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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "0pKllbPyK_BC"
},
"source": [
"# Applio NoUI\n",
"Created by [Blaise](https://github.com/blaise-tk) with [Vidal](https://github.com/Vidalnt) and [Poopmaster](https://github.com/poiqazwsx). Based on [RVC_CLI](https://github.com/blaise-tk/RVC_CLI).\n",
"\n",
"- Colab inspired on [RVC v2 Disconnected](https://colab.research.google.com/drive/1XIPCP9ken63S7M6b5ui1b36Cs17sP-NS).\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y-iR3WeLMlac"
},
"source": [
"### If you restart the runtime, run it again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xwZkZGd-H0zT"
},
"outputs": [],
"source": [
"%cd /content/Applio"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ymMCTSD6m8qV"
},
"source": [
"# Installation\n",
"## If the runtime restarts, run the cell above and re-run the installation steps."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "yFhAeKGOp9aa"
},
"outputs": [],
"source": [
"# @title Mount Google Drive\n",
"from google.colab import drive\n",
"\n",
"drive.mount(\"/content/drive\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "7GysECSxBya4"
},
"outputs": [],
"source": [
"# @title Clone\n",
"!git clone https://github.com/IAHispano/Applio\n",
"%cd /content/Applio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "CAXW55BQm0PP"
},
"outputs": [],
"source": [
"# @title Install\n",
"rot_47 = lambda encoded_text: \"\".join(\n",
" [\n",
" (\n",
" chr(\n",
" (ord(c) - (ord(\"a\") if c.islower() else ord(\"A\")) - 47) % 26\n",
" + (ord(\"a\") if c.islower() else ord(\"A\"))\n",
" )\n",
" if c.isalpha()\n",
" else c\n",
" )\n",
" for c in encoded_text\n",
" ]\n",
")\n",
"import codecs\n",
"import os\n",
"import shutil\n",
"import tarfile\n",
"import subprocess\n",
"from pathlib import Path\n",
"from datetime import datetime\n",
"E = Exception\n",
"B = print\n",
"\n",
"\n",
"def vidal_setup(ForceIn):\n",
" L = \"Kikpm.ovm.bu\"\n",
" K = \"/content/\"\n",
" C = ForceIn\n",
"\n",
" def F():\n",
" print(\"Installing pip packages...\")\n",
" subprocess.check_call([\"pip\", \"install\", \"-r\", \"requirements.txt\", \"--quiet\"])\n",
"\n",
" A = K + rot_47(L)\n",
" G = K + rot_47(L)\n",
" D = \"/\"\n",
" if not os.path.exists(A):\n",
" M = os.path.dirname(A)\n",
" os.makedirs(M, exist_ok=True)\n",
" print(\"No cached install found..\")\n",
" try:\n",
" N = rot_47(\n",
" codecs.decode(\n",
" \"pbbxa://pcooqvonikm.kw/QIPqaxivw/Ixxtqw/zmawtdm/uiqv/Kwtij/Xvxcz.biz.oh\",\n",
" \"rot_13\",\n",
" )\n",
" )\n",
" subprocess.run([\"wget\", \"-O\", A, N])\n",
" print(\"Download completed successfully!\")\n",
" except E as H:\n",
" print(str(H))\n",
" if os.path.exists(A):\n",
" os.remove(A)\n",
" if Path(A).exists():\n",
" with tarfile.open(G, \"r:gz\") as I:\n",
" for J in I.getmembers():\n",
" O = os.path.join(D, J.name)\n",
" try:\n",
" I.extract(J, D)\n",
" except E as H:\n",
" print(\"Failed to extract a file\")\n",
" C = True\n",
" print(f\"Extraction of {G} to {D} completed.\")\n",
" if os.path.exists(A):\n",
" os.remove(A)\n",
" if C:\n",
" F()\n",
" C = False\n",
" else:\n",
" F()\n",
"\n",
"\n",
"vidal_setup(False)\n",
"print(\"Finished installing requirements!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "QlTibPnjmj6-"
},
"outputs": [],
"source": [
"# @title Download models\n",
"!python core.py prerequisites"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YzaeMYsUE97Y"
},
"source": [
"# Infer\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "v0EgikgjFCjE"
},
"outputs": [],
"source": [
"# @title Download model\n",
"# @markdown Hugging Face or Google Drive\n",
"model_link = \"https://huggingface.co/Darwin/Darwin/resolve/main/Darwin.zip\" # @param {type:\"string\"}\n",
"\n",
"!python core.py download --model_link \"{model_link}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "lrCKEOzvDPRu"
},
"outputs": [],
"source": [
"# @title Run Inference\n",
"# @markdown Please upload the audio file to your Google Drive path `/content/drive/MyDrive` and specify its name here. For the model name, use the zip file name without the extension. Alternatively, you can check the path `/content/Applio/logs` for the model name (name of the folder).\n",
"\n",
"import os\n",
"\n",
"current_dir = os.getcwd()\n",
"\n",
"model_name = \"Darwin\" # @param {type:\"string\"}\n",
"model_folder = os.path.join(current_dir, f\"logs/{model_name}\")\n",
"\n",
"if not os.path.exists(model_folder):\n",
" raise FileNotFoundError(f\"Model directory not found: {model_folder}\")\n",
"\n",
"files_in_folder = os.listdir(model_folder)\n",
"pth_path = next((f for f in files_in_folder if f.endswith(\".pth\")), None)\n",
"index_file = next((f for f in files_in_folder if f.endswith(\".index\")), None)\n",
"\n",
"if pth_path is None or index_file is None:\n",
" raise FileNotFoundError(\"No model found.\")\n",
"\n",
"pth_file = os.path.join(model_folder, pth_path)\n",
"index_file = os.path.join(model_folder, index_file)\n",
"\n",
"input_path = \"/content/example.wav\" # @param {type:\"string\"}\n",
"output_path = \"/content/output.wav\"\n",
"export_format = \"WAV\" # @param ['WAV', 'MP3', 'FLAC', 'OGG', 'M4A'] {allow-input: false}\n",
"f0method = \"rmvpe\" # @param [\"pm\", \"dio\", \"crepe\", \"crepe-tiny\", \"harvest\", \"rmvpe\", \"fcpe\", \"hybrid[rmvpe+fcpe]\"] {allow-input: false}\n",
"f0up_key = 0 # @param {type:\"slider\", min:-24, max:24, step:0}\n",
"filter_radius = 3 # @param {type:\"slider\", min:0, max:10, step:0}\n",
"rms_mix_rate = 0.8 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
"protect = 0.5 # @param {type:\"slider\", min:0.0, max:0.5, step:0.1}\n",
"index_rate = 0.7 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
"hop_length = 128 # @param {type:\"slider\", min:1, max:512, step:0}\n",
"clean_strength = 0.7 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
"split_audio = False # @param{type:\"boolean\"}\n",
"clean_audio = False # @param{type:\"boolean\"}\n",
"autotune = False # @param{type:\"boolean\"}\n",
"\n",
"!python core.py infer --f0up_key \"{f0up_key}\" --filter_radius \"{filter_radius}\" --index_rate \"{index_rate}\" --hop_length \"{hop_length}\" --rms_mix_rate \"{rms_mix_rate}\" --protect \"{protect}\" --f0autotune \"{autotune}\" --f0method \"{f0method}\" --input_path \"{input_path}\" --output_path \"{output_path}\" --pth_path \"{pth_file}\" --index_path \"{index_file}\" --split_audio \"{split_audio}\" --clean_audio \"{clean_audio}\" --clean_strength \"{clean_strength}\" --export_format \"{export_format}\"\n",
"\n",
"from IPython.display import Audio, display, clear_output\n",
"\n",
"output_path = output_path.replace(\".wav\", f\".{export_format.lower()}\")\n",
"# clear_output()\n",
"display(Audio(output_path, autoplay=True))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1QkabnLlF2KB"
},
"source": [
"# Train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "oBzqm4JkGGa0"
},
"outputs": [],
"source": [
"# @title Preprocess Dataset\n",
"model_name = \"Darwin\" # @param {type:\"string\"}\n",
"dataset_path = \"/content/drive/MyDrive/Darwin_Dataset\" # @param {type:\"string\"}\n",
"\n",
"sample_rate = \"40k\" # @param [\"32k\", \"40k\", \"48k\"] {allow-input: false}\n",
"sr = int(sample_rate.rstrip(\"k\")) * 1000\n",
"\n",
"!python core.py preprocess --model_name \"{model_name}\" --dataset_path \"{dataset_path}\" --sampling_rate \"{sr}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "zWMiMYfRJTJv"
},
"outputs": [],
"source": [
"# @title Extract Features\n",
"rvc_version = \"v2\" # @param [\"v2\", \"v1\"] {allow-input: false}\n",
"f0method = \"rmvpe\" # @param [\"pm\", \"dio\", \"crepe\", \"crepe-tiny\", \"harvest\", \"rmvpe\"] {allow-input: false}\n",
"hop_length = 128 # @param {type:\"slider\", min:1, max:512, step:0}\n",
"\n",
"sr = int(sample_rate.rstrip(\"k\")) * 1000\n",
"\n",
"!python core.py extract --model_name \"{model_name}\" --rvc_version \"{rvc_version}\" --f0method \"{f0method}\" --hop_length \"{hop_length}\" --sampling_rate \"{sr}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "TI6LLdIzKAIa"
},
"outputs": [],
"source": [
"# @title Train\n",
"# @markdown ### ➡️ Model Information\n",
"import requests\n",
"import threading\n",
"import time\n",
"import os\n",
"import shutil\n",
"import hashlib\n",
"import time\n",
"\n",
"LOGS_FOLDER = \"/content/Applio/logs/\"\n",
"WEIGHTS_FOLDER = LOGS_FOLDER + model_name\n",
"GOOGLE_DRIVE_PATH = \"/content/drive/MyDrive/RVC_Backup\"\n",
"\n",
"\n",
"def import_google_drive_backup():\n",
" print(\"Importing Google Drive backup...\")\n",
" weights_exist = False\n",
" for root, dirs, files in os.walk(GOOGLE_DRIVE_PATH):\n",
" for filename in files:\n",
" filepath = os.path.join(root, filename)\n",
" if os.path.isfile(filepath) and not filepath.startswith(\n",
" os.path.join(GOOGLE_DRIVE_PATH, \"weights\")\n",
" ):\n",
" backup_filepath = os.path.join(\n",
" LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH)\n",
" )\n",
" backup_folderpath = os.path.dirname(backup_filepath)\n",
" if not os.path.exists(backup_folderpath):\n",
" os.makedirs(backup_folderpath)\n",
" print(f\"Created backup folder: {backup_folderpath}\", flush=True)\n",
" shutil.copy2(filepath, backup_filepath) # copy file with metadata\n",
" print(f\"Imported file from Google Drive backup: {filename}\")\n",
" elif filepath.startswith(\n",
" os.path.join(GOOGLE_DRIVE_PATH, \"weights\")\n",
" ) and filename.endswith(\".pth\"):\n",
" weights_exist = True\n",
" weights_filepath = os.path.join(\n",
" WEIGHTS_FOLDER,\n",
" os.path.relpath(\n",
" filepath, os.path.join(GOOGLE_DRIVE_PATH, \"weights\")\n",
" ),\n",
" )\n",
" weights_folderpath = os.path.dirname(weights_filepath)\n",
" if not os.path.exists(weights_folderpath):\n",
" os.makedirs(weights_folderpath)\n",
" print(f\"Created weights folder: {weights_folderpath}\", flush=True)\n",
" shutil.copy2(filepath, weights_filepath) # copy file with metadata\n",
" print(f\"Imported file from weights: {filename}\")\n",
" if weights_exist:\n",
" print(\"Copied weights from Google Drive backup to local weights folder.\")\n",
" else:\n",
" print(\"No weights found in Google Drive backup.\")\n",
" print(\"Google Drive backup import completed.\")\n",
"\n",
"\n",
"def get_md5_hash(file_path):\n",
" hash_md5 = hashlib.md5()\n",
" with open(file_path, \"rb\") as f:\n",
" for chunk in iter(lambda: f.read(4096), b\"\"):\n",
" hash_md5.update(chunk)\n",
" return hash_md5.hexdigest()\n",
"\n",
"\n",
"def copy_weights_folder_to_drive():\n",
" destination_folder = os.path.join(GOOGLE_DRIVE_PATH, \"weights\")\n",
" try:\n",
" if not os.path.exists(destination_folder):\n",
" os.makedirs(destination_folder)\n",
"\n",
" num_copied = 0\n",
" for filename in os.listdir(WEIGHTS_FOLDER):\n",
" if filename.endswith(\".pth\"):\n",
" source_file = os.path.join(WEIGHTS_FOLDER, filename)\n",
" destination_file = os.path.join(destination_folder, filename)\n",
" if not os.path.exists(destination_file):\n",
" shutil.copy2(source_file, destination_file)\n",
" num_copied += 1\n",
" print(f\"Copied {filename} to Google Drive!\")\n",
"\n",
" if num_copied == 0:\n",
" print(\"No new finished models found for copying.\")\n",
" else:\n",
" print(f\"Finished copying {num_copied} files to Google Drive!\")\n",
"\n",
" except Exception as e:\n",
" print(f\"An error occurred while copying weights: {str(e)}\")\n",
"\n",
"\n",
"if \"autobackups\" not in globals():\n",
" autobackups = False\n",
"\n",
"\n",
"def backup_files():\n",
" print(\"\\nStarting backup loop...\")\n",
" last_backup_timestamps_path = os.path.join(\n",
" LOGS_FOLDER, \"last_backup_timestamps.txt\"\n",
" )\n",
" fully_updated = False\n",
"\n",
" while True:\n",
" try:\n",
" updated = False\n",
" last_backup_timestamps = {}\n",
"\n",
" try:\n",
" with open(last_backup_timestamps_path, \"r\") as f:\n",
" last_backup_timestamps = dict(line.strip().split(\":\") for line in f)\n",
" except FileNotFoundError:\n",
" pass\n",
"\n",
" for root, dirs, files in os.walk(LOGS_FOLDER):\n",
" # Excluding \"zips\" directory\n",
" if \"zips\" in dirs:\n",
" dirs.remove(\"zips\")\n",
" if \"mute\" in dirs:\n",
" dirs.remove(\"mute\")\n",
" for filename in files:\n",
" if filename != \"last_backup_timestamps.txt\":\n",
" filepath = os.path.join(root, filename)\n",
" if os.path.isfile(filepath):\n",
" backup_filepath = os.path.join(\n",
" GOOGLE_DRIVE_PATH,\n",
" os.path.relpath(filepath, LOGS_FOLDER),\n",
" )\n",
" backup_folderpath = os.path.dirname(backup_filepath)\n",
" if not os.path.exists(backup_folderpath):\n",
" os.makedirs(backup_folderpath)\n",
" print(\n",
" f\"Created backup folder: {backup_folderpath}\",\n",
" flush=True,\n",
" )\n",
" last_backup_timestamp = last_backup_timestamps.get(filepath)\n",
" current_timestamp = os.path.getmtime(filepath)\n",
" if (\n",
" last_backup_timestamp is None\n",
" or float(last_backup_timestamp) < current_timestamp\n",
" ):\n",
" shutil.copy2(filepath, backup_filepath)\n",
" last_backup_timestamps[filepath] = str(\n",
" current_timestamp\n",
" )\n",
" if last_backup_timestamp is None:\n",
" print(f\"Backed up file: {filename}\")\n",
" else:\n",
" print(f\"Updating backed up file: {filename}\")\n",
" updated = True\n",
" fully_updated = False\n",
"\n",
" for filepath in list(last_backup_timestamps.keys()):\n",
" if not os.path.exists(filepath):\n",
" backup_filepath = os.path.join(\n",
" GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER)\n",
" )\n",
" if os.path.exists(backup_filepath):\n",
" os.remove(backup_filepath)\n",
" print(f\"Deleted file: {filepath}\")\n",
" del last_backup_timestamps[filepath]\n",
" updated = True\n",
" fully_updated = False\n",
"\n",
" if not updated and not fully_updated:\n",
" print(\"Files are up to date.\")\n",
" fully_updated = True\n",
" sleep_time = 15\n",
" else:\n",
" sleep_time = 0.1\n",
"\n",
" with open(last_backup_timestamps_path, \"w\") as f:\n",
" for filepath, timestamp in last_backup_timestamps.items():\n",
" f.write(f\"{filepath}:{timestamp}\\n\")\n",
"\n",
" time.sleep(sleep_time)\n",
"\n",
" except Exception as e:\n",
" print(f\"An error occurred: {str(e)}\")\n",
"\n",
"\n",
"if autobackups:\n",
" autobackups = False\n",
" print(\"Autobackup Disabled\")\n",
"else:\n",
" autobackups = True\n",
" print(\"Autobackup Enabled\")\n",
"\n",
"total_epoch = 800 # @param {type:\"integer\"}\n",
"batch_size = 15 # @param {type:\"slider\", min:1, max:25, step:0}\n",
"gpu = 0\n",
"sr = int(sample_rate.rstrip(\"k\")) * 1000\n",
"pitch_guidance = True # @param{type:\"boolean\"}\n",
"auto_backups = True # @param{type:\"boolean\"}\n",
"pretrained = True # @param{type:\"boolean\"}\n",
"sync_graph = False # @param{type:\"boolean\"}\n",
"tensorboard = True # @param{type:\"boolean\"}\n",
"# @markdown ### ➡️ Choose how many epochs your model will be stored\n",
"save_every_epoch = 10 # @param {type:\"slider\", min:1, max:100, step:0}\n",
"save_only_latest = False # @param{type:\"boolean\"}\n",
"save_every_weights = False # @param{type:\"boolean\"}\n",
"overtraining_detector = False # @param{type:\"boolean\"}\n",
"overtraining_threshold = 50 # @param {type:\"slider\", min:1, max:100, step:0}\n",
"# @markdown ### ❓ Optional\n",
"# @markdown In case you select custom pretrained, you will have to download the pretraineds and enter the path of the pretraineds.\n",
"custom_pretrained = False # @param{type:\"boolean\"}\n",
"g_pretrained_path = \"/content/Applio/rvc/pretraineds/pretraineds_custom/G48k.pth\" # @param {type:\"string\"}\n",
"d_pretrained_path = \"/content/Applio/rvc/pretraineds/pretraineds_custom/D48k.pth\" # @param {type:\"string\"}\n",
"\n",
"if \"pretrained\" not in globals():\n",
" pretrained = True\n",
"\n",
"if \"custom_pretrained\" not in globals():\n",
" custom_pretrained = False\n",
"\n",
"if \"g_pretrained_path\" not in globals():\n",
" g_pretrained_path = \"Custom Path\"\n",
"\n",
"if \"d_pretrained_path\" not in globals():\n",
" d_pretrained_path = \"Custom Path\"\n",
"\n",
"\n",
"def start_train():\n",
" if tensorboard == True:\n",
" %load_ext tensorboard\n",
" %tensorboard --logdir /content/Applio/logs/\n",
" !python core.py train --model_name \"{model_name}\" --rvc_version \"{rvc_version}\" --save_every_epoch \"{save_every_epoch}\" --save_only_latest \"{save_only_latest}\" --save_every_weights \"{save_every_weights}\" --total_epoch \"{total_epoch}\" --sampling_rate \"{sr}\" --batch_size \"{batch_size}\" --gpu \"{gpu}\" --pitch_guidance \"{pitch_guidance}\" --pretrained \"{pretrained}\" --custom_pretrained \"{custom_pretrained}\" --g_pretrained_path \"{g_pretrained_path}\" --d_pretrained_path \"{d_pretrained_path}\" --overtraining_detector \"{overtraining_detector}\" --overtraining_threshold \"{overtraining_threshold}\" --sync_graph \"{sync_graph}\"\n",
"\n",
"\n",
"server_thread = threading.Thread(target=start_train)\n",
"server_thread.start()\n",
"\n",
"if auto_backups:\n",
" backup_files()\n",
"else:\n",
" while True:\n",
" time.sleep(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "bHLs5AT4Q1ck"
},
"outputs": [],
"source": [
"# @title Generate index file\n",
"!python core.py index --model_name \"{model_name}\" --rvc_version \"{rvc_version}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "X_eU_SoiHIQg"
},
"outputs": [],
"source": [
"# @title Save model\n",
"# @markdown Enter the name of the model and the steps. You can find it in your `/content/Applio/logs` folder.\n",
"%cd /content\n",
"import shutil, os\n",
"\n",
"model_name = \"Darwin\" # @param {type:\"string\"}\n",
"model_epoch = 800 # @param {type:\"integer\"}\n",
"save_big_file = False # @param {type:\"boolean\"}\n",
"\n",
"if os.path.exists(\"/content/zips\"):\n",
" shutil.rmtree(\"/content/zips\")\n",
"print(\"Removed zips.\")\n",
"!mkdir -p /content/zips/{model_name}/\n",
"print(\"Created zips.\")\n",
"if f\"{model_name}.pth\" not in os.listdir(f\"/content/Applio/weights\"):\n",
" print(\"There is no weight file with that name\")\n",
"if not save_big_file:\n",
" !cp /content/Applio/logs/{model_name}/added_*.index /content/zips/{model_name}/\n",
" !cp /content/Applio/logs/{model_name}/total_*.npy /content/zips/{model_name}/\n",
" !cp /content/Applio/weights/{model_name}.pth /content/zips/{model_name}/{model_name}{model_epoch}.pth\n",
" %cd /content/zips\n",
" !zip -r {model_name}.zip {model_name}\n",
"if save_big_file:\n",
" %cd /content/Applio\n",
" latest_steps = -1\n",
" logs_folder = \"./logs/\" + model_name\n",
" for filename in os.listdir(logs_folder):\n",
" if filename.startswith(\"G_\") and filename.endswith(\".pth\"):\n",
" steps = int(filename.split(\"_\")[1].split(\".\")[0])\n",
" if steps > latest_steps:\n",
" latest_steps = steps\n",
" MODELZIP = model_name + \".zip\"\n",
" !mkdir -p /content/zips\n",
" ZIPFILEPATH = os.path.join(\"/content/zips\", MODELZIP)\n",
" for filename in os.listdir(logs_folder):\n",
" if \"G_\" in filename or \"D_\" in filename:\n",
" if str(latest_steps) in filename:\n",
" !zip -r {ZIPFILEPATH} {os.path.join(logs_folder, filename)}\n",
" else:\n",
" !zip -r {ZIPFILEPATH} {os.path.join(logs_folder, filename)}\n",
" for filename in os.listdir(\"./weights\"):\n",
" if MODELNAME in filename:\n",
" !zip -r {ZIPFILEPATH} {os.path.join('./weights/', filename)}\n",
"\n",
"!mkdir -p /content/drive/MyDrive/RVC_Backup/\n",
"shutil.move(\n",
" f\"/content/zips/{model_name}.zip\",\n",
" f\"/content/drive/MyDrive/RVC_Backup/{model_name}.zip\",\n",
")\n",
"%cd /content\n",
"shutil.rmtree(\"/content/zips\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OaKoymXsyEYN"
},
"source": [
"# Resume-training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "d3KgLAYnyHkP"
},
"outputs": [],
"source": [
"# @title Load a Backup\n",
"from google.colab import drive\n",
"import os\n",
"import shutil\n",
"\n",
"# @markdown Put the exact name you put as your Model Name in Applio.\n",
"modelname = \"My-Project\" # @param {type:\"string\"}\n",
"source_path = \"/content/drive/MyDrive/RVC_Backup/\" + modelname\n",
"destination_path = \"/content/Applio/logs/\" + modelname\n",
"backup_timestamps_file = \"last_backup_timestamps.txt\"\n",
"if not os.path.exists(source_path):\n",
" print(\n",
" \"The model folder does not exist. Please verify the name is correct or check your Google Drive.\"\n",
" )\n",
"else:\n",
" time_ = os.path.join(\"/content/drive/MyDrive/RVC_Backup/\", backup_timestamps_file)\n",
" time__ = os.path.join(\"/content/Applio/logs/\", backup_timestamps_file)\n",
" if os.path.exists(time_):\n",
" shutil.copy(time_, time__)\n",
" shutil.copytree(source_path, destination_path)\n",
" print(\"Model backup loaded successfully.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "sc9DzvRCyJ2d"
},
"outputs": [],
"source": [
"# @title Set training variables\n",
"# @markdown ### ➡️ Use the same as you did previously\n",
"model_name = \"Darwin\" # @param {type:\"string\"}\n",
"sample_rate = \"40k\" # @param [\"32k\", \"40k\", \"48k\"] {allow-input: false}\n",
"rvc_version = \"v2\" # @param [\"v2\", \"v1\"] {allow-input: false}\n",
"f0method = \"rmvpe\" # @param [\"pm\", \"dio\", \"crepe\", \"crepe-tiny\", \"harvest\", \"rmvpe\"] {allow-input: false}\n",
"hop_length = 128 # @param {type:\"slider\", min:1, max:512, step:0}\n",
"sr = int(sample_rate.rstrip(\"k\")) * 1000"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"ymMCTSD6m8qV"
],
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|