separation_code", "inference.py"),
"--model_type",
model_info["type"],
"--config_path",
model_info["config"],
"--start_check_point",
model_info["model"],
"--input_file",
input_audio_path,
"--store_dir",
store_dir,
"--flac_file",
"--pcm_type",
"PCM_16",
"--extract_instrumental",
]
if devices == "cpu":
command.append("--force_cpu")
else:
device_ids = [str(int(device)) for device in devices.split()]
command.extend(["--device_ids"] + device_ids)
subprocess.run(command)
os.rename(
os.path.join(
store_dir,
search_with_two_words(
store_dir,
os.path.basename(input_audio_path).split(".")[0],
"instrumental",
),
),
os.path.join(
inst_dir,
f"{os.path.basename(input_audio_path).split('.')[0]}_instrumentals.flac",
),
)
inst_file = os.path.join(
inst_dir,
search_with_two_words(
inst_dir, os.path.basename(input_audio_path).split(".")[0], "instrumentals"
),
)
# karaoke separation
model_info = get_model_info_by_name(karaoke_model)
store_dir = os.path.join(now_dir, "audio_files", music_folder, "karaoke")
os.makedirs(store_dir, exist_ok=True)
vocals_path = os.path.join(now_dir, "audio_files", music_folder, "vocals")
input_file = search_with_word(vocals_path, "vocals")
karaoke_exists = search_with_word(store_dir, "karaoke") is not None
if karaoke_exists:
print("Backing vocals already separated...")
else:
if input_file:
input_file = os.path.join(vocals_path, input_file)
print("Separating Backing vocals...")
if model_info["name"] == "Mel-Roformer Karaoke by aufr33 and viperx":
model_ckpt_path = os.path.join(model_info["path"], "model.ckpt")
if not os.path.exists(model_ckpt_path):
download_file(
model_info["model_url"],
model_info["path"],
"model.ckpt",
)
config_json_path = os.path.join(model_info["path"], "config.yaml")
if not os.path.exists(config_json_path):
download_file(
model_info["config_url"],
model_info["path"],
"config.yaml",
)
if not fp16:
with open(model_info["config"], "r") as file:
config = yaml.safe_load(file)
config["training"]["use_amp"] = False
with open(model_info["config"], "w") as file:
yaml.safe_dump(config, file)
command = [
"python",
os.path.join(
now_dir, "programs", "music_separation_code", "inference.py"
),
"--model_type",
model_info["type"],
"--config_path",
model_info["config"],
"--start_check_point",
model_info["model"],
"--input_file",
input_file,
"--store_dir",
store_dir,
"--flac_file",
"--pcm_type",
"PCM_16",
"--extract_instrumental",
]
if devices == "cpu":
command.append("--force_cpu")
else:
device_ids = [str(int(device)) for device in devices.split()]
command.extend(["--device_ids"] + device_ids)
subprocess.run(command)
else:
separator = Separator(
model_file_dir=os.path.join(now_dir, "models", "karaoke"),
log_level=logging.WARNING,
normalization_threshold=1.0,
output_format="flac",
output_dir=store_dir,
vr_params={
"batch_size": batch_size,
"enable_tta": use_tta,
},
)
separator.load_model(model_filename=model_info["full_name"])
separator.separate(input_file)
karaoke_path = os.path.join(now_dir, "audio_files", music_folder, "karaoke")
vocals_result = search_with_two_words(
karaoke_path,
os.path.basename(input_audio_path).split(".")[0],
"Vocals",
)
instrumental_result = search_with_two_words(
karaoke_path,
os.path.basename(input_audio_path).split(".")[0],
"Instrumental",
)
if "UVR-BVE-4B_SN-44100-1" in os.path.basename(vocals_result):
os.rename(
os.path.join(karaoke_path, vocals_result),
os.path.join(
karaoke_path,
f"{os.path.basename(input_audio_path).split('.')[0]}_karaoke.flac",
),
)
if "UVR-BVE-4B_SN-44100-1" in os.path.basename(instrumental_result):
os.rename(
os.path.join(karaoke_path, instrumental_result),
os.path.join(
karaoke_path,
f"{os.path.basename(input_audio_path).split('.')[0]}_instrumental.flac",
),
)
# dereverb
model_info = get_model_info_by_name(dereverb_model)
store_dir = os.path.join(now_dir, "audio_files", music_folder, "dereverb")
os.makedirs(store_dir, exist_ok=True)
karaoke_path = os.path.join(now_dir, "audio_files", music_folder, "karaoke")
input_file = search_with_word(karaoke_path, "karaoke")
noreverb_exists = search_with_word(store_dir, "noreverb") is not None
if noreverb_exists:
print("Reverb already removed...")
else:
if input_file:
input_file = os.path.join(karaoke_path, input_file)
print("Removing reverb...")
if (
model_info["name"] == "BS-Roformer Dereverb by anvuew"
or model_info["name"] == "MDX23C DeReverb by aufr33 and jarredou"
):
model_ckpt_path = os.path.join(model_info["path"], "model.ckpt")
if not os.path.exists(model_ckpt_path):
download_file(
model_info["model_url"],
model_info["path"],
"model.ckpt",
)
config_json_path = os.path.join(model_info["path"], "config.yaml")
if not os.path.exists(config_json_path):
download_file(
model_info["config_url"],
model_info["path"],
"config.yaml",
)
if not fp16:
with open(model_info["config"], "r") as file:
config = yaml.safe_load(file)
config["training"]["use_amp"] = False
with open(model_info["config"], "w") as file:
yaml.safe_dump(config, file)
command = [
"python",
os.path.join(
now_dir, "programs", "music_separation_code", "inference.py"
),
"--model_type",
model_info["type"],
"--config_path",
model_info["config"],
"--start_check_point",
model_info["model"],
"--input_file",
input_file,
"--store_dir",
store_dir,
"--flac_file",
"--pcm_type",
"PCM_16",
]
if devices == "cpu":
command.append("--force_cpu")
else:
device_ids = [str(int(device)) for device in devices.split()]
command.extend(["--device_ids"] + device_ids)
subprocess.run(command)
else:
if model_info["arch"] == "vr":
separator = Separator(
model_file_dir=os.path.join(now_dir, "models", "dereverb"),
log_level=logging.WARNING,
normalization_threshold=1.0,
output_format="flac",
output_dir=store_dir,
output_single_stem="No Reverb",
vr_params={
"batch_size": batch_size,
"enable_tta": use_tta,
},
)
else:
separator = Separator(
model_file_dir=os.path.join(now_dir, "models", "dereverb"),
log_level=logging.WARNING,
normalization_threshold=1.0,
output_format="flac",
output_dir=store_dir,
output_single_stem="No Reverb",
)
separator.load_model(model_filename=model_info["full_name"])
separator.separate(input_file)
dereverb_path = os.path.join(
now_dir, "audio_files", music_folder, "dereverb"
)
search_result = search_with_two_words(
dereverb_path,
os.path.basename(input_audio_path).split(".")[0],
"No Reverb",
)
if "UVR-DeEcho-DeReverb" in os.path.basename(
search_result
) or "MDX Reverb HQ by FoxJoy" in os.path.basename(search_result):
os.rename(
os.path.join(dereverb_path, search_result),
os.path.join(
dereverb_path,
f"{os.path.basename(input_audio_path).split('.')[0]}_noreverb.flac",
),
)
# deecho
store_dir = os.path.join(now_dir, "audio_files", music_folder, "deecho")
os.makedirs(store_dir, exist_ok=True)
if deecho:
no_echo_exists = search_with_word(store_dir, "noecho") is not None
if no_echo_exists:
print("Echo already removed...")
else:
print("Removing echo...")
model_info = get_model_info_by_name(deecho_model)
dereverb_path = os.path.join(
now_dir, "audio_files", music_folder, "dereverb"
)
noreverb_file = search_with_word(dereverb_path, "noreverb")
input_file = os.path.join(dereverb_path, noreverb_file)
separator = Separator(
model_file_dir=os.path.join(now_dir, "models", "deecho"),
log_level=logging.WARNING,
normalization_threshold=1.0,
output_format="flac",
output_dir=store_dir,
output_single_stem="No Echo",
vr_params={
"batch_size": batch_size,
"enable_tta": use_tta,
},
)
separator.load_model(model_filename=model_info["full_name"])
separator.separate(input_file)
deecho_path = os.path.join(now_dir, "audio_files", music_folder, "deecho")
search_result = search_with_two_words(
deecho_path,
os.path.basename(input_audio_path).split(".")[0],
"No Echo",
)
if "UVR-De-Echo-Normal" in os.path.basename(
search_result
) or "UVR-Deecho-Agggressive" in os.path.basename(search_result):
os.rename(
os.path.join(deecho_path, search_result),
os.path.join(
deecho_path,
f"{os.path.basename(input_audio_path).split('.')[0]}_noecho.flac",
),
)
# denoise
store_dir = os.path.join(now_dir, "audio_files", music_folder, "denoise")
os.makedirs(store_dir, exist_ok=True)
if denoise:
no_noise_exists = search_with_word(store_dir, "dry") is not None
if no_noise_exists:
print("Noise already removed")
else:
model_info = get_model_info_by_name(denoise_model)
print("Removing noise")
input_file = (
os.path.join(
now_dir,
"audio_files",
music_folder,
"deecho",
search_with_word(
os.path.join(now_dir, "audio_files", music_folder, "deecho"),
"noecho",
),
)
if deecho
else os.path.join(
now_dir,
"audio_files",
music_folder,
"dereverb",
search_with_word(
os.path.join(now_dir, "audio_files", music_folder, "dereverb"),
"noreverb",
),
)
)
if (
model_info["name"] == "Mel-Roformer Denoise Normal by aufr33"
or model_info["name"] == "Mel-Roformer Denoise Aggressive by aufr33"
):
model_ckpt_path = os.path.join(model_info["path"], "model.ckpt")
if not os.path.exists(model_ckpt_path):
download_file(
model_info["model_url"],
model_info["path"],
"model.ckpt",
)
config_json_path = os.path.join(model_info["path"], "config.yaml")
if not os.path.exists(config_json_path):
download_file(
model_info["config_url"], model_info["path"], "config.yaml"
)
if not fp16:
with open(model_info["config"], "r") as file:
config = yaml.safe_load(file)
config["training"]["use_amp"] = False
with open(model_info["config"], "w") as file:
yaml.safe_dump(config, file)
command = [
"python",
os.path.join(
now_dir, "programs", "music_separation_code", "inference.py"
),
"--model_type",
model_info["type"],
"--config_path",
model_info["config"],
"--start_check_point",
model_info["model"],
"--input_file",
input_file,
"--store_dir",
store_dir,
"--flac_file",
"--pcm_type",
"PCM_16",
]
if devices == "cpu":
command.append("--force_cpu")
else:
device_ids = [str(int(device)) for device in devices.split()]
command.extend(["--device_ids"] + device_ids)
subprocess.run(command)
else:
separator = Separator(
model_file_dir=os.path.join(now_dir, "models", "denoise"),
log_level=logging.WARNING,
normalization_threshold=1.0,
output_format="flac",
output_dir=store_dir,
output_single_stem="No Noise",
vr_params={
"batch_size": batch_size,
"enable_tta": use_tta,
},
)
separator.load_model(model_filename=model_info["full_name"])
separator.separate(input_file)
search_result = search_with_two_words(
deecho_path,
os.path.basename(input_audio_path).split(".")[0],
"No Noise",
)
if "UVR Denoise" in os.path.basename(search_result):
os.rename(
os.path.join(deecho_path, search_result),
os.path.join(
deecho_path,
f"{os.path.basename(input_audio_path).split('.')[0]}_dry.flac",
),
)
# RVC
denoise_path = os.path.join(now_dir, "audio_files", music_folder, "denoise")
deecho_path = os.path.join(now_dir, "audio_files", music_folder, "deecho")
dereverb_path = os.path.join(now_dir, "audio_files", music_folder, "dereverb")
denoise_audio = search_with_two_words(
denoise_path, os.path.basename(input_audio_path).split(".")[0], "dry"
)
deecho_audio = search_with_two_words(
deecho_path, os.path.basename(input_audio_path).split(".")[0], "noecho"
)
dereverb = search_with_two_words(
dereverb_path, os.path.basename(input_audio_path).split(".")[0], "noreverb"
)
if denoise_audio:
final_path = os.path.join(
now_dir, "audio_files", music_folder, "denoise", denoise_audio
)
elif deecho_audio:
final_path = os.path.join(
now_dir, "audio_files", music_folder, "deecho", deecho_audio
)
elif dereverb:
final_path = os.path.join(
now_dir, "audio_files", music_folder, "dereverb", dereverb
)
else:
final_path = None
store_dir = os.path.join(now_dir, "audio_files", music_folder, "rvc")
os.makedirs(store_dir, exist_ok=True)
print("Making RVC inference...")
output_rvc = os.path.join(
now_dir,
"audio_files",
music_folder,
"rvc",
f"{os.path.basename(input_audio_path).split('.')[0]}_rvc.wav",
)
inference_vc = import_voice_converter()
inference_vc.convert_audio(
audio_input_path=final_path,
audio_output_path=output_rvc,
model_path=model_path,
index_path=index_path,
embedder_model=embedder_model,
pitch=pitch,
f0_file=None,
f0_method=pitch_extract,
filter_radius=filter_radius,
index_rate=index_rate,
volume_envelope=rms_mix_rate,
protect=protect,
split_audio=split_audio,
f0_autotune=autotune,
hop_length=hop_lenght,
export_format=export_format_rvc,
embedder_model_custom=None,
)
backing_vocals = os.path.join(
karaoke_path, search_with_word(karaoke_path, "instrumental")
)
if infer_backing_vocals:
print("Infering backing vocals...")
karaoke_path = os.path.join(now_dir, "audio_files", music_folder, "karaoke")
instrumental_file = search_with_word(karaoke_path, "instrumental")
backing_vocals = os.path.join(karaoke_path, instrumental_file)
output_backing_vocals = os.path.join(
karaoke_path, f"{input_audio_basename}_instrumental_output.wav"
)
inference_vc.convert_audio(
audio_input_path=backing_vocals,
audio_output_path=output_backing_vocals,
model_path=infer_backing_vocals_model,
index_path=infer_backing_vocals_index,
embedder_model=embedder_model_back,
pitch=pitch_back,
f0_file=None,
f0_method=pitch_extract_back,
filter_radius=filter_radius_back,
index_rate=index_rate_back,
volume_envelope=rms_mix_rate_back,
protect=protect_back,
split_audio=split_audio_back,
f0_autotune=autotune_back,
hop_length=hop_length_back,
export_format=export_format_rvc_back,
embedder_model_custom=None,
)
backing_vocals = output_backing_vocals
# post process
if reverb:
add_audio_effects(
os.path.join(
now_dir,
"audio_files",
music_folder,
"rvc",
get_last_modified_file(
os.path.join(now_dir, "audio_files", music_folder, "rvc")
),
),
reverb_room_size,
reverb_wet_gain,
reverb_dry_gain,
reverb_damping,
reverb_width,
os.path.join(
now_dir,
"audio_files",
music_folder,
"rvc",
os.path.basename(input_audio_path),
),
)
if change_inst_pitch != 0:
print("Changing instrumental pitch...")
inst_path = os.path.join(
now_dir,
"audio_files",
music_folder,
"instrumentals",
search_with_word(
os.path.join(now_dir, "audio_files", music_folder, "instrumentals"),
"instrumentals",
),
)
audio = AudioSegment.from_file(inst_path)
factor = 2 ** (change_inst_pitch / 12)
new_frame_rate = int(audio.frame_rate * factor)
audio = audio._spawn(audio.raw_data, overrides={"frame_rate": new_frame_rate})
audio = audio.set_frame_rate(audio.frame_rate)
output_dir_pitch = os.path.join(
now_dir, "audio_files", music_folder, "instrumentals"
)
output_path_pitch = os.path.join(
output_dir_pitch, "inst_with_changed_pitch.flac"
)
audio.export(output_path_pitch, format="flac")
# merge audios
store_dir = os.path.join(now_dir, "audio_files", music_folder, "final")
os.makedirs(store_dir, exist_ok=True)
vocals_path = os.path.join(now_dir, "audio_files", music_folder, "rvc")
vocals_file = get_last_modified_file(
os.path.join(now_dir, "audio_files", music_folder, "rvc")
)
vocals_file = os.path.join(vocals_path, vocals_file)
karaoke_path = os.path.join(now_dir, "audio_files", music_folder, "karaoke")
karaoke_file = search_with_word(karaoke_path, "Instrumental") or search_with_word(
karaoke_path, "instrumental"
)
karaoke_file = os.path.join(karaoke_path, karaoke_file)
final_output_path = os.path.join(
now_dir,
"audio_files",
music_folder,
"final",
f"{os.path.basename(input_audio_path).split('.')[0]}_final.{export_format_final.lower()}",
)
print("Merging audios...")
result = merge_audios(
vocals_file,
inst_file,
backing_vocals,
final_output_path,
vocals_volume,
instrumentals_volume,
backing_vocals_volume,
export_format_final,
)
print("Audios merged...")
if delete_audios:
main_directory = os.path.join(now_dir, "audio_files", music_folder)