{ "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 }