diff --git a/assets/Applio_NoUI.ipynb b/assets/Applio_NoUI.ipynb
index edd9a9ca..bbc06139 100644
--- a/assets/Applio_NoUI.ipynb
+++ b/assets/Applio_NoUI.ipynb
@@ -1,822 +1,818 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0pKllbPyK_BC"
- },
- "source": [
- "## **Applio NoUI**\n",
- "A simple, high-quality voice conversion tool focused on ease of use and performance. \n",
- "\n",
- "[Support](https://discord.gg/urxFjYmYYh) — [Discord Bot](https://discord.com/oauth2/authorize?client_id=1144714449563955302&permissions=1376674695271&scope=bot%20applications.commands) — [GitHub](https://github.com/IAHispano/Applio)\n",
- "\n",
- "
\n",
- "\n",
- "### **Credits**\n",
- "- Encryption method: [Hina](https://github.com/hinabl)\n",
- "- Extra section: [Poopmaster](https://github.com/poiqazwsx)\n",
- "- Main development: [Applio Team](https://github.com/IAHispano)\n",
- "- Colab inspired on [RVC v2 Disconnected](https://colab.research.google.com/drive/1XIPCP9ken63S7M6b5ui1b36Cs17sP-NS)."
- ]
- },
- {
- "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 --branch 3.2.8-bugfix --single-branch\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 tarfile\n",
- "import subprocess\n",
- "from pathlib import Path\n",
- "from IPython.display import clear_output\n",
- "\n",
- "def vidal_setup(C):\n",
- " def F():\n",
- " print(\"Installing pip packages...\")\n",
- " subprocess.check_call([\"pip\", \"install\", \"-r\", \"requirements.txt\", \"--quiet\"])\n",
- "\n",
- " A = \"/content/\" + rot_47(\"Kikpm.ovm.bu\")\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 = codecs.decode(\n",
- " \"uggcf://uhttvatsnpr.pb/VNUvfcnab/Nccyvb/erfbyir/znva/Raivebzrag/Pbyno/Cache.gne.tm\",\n",
- " \"rot_13\",\n",
- " )\n",
- " subprocess.run([\"wget\", \"-O\", A, N])\n",
- " print(\"Download completed successfully!\")\n",
- " except Exception 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(A, \"r:gz\") as I:\n",
- " I.extractall(D)\n",
- " print(f\"Extraction of {A} 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",
- "!pip uninstall torch torchvision torchaudio -y\n",
- "!pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --upgrade --index-url https://download.pytorch.org/whl/cu121\n",
- "clear_output()\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\" --models \"True\" --exe \"True\" --pretraineds_v1_f0 \"False\" --pretraineds_v2_f0 \"True\" --pretraineds_v1_nof0 \"False\" --pretraineds_v2_nof0 \"False\" "
- ]
- },
- {
- "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",
- "f0_method = \"rmvpe\" # @param [\"crepe\", \"crepe-tiny\", \"rmvpe\", \"fcpe\", \"hybrid[rmvpe+fcpe]\"] {allow-input: false}\n",
- "f0_up_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",
- "f0_autotune = False # @param{type:\"boolean\"}\n",
- "formant_shift = False # @param{type:\"boolean\"}\n",
- "formant_qfrency = 1.0 # @param {type:\"slider\", min:1.0, max:16.0, step:0.1}\n",
- "formant_timbre = 1.0 # @param {type:\"slider\", min:1.0, max:16.0, step:0.1}\n",
- "embedder_model = \"contentvec\" # @param [\"contentvec\", \"chinese-hubert-base\", \"japanese-hubert-base\", \"korean-hubert-base\", \"custom\"] {allow-input: false}\n",
- "embedder_model_custom = \"\" # @param {type:\"string\"}\n",
- "\n",
- "\n",
- "# Post-processing effects\n",
- "if \"post_process\" not in globals():\n",
- " post_process = False \n",
- "if \"reverb\" not in globals():\n",
- " reverb = False \n",
- "if \"pitch_shift\" not in globals():\n",
- " pitch_shift = False \n",
- "if \"limiter\" not in globals():\n",
- " limiter = False \n",
- "if \"gain\" not in globals():\n",
- " gain = False \n",
- "if \"distortion\" not in globals():\n",
- " distortion = False \n",
- "if \"chorus\" not in globals():\n",
- " chorus = False \n",
- "if \"bitcrush\" not in globals():\n",
- " bitcrush = False\n",
- "if \"clipping\" not in globals():\n",
- " clipping = False \n",
- "if \"compressor\" not in globals():\n",
- " compressor = False \n",
- "if \"delay\" not in globals():\n",
- " delay = False\n",
- "\n",
- "if \"reverb_room_size\" not in globals():\n",
- " reverb_room_size = 0.5 \n",
- "if \"reverb_damping\" not in globals():\n",
- " reverb_damping = 0.5 \n",
- "if \"reverb_wet_gain\" not in globals():\n",
- " reverb_wet_gain = 0.0 \n",
- "if \"reverb_dry_gain\" not in globals():\n",
- " reverb_dry_gain = 0.0 \n",
- "if \"reverb_width\" not in globals():\n",
- " reverb_width = 1.0 \n",
- "if \"reverb_freeze_mode\" not in globals():\n",
- " reverb_freeze_mode = 0.0 \n",
- "\n",
- "if \"pitch_shift_semitones\" not in globals():\n",
- " pitch_shift_semitones = 0.0 \n",
- "\n",
- "if \"limiter_threshold\" not in globals():\n",
- " limiter_threshold = -1.0 \n",
- "if \"limiter_release_time\" not in globals():\n",
- " limiter_release_time = 0.05 \n",
- "\n",
- "if \"gain_db\" not in globals():\n",
- " gain_db = 0.0 \n",
- "\n",
- "if \"distortion_gain\" not in globals():\n",
- " distortion_gain = 0.0 \n",
- "\n",
- "if \"chorus_rate\" not in globals():\n",
- " chorus_rate = 1.5 \n",
- "if \"chorus_depth\" not in globals():\n",
- " chorus_depth = 0.1 \n",
- "if \"chorus_center_delay\" not in globals():\n",
- " chorus_center_delay = 15.0 \n",
- "if \"chorus_feedback\" not in globals():\n",
- " chorus_feedback = 0.25 \n",
- "if \"chorus_mix\" not in globals():\n",
- " chorus_mix = 0.5 \n",
- "\n",
- "if \"bitcrush_bit_depth\" not in globals():\n",
- " bitcrush_bit_depth = 4 \n",
- "\n",
- "if \"clipping_threshold\" not in globals():\n",
- " clipping_threshold = 0.5 \n",
- "\n",
- "if \"compressor_threshold\" not in globals():\n",
- " compressor_threshold = -20.0\n",
- "if \"compressor_ratio\" not in globals():\n",
- " compressor_ratio = 4.0 \n",
- "if \"compressor_attack\" not in globals():\n",
- " compressor_attack = 0.001 \n",
- "if \"compressor_release\" not in globals():\n",
- " compressor_release = 0.1 \n",
- "\n",
- "if \"delay_seconds\" not in globals():\n",
- " delay_seconds = 0.1\n",
- "if \"delay_feedback\" not in globals():\n",
- " delay_feedback = 0.5 \n",
- "if \"delay_mix\" not in globals():\n",
- " delay_mix = 0.5 \n",
- " \n",
- "!python core.py infer --pitch \"{f0_up_key}\" --filter_radius \"{filter_radius}\" --volume_envelope \"{rms_mix_rate}\" --index_rate \"{index_rate}\" --hop_length \"{hop_length}\" --protect \"{protect}\" --f0_autotune \"{f0_autotune}\" --f0_method \"{f0_method}\" --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}\" --embedder_model \"{embedder_model}\" --embedder_model_custom \"{embedder_model_custom}\" --formant_shifting \"{formant_shift}\" --formant_qfrency \"{formant_qfrency}\" --formant_timbre \"{formant_timbre}\" --post_process \"{post_process}\" --reverb \"{reverb}\" --pitch_shift \"{pitch_shift}\" --limiter \"{limiter}\" --gain \"{gain}\" --distortion \"{distortion}\" --chorus \"{chorus}\" --bitcrush \"{bitcrush}\" --clipping \"{clipping}\" --compressor \"{compressor}\" --delay \"{delay}\" --reverb_room_size \"{reverb_room_size}\" --reverb_damping \"{reverb_damping}\" --reverb_wet_gain \"{reverb_wet_gain}\" --reverb_dry_gain \"{reverb_dry_gain}\" --reverb_width \"{reverb_width}\" --reverb_freeze_mode \"{reverb_freeze_mode}\" --pitch_shift_semitones \"{pitch_shift_semitones}\" --limiter_threshold \"{limiter_threshold}\" --limiter_release_time \"{limiter_release_time}\" --gain_db \"{gain_db}\" --distortion_gain \"{distortion_gain}\" --chorus_rate \"{chorus_rate}\" --chorus_depth \"{chorus_depth}\" --chorus_center_delay \"{chorus_center_delay}\" --chorus_feedback \"{chorus_feedback}\" --chorus_mix \"{chorus_mix}\" --bitcrush_bit_depth \"{bitcrush_bit_depth}\" --clipping_threshold \"{clipping_threshold}\" --compressor_threshold \"{compressor_threshold}\" --compressor_ratio \"{compressor_ratio}\" --compressor_attack \"{compressor_attack}\" --compressor_release \"{compressor_release}\" --delay_seconds \"{delay_seconds}\" --delay_feedback \"{delay_feedback}\" --delay_mix \"{delay_mix}\"\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": "yrWw2h9d2TRn"
- },
- "source": [
- "## **Advanced Settings**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "cellView": "form",
- "id": "J43qejJ-2Tpp"
- },
- "outputs": [],
- "source": [
- "# @title # Post-processing effects\n",
- "post_process = False # @param{type:\"boolean\"}\n",
- "reverb = False # @param{type:\"boolean\"}\n",
- "pitch_shift = False # @param{type:\"boolean\"}\n",
- "limiter = False # @param{type:\"boolean\"}\n",
- "gain = False # @param{type:\"boolean\"}\n",
- "distortion = False # @param{type:\"boolean\"}\n",
- "chorus = False # @param{type:\"boolean\"}\n",
- "bitcrush = False # @param{type:\"boolean\"}\n",
- "clipping = False # @param{type:\"boolean\"}\n",
- "compressor = False # @param{type:\"boolean\"}\n",
- "delay = False # @param{type:\"boolean\"}\n",
- "\n",
- "reverb_room_size = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "reverb_damping = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "reverb_wet_gain = 0.0 # @param {type:\"slider\", min:-20.0, max:20.0, step:0.1}\n",
- "reverb_dry_gain = 0.0 # @param {type:\"slider\", min:-20.0, max:20.0, step:0.1}\n",
- "reverb_width = 1.0 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "reverb_freeze_mode = 0.0 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "\n",
- "pitch_shift_semitones = 0.0 # @param {type:\"slider\", min:-12.0, max:12.0, step:0.1}\n",
- "\n",
- "limiter_threshold = -1.0 # @param {type:\"slider\", min:-20.0, max:0.0, step:0.1}\n",
- "limiter_release_time = 0.05 # @param {type:\"slider\", min:0.0, max:1.0, step:0.01}\n",
- "\n",
- "gain_db = 0.0 # @param {type:\"slider\", min:-20.0, max:20.0, step:0.1}\n",
- "\n",
- "distortion_gain = 0.0 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "\n",
- "chorus_rate = 1.5 # @param {type:\"slider\", min:0.1, max:10.0, step:0.1}\n",
- "chorus_depth = 0.1 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "chorus_center_delay = 15.0 # @param {type:\"slider\", min:0.0, max:50.0, step:0.1}\n",
- "chorus_feedback = 0.25 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "chorus_mix = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "\n",
- "bitcrush_bit_depth = 4 # @param {type:\"slider\", min:1, max:16, step:1}\n",
- "\n",
- "clipping_threshold = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "\n",
- "compressor_threshold = -20.0 # @param {type:\"slider\", min:-60.0, max:0.0, step:0.1}\n",
- "compressor_ratio = 4.0 # @param {type:\"slider\", min:1.0, max:20.0, step:0.1}\n",
- "compressor_attack = 0.001 # @param {type:\"slider\", min:0.0, max:0.1, step:0.001}\n",
- "compressor_release = 0.1 # @param {type:\"slider\", min:0.0, max:1.0, step:0.01}\n",
- "\n",
- "delay_seconds = 0.1 # @param {type:\"slider\", min:0.0, max:1.0, step:0.01}\n",
- "delay_feedback = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "delay_mix = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n"
- ]
- },
- {
- "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",
- "import os\n",
- "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\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",
- "cpu_cores = 2 # @param {type:\"slider\", min:1, max:2, step:1}\n",
- "cut_preprocess = \"Automatic\" # @param [\"Skip\", \"Simple\", \"Automatic\"] {allow-input: false}\n",
- "process_effects = False # @param{type:\"boolean\"}\n",
- "noise_reduction = False # @param{type:\"boolean\"}\n",
- "noise_reduction_strength = 0.7 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
- "chunk_len = 3.0 # @param {type:\"slider\", min:0.5, max:5.0, step:0.5}\n",
- "overlap_len = 0.3 # @param {type:\"slider\", min:0.0, max:0.4, step:0.1}\n",
- "\n",
- "!python core.py preprocess --model_name \"{model_name}\" --dataset_path \"{dataset_path}\" --sample_rate \"{sr}\" --cpu_cores \"{cpu_cores}\" --cut_preprocess \"{cut_preprocess}\" --process_effects \"{process_effects}\" --noise_reduction \"{noise_reduction}\" --noise_reduction_strength \"{noise_reduction_strength}\" --chunk_len \"{chunk_len}\" --overlap_len \"{overlap_len}\""
- ]
- },
- {
- "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",
- "f0_method = \"rmvpe\" # @param [\"crepe\", \"crepe-tiny\", \"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",
- "cpu_cores = 2 # @param {type:\"slider\", min:1, max:2, step:1}\n",
- "include_mutes = 2 # @param {type:\"slider\", min:0, max:10, step:1}\n",
- "embedder_model = \"contentvec\" # @param [\"contentvec\", \"chinese-hubert-base\", \"japanese-hubert-base\", \"korean-hubert-base\", \"custom\"] {allow-input: false}\n",
- "embedder_model_custom = \"\" # @param {type:\"string\"}\n",
- "\n",
- "!python core.py extract --model_name \"{model_name}\" --rvc_version \"{rvc_version}\" --f0_method \"{f0_method}\" --hop_length \"{hop_length}\" --sample_rate \"{sr}\" --cpu_cores \"{cpu_cores}\" --gpu \"0\" --embedder_model \"{embedder_model}\" --embedder_model_custom \"{embedder_model_custom}\" --include_mutes \"{include_mutes}\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "id": "TI6LLdIzKAIa"
- },
- "outputs": [],
- "source": [
- "# @title Train\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",
- "GOOGLE_DRIVE_PATH = \"/content/drive/MyDrive/RVC_Backup\"\n",
- "\n",
- "\n",
- "def import_google_drive_backup():\n",
- " print(\"Importing Google Drive backup...\")\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):\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)\n",
- " print(f\"Imported file from Google Drive backup: {filename}\")\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",
- "if \"autobackups\" not in globals():\n",
- " autobackups = False\n",
- "# @markdown ### 💾 AutoBackup\n",
- "cooldown = 15 # @param {type:\"slider\", min:0, max:100, step:0}\n",
- "auto_backups = True # @param{type:\"boolean\"}\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_files = 0\n",
- " deleted_files = 0\n",
- " new_files = 0\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\" and \"mute\" directories\n",
- " if \"zips\" in dirs:\n",
- " dirs.remove(\"zips\")\n",
- " if \"mute\" in dirs:\n",
- " dirs.remove(\"mute\")\n",
- "\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",
- " 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(current_timestamp)\n",
- " if last_backup_timestamp is None:\n",
- " new_files += 1\n",
- " else:\n",
- " updated_files += 1\n",
- "\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",
- " deleted_files += 1\n",
- " del last_backup_timestamps[filepath]\n",
- "\n",
- "\n",
- " if updated_files > 0 or deleted_files > 0 or new_files > 0:\n",
- " print(f\"Backup Complete: {new_files} new, {updated_files} updated, {deleted_files} deleted.\")\n",
- " fully_updated = False\n",
- " elif not fully_updated:\n",
- " print(\"Files are up to date.\")\n",
- " fully_updated = True\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(cooldown if fully_updated else 0.1)\n",
- "\n",
- "\n",
- " except Exception as error:\n",
- " print(f\"An error occurred during backup: {error}\")\n",
- "\n",
- "\n",
- "if autobackups:\n",
- " autobackups = False\n",
- " print(\"Autobackup Disabled\")\n",
- "else:\n",
- " autobackups = True\n",
- " print(\"Autobackup Enabled\")\n",
- "# @markdown ### ⚙️ Train Settings\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",
- "pretrained = True # @param{type:\"boolean\"}\n",
- "cleanup = False # @param{type:\"boolean\"}\n",
- "cache_data_in_gpu = 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/models/pretraineds/pretraineds_custom/G48k.pth\" # @param {type:\"string\"}\n",
- "d_pretrained_path = \"/content/Applio/rvc/models/pretraineds/pretraineds_custom/D48k.pth\" # @param {type:\"string\"}\n",
- "vocoder = \"HiFi-GAN\" # @param [\"HiFi-GAN\", \"MRF HiFi-GAN\", \"RefineGAN\"] {allow-input: false}\n",
- "checkpointing = False # @param{type:\"boolean\"}\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}\" --sample_rate \"{sr}\" --batch_size \"{batch_size}\" --gpu \"{gpu}\" --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}\" --cleanup \"{cleanup}\" --cache_data_in_gpu \"{cache_data_in_gpu}\" --vocoder \"{vocoder}\" --checkpointing \"{checkpointing}\"\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",
- "index_algorithm = \"Auto\" # @param [\"Auto\", \"Faiss\", \"KMeans\"] {allow-input: false}\n",
- "!python core.py index --model_name \"{model_name}\" --rvc_version \"{rvc_version}\" --index_algorithm \"{index_algorithm}\""
- ]
- },
- {
- "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 os, shutil, sys\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",
- "\n",
- "os.makedirs(f\"/content/zips/{model_name}/\", exist_ok=True)\n",
- "print(\"Created zips.\")\n",
- "\n",
- "logs_folder = f\"/content/Applio/logs/{model_name}/\"\n",
- "weight_file = None\n",
- "if not os.path.exists(logs_folder):\n",
- " print(f\"Model folder not found.\")\n",
- " sys.exit(\"\")\n",
- "\n",
- "for filename in os.listdir(logs_folder):\n",
- " if filename.startswith(f\"{model_name}_{model_epoch}e\") and filename.endswith(\".pth\"):\n",
- " weight_file = filename\n",
- " break\n",
- "if weight_file is None:\n",
- " print(\"There is no weight file with that name\")\n",
- " sys.exit(\"\")\n",
- "if not save_big_file:\n",
- " !cp {logs_folder}added_*.index /content/zips/{model_name}/\n",
- " !cp {logs_folder}total_*.npy /content/zips/{model_name}/\n",
- " !cp {logs_folder}{weight_file} /content/zips/{model_name}/\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",
- "\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/Applio\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",
- "f0_method = \"rmvpe\" # @param [\"crepe\", \"crepe-tiny\", \"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
-}
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0pKllbPyK_BC"
+ },
+ "source": [
+ "## **Applio NoUI**\n",
+ "A simple, high-quality voice conversion tool focused on ease of use and performance. \n",
+ "\n",
+ "[Support](https://discord.gg/urxFjYmYYh) — [Discord Bot](https://discord.com/oauth2/authorize?client_id=1144714449563955302&permissions=1376674695271&scope=bot%20applications.commands) — [GitHub](https://github.com/IAHispano/Applio)\n",
+ "\n",
+ "
\n",
+ "\n",
+ "### **Credits**\n",
+ "- Encryption method: [Hina](https://github.com/hinabl)\n",
+ "- Extra section: [Poopmaster](https://github.com/poiqazwsx)\n",
+ "- Main development: [Applio Team](https://github.com/IAHispano)\n",
+ "- Colab inspired on [RVC v2 Disconnected](https://colab.research.google.com/drive/1XIPCP9ken63S7M6b5ui1b36Cs17sP-NS)."
+ ]
+ },
+ {
+ "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 --branch 3.2.8-bugfix --single-branch\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 tarfile\n",
+ "import subprocess\n",
+ "from pathlib import Path\n",
+ "from IPython.display import clear_output\n",
+ "\n",
+ "def vidal_setup(C):\n",
+ " def F():\n",
+ " print(\"Installing pip packages...\")\n",
+ " subprocess.check_call([\"pip\", \"install\", \"-r\", \"requirements.txt\", \"--quiet\"])\n",
+ "\n",
+ " A = \"/content/\" + rot_47(\"Kikpm.ovm.bu\")\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 = codecs.decode(\n",
+ " \"uggcf://uhttvatsnpr.pb/VNUvfcnab/Nccyvb/erfbyir/znva/Raivebzrag/Pbyno/Cache.gne.tm\",\n",
+ " \"rot_13\",\n",
+ " )\n",
+ " subprocess.run([\"wget\", \"-O\", A, N])\n",
+ " print(\"Download completed successfully!\")\n",
+ " except Exception 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(A, \"r:gz\") as I:\n",
+ " I.extractall(D)\n",
+ " print(f\"Extraction of {A} 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",
+ "!pip uninstall torch torchvision torchaudio -y\n",
+ "!pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --upgrade --index-url https://download.pytorch.org/whl/cu121\n",
+ "clear_output()\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\" --models \"True\" --exe \"True\" --pretraineds_v1_f0 \"False\" --pretraineds_v2_f0 \"True\" --pretraineds_v1_nof0 \"False\" --pretraineds_v2_nof0 \"False\" "
+ ]
+ },
+ {
+ "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",
+ "f0_method = \"rmvpe\" # @param [\"crepe\", \"crepe-tiny\", \"rmvpe\", \"fcpe\", \"hybrid[rmvpe+fcpe]\"] {allow-input: false}\n",
+ "f0_up_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",
+ "f0_autotune = False # @param{type:\"boolean\"}\n",
+ "formant_shift = False # @param{type:\"boolean\"}\n",
+ "formant_qfrency = 1.0 # @param {type:\"slider\", min:1.0, max:16.0, step:0.1}\n",
+ "formant_timbre = 1.0 # @param {type:\"slider\", min:1.0, max:16.0, step:0.1}\n",
+ "embedder_model = \"contentvec\" # @param [\"contentvec\", \"chinese-hubert-base\", \"japanese-hubert-base\", \"korean-hubert-base\", \"custom\"] {allow-input: false}\n",
+ "embedder_model_custom = \"\" # @param {type:\"string\"}\n",
+ "\n",
+ "\n",
+ "# Post-processing effects\n",
+ "if \"post_process\" not in globals():\n",
+ " post_process = False \n",
+ "if \"reverb\" not in globals():\n",
+ " reverb = False \n",
+ "if \"pitch_shift\" not in globals():\n",
+ " pitch_shift = False \n",
+ "if \"limiter\" not in globals():\n",
+ " limiter = False \n",
+ "if \"gain\" not in globals():\n",
+ " gain = False \n",
+ "if \"distortion\" not in globals():\n",
+ " distortion = False \n",
+ "if \"chorus\" not in globals():\n",
+ " chorus = False \n",
+ "if \"bitcrush\" not in globals():\n",
+ " bitcrush = False\n",
+ "if \"clipping\" not in globals():\n",
+ " clipping = False \n",
+ "if \"compressor\" not in globals():\n",
+ " compressor = False \n",
+ "if \"delay\" not in globals():\n",
+ " delay = False\n",
+ "\n",
+ "if \"reverb_room_size\" not in globals():\n",
+ " reverb_room_size = 0.5 \n",
+ "if \"reverb_damping\" not in globals():\n",
+ " reverb_damping = 0.5 \n",
+ "if \"reverb_wet_gain\" not in globals():\n",
+ " reverb_wet_gain = 0.0 \n",
+ "if \"reverb_dry_gain\" not in globals():\n",
+ " reverb_dry_gain = 0.0 \n",
+ "if \"reverb_width\" not in globals():\n",
+ " reverb_width = 1.0 \n",
+ "if \"reverb_freeze_mode\" not in globals():\n",
+ " reverb_freeze_mode = 0.0 \n",
+ "\n",
+ "if \"pitch_shift_semitones\" not in globals():\n",
+ " pitch_shift_semitones = 0.0 \n",
+ "\n",
+ "if \"limiter_threshold\" not in globals():\n",
+ " limiter_threshold = -1.0 \n",
+ "if \"limiter_release_time\" not in globals():\n",
+ " limiter_release_time = 0.05 \n",
+ "\n",
+ "if \"gain_db\" not in globals():\n",
+ " gain_db = 0.0 \n",
+ "\n",
+ "if \"distortion_gain\" not in globals():\n",
+ " distortion_gain = 0.0 \n",
+ "\n",
+ "if \"chorus_rate\" not in globals():\n",
+ " chorus_rate = 1.5 \n",
+ "if \"chorus_depth\" not in globals():\n",
+ " chorus_depth = 0.1 \n",
+ "if \"chorus_center_delay\" not in globals():\n",
+ " chorus_center_delay = 15.0 \n",
+ "if \"chorus_feedback\" not in globals():\n",
+ " chorus_feedback = 0.25 \n",
+ "if \"chorus_mix\" not in globals():\n",
+ " chorus_mix = 0.5 \n",
+ "\n",
+ "if \"bitcrush_bit_depth\" not in globals():\n",
+ " bitcrush_bit_depth = 4 \n",
+ "\n",
+ "if \"clipping_threshold\" not in globals():\n",
+ " clipping_threshold = 0.5 \n",
+ "\n",
+ "if \"compressor_threshold\" not in globals():\n",
+ " compressor_threshold = -20.0\n",
+ "if \"compressor_ratio\" not in globals():\n",
+ " compressor_ratio = 4.0 \n",
+ "if \"compressor_attack\" not in globals():\n",
+ " compressor_attack = 0.001 \n",
+ "if \"compressor_release\" not in globals():\n",
+ " compressor_release = 0.1 \n",
+ "\n",
+ "if \"delay_seconds\" not in globals():\n",
+ " delay_seconds = 0.1\n",
+ "if \"delay_feedback\" not in globals():\n",
+ " delay_feedback = 0.5 \n",
+ "if \"delay_mix\" not in globals():\n",
+ " delay_mix = 0.5 \n",
+ " \n",
+ "!python core.py infer --pitch \"{f0_up_key}\" --filter_radius \"{filter_radius}\" --volume_envelope \"{rms_mix_rate}\" --index_rate \"{index_rate}\" --hop_length \"{hop_length}\" --protect \"{protect}\" --f0_autotune \"{f0_autotune}\" --f0_method \"{f0_method}\" --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}\" --embedder_model \"{embedder_model}\" --embedder_model_custom \"{embedder_model_custom}\" --formant_shifting \"{formant_shift}\" --formant_qfrency \"{formant_qfrency}\" --formant_timbre \"{formant_timbre}\" --post_process \"{post_process}\" --reverb \"{reverb}\" --pitch_shift \"{pitch_shift}\" --limiter \"{limiter}\" --gain \"{gain}\" --distortion \"{distortion}\" --chorus \"{chorus}\" --bitcrush \"{bitcrush}\" --clipping \"{clipping}\" --compressor \"{compressor}\" --delay \"{delay}\" --reverb_room_size \"{reverb_room_size}\" --reverb_damping \"{reverb_damping}\" --reverb_wet_gain \"{reverb_wet_gain}\" --reverb_dry_gain \"{reverb_dry_gain}\" --reverb_width \"{reverb_width}\" --reverb_freeze_mode \"{reverb_freeze_mode}\" --pitch_shift_semitones \"{pitch_shift_semitones}\" --limiter_threshold \"{limiter_threshold}\" --limiter_release_time \"{limiter_release_time}\" --gain_db \"{gain_db}\" --distortion_gain \"{distortion_gain}\" --chorus_rate \"{chorus_rate}\" --chorus_depth \"{chorus_depth}\" --chorus_center_delay \"{chorus_center_delay}\" --chorus_feedback \"{chorus_feedback}\" --chorus_mix \"{chorus_mix}\" --bitcrush_bit_depth \"{bitcrush_bit_depth}\" --clipping_threshold \"{clipping_threshold}\" --compressor_threshold \"{compressor_threshold}\" --compressor_ratio \"{compressor_ratio}\" --compressor_attack \"{compressor_attack}\" --compressor_release \"{compressor_release}\" --delay_seconds \"{delay_seconds}\" --delay_feedback \"{delay_feedback}\" --delay_mix \"{delay_mix}\"\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": "yrWw2h9d2TRn"
+ },
+ "source": [
+ "## **Advanced Settings**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "cellView": "form",
+ "id": "J43qejJ-2Tpp"
+ },
+ "outputs": [],
+ "source": [
+ "# @title # Post-processing effects\n",
+ "post_process = False # @param{type:\"boolean\"}\n",
+ "reverb = False # @param{type:\"boolean\"}\n",
+ "pitch_shift = False # @param{type:\"boolean\"}\n",
+ "limiter = False # @param{type:\"boolean\"}\n",
+ "gain = False # @param{type:\"boolean\"}\n",
+ "distortion = False # @param{type:\"boolean\"}\n",
+ "chorus = False # @param{type:\"boolean\"}\n",
+ "bitcrush = False # @param{type:\"boolean\"}\n",
+ "clipping = False # @param{type:\"boolean\"}\n",
+ "compressor = False # @param{type:\"boolean\"}\n",
+ "delay = False # @param{type:\"boolean\"}\n",
+ "\n",
+ "reverb_room_size = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "reverb_damping = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "reverb_wet_gain = 0.0 # @param {type:\"slider\", min:-20.0, max:20.0, step:0.1}\n",
+ "reverb_dry_gain = 0.0 # @param {type:\"slider\", min:-20.0, max:20.0, step:0.1}\n",
+ "reverb_width = 1.0 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "reverb_freeze_mode = 0.0 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "\n",
+ "pitch_shift_semitones = 0.0 # @param {type:\"slider\", min:-12.0, max:12.0, step:0.1}\n",
+ "\n",
+ "limiter_threshold = -1.0 # @param {type:\"slider\", min:-20.0, max:0.0, step:0.1}\n",
+ "limiter_release_time = 0.05 # @param {type:\"slider\", min:0.0, max:1.0, step:0.01}\n",
+ "\n",
+ "gain_db = 0.0 # @param {type:\"slider\", min:-20.0, max:20.0, step:0.1}\n",
+ "\n",
+ "distortion_gain = 0.0 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "\n",
+ "chorus_rate = 1.5 # @param {type:\"slider\", min:0.1, max:10.0, step:0.1}\n",
+ "chorus_depth = 0.1 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "chorus_center_delay = 15.0 # @param {type:\"slider\", min:0.0, max:50.0, step:0.1}\n",
+ "chorus_feedback = 0.25 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "chorus_mix = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "\n",
+ "bitcrush_bit_depth = 4 # @param {type:\"slider\", min:1, max:16, step:1}\n",
+ "\n",
+ "clipping_threshold = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "\n",
+ "compressor_threshold = -20.0 # @param {type:\"slider\", min:-60.0, max:0.0, step:0.1}\n",
+ "compressor_ratio = 4.0 # @param {type:\"slider\", min:1.0, max:20.0, step:0.1}\n",
+ "compressor_attack = 0.001 # @param {type:\"slider\", min:0.0, max:0.1, step:0.001}\n",
+ "compressor_release = 0.1 # @param {type:\"slider\", min:0.0, max:1.0, step:0.01}\n",
+ "\n",
+ "delay_seconds = 0.1 # @param {type:\"slider\", min:0.0, max:1.0, step:0.01}\n",
+ "delay_feedback = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "delay_mix = 0.5 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n"
+ ]
+ },
+ {
+ "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",
+ "import os\n",
+ "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\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",
+ "cpu_cores = 2 # @param {type:\"slider\", min:1, max:2, step:1}\n",
+ "cut_preprocess = True # @param{type:\"boolean\"}\n",
+ "process_effects = False # @param{type:\"boolean\"}\n",
+ "noise_reduction = False # @param{type:\"boolean\"}\n",
+ "noise_reduction_strength = 0.7 # @param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
+ "\n",
+ "!python core.py preprocess --model_name \"{model_name}\" --dataset_path \"{dataset_path}\" --sample_rate \"{sr}\" --cpu_cores \"{cpu_cores}\" --cut_preprocess \"{cut_preprocess}\" --process_effects \"{process_effects}\" --noise_reduction \"{noise_reduction}\" --noise_reduction_strength \"{noise_reduction_strength}\""
+ ]
+ },
+ {
+ "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",
+ "f0_method = \"rmvpe\" # @param [\"crepe\", \"crepe-tiny\", \"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",
+ "cpu_cores = 2 # @param {type:\"slider\", min:1, max:2, step:1}\n",
+ "embedder_model = \"contentvec\" # @param [\"contentvec\", \"chinese-hubert-base\", \"japanese-hubert-base\", \"korean-hubert-base\", \"custom\"] {allow-input: false}\n",
+ "embedder_model_custom = \"\" # @param {type:\"string\"}\n",
+ "\n",
+ "!python core.py extract --model_name \"{model_name}\" --rvc_version \"{rvc_version}\" --f0_method \"{f0_method}\" --hop_length \"{hop_length}\" --sample_rate \"{sr}\" --cpu_cores \"{cpu_cores}\" --gpu \"0\" --embedder_model \"{embedder_model}\" --embedder_model_custom \"{embedder_model_custom}\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "TI6LLdIzKAIa"
+ },
+ "outputs": [],
+ "source": [
+ "# @title Train\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",
+ "GOOGLE_DRIVE_PATH = \"/content/drive/MyDrive/RVC_Backup\"\n",
+ "\n",
+ "\n",
+ "def import_google_drive_backup():\n",
+ " print(\"Importing Google Drive backup...\")\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):\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)\n",
+ " print(f\"Imported file from Google Drive backup: {filename}\")\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",
+ "if \"autobackups\" not in globals():\n",
+ " autobackups = False\n",
+ "# @markdown ### 💾 AutoBackup\n",
+ "cooldown = 15 # @param {type:\"slider\", min:0, max:100, step:0}\n",
+ "auto_backups = True # @param{type:\"boolean\"}\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_files = 0\n",
+ " deleted_files = 0\n",
+ " new_files = 0\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\" and \"mute\" directories\n",
+ " if \"zips\" in dirs:\n",
+ " dirs.remove(\"zips\")\n",
+ " if \"mute\" in dirs:\n",
+ " dirs.remove(\"mute\")\n",
+ "\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",
+ " 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(current_timestamp)\n",
+ " if last_backup_timestamp is None:\n",
+ " new_files += 1\n",
+ " else:\n",
+ " updated_files += 1\n",
+ "\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",
+ " deleted_files += 1\n",
+ " del last_backup_timestamps[filepath]\n",
+ "\n",
+ "\n",
+ " if updated_files > 0 or deleted_files > 0 or new_files > 0:\n",
+ " print(f\"Backup Complete: {new_files} new, {updated_files} updated, {deleted_files} deleted.\")\n",
+ " fully_updated = False\n",
+ " elif not fully_updated:\n",
+ " print(\"Files are up to date.\")\n",
+ " fully_updated = True\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(cooldown if fully_updated else 0.1)\n",
+ "\n",
+ "\n",
+ " except Exception as error:\n",
+ " print(f\"An error occurred during backup: {error}\")\n",
+ "\n",
+ "\n",
+ "if autobackups:\n",
+ " autobackups = False\n",
+ " print(\"Autobackup Disabled\")\n",
+ "else:\n",
+ " autobackups = True\n",
+ " print(\"Autobackup Enabled\") \n",
+ "# @markdown ### ⚙️ Train Settings\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",
+ "pretrained = True # @param{type:\"boolean\"}\n",
+ "cleanup = False # @param{type:\"boolean\"}\n",
+ "cache_data_in_gpu = 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/models/pretraineds/pretraineds_custom/G48k.pth\" # @param {type:\"string\"}\n",
+ "d_pretrained_path = \"/content/Applio/rvc/models/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}\" --sample_rate \"{sr}\" --batch_size \"{batch_size}\" --gpu \"{gpu}\" --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}\" --cleanup \"{cleanup}\" --cache_data_in_gpu \"{cache_data_in_gpu}\"\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",
+ "index_algorithm = \"Auto\" # @param [\"Auto\", \"Faiss\", \"KMeans\"] {allow-input: false}\n",
+ "!python core.py index --model_name \"{model_name}\" --rvc_version \"{rvc_version}\" --index_algorithm \"{index_algorithm}\""
+ ]
+ },
+ {
+ "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 os, shutil, sys\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",
+ "\n",
+ "os.makedirs(f\"/content/zips/{model_name}/\", exist_ok=True)\n",
+ "print(\"Created zips.\")\n",
+ "\n",
+ "logs_folder = f\"/content/Applio/logs/{model_name}/\"\n",
+ "weight_file = None\n",
+ "if not os.path.exists(logs_folder):\n",
+ " print(f\"Model folder not found.\")\n",
+ " sys.exit(\"\")\n",
+ "\n",
+ "for filename in os.listdir(logs_folder):\n",
+ " if filename.startswith(f\"{model_name}_{model_epoch}e\") and filename.endswith(\".pth\"):\n",
+ " weight_file = filename\n",
+ " break\n",
+ "if weight_file is None:\n",
+ " print(\"There is no weight file with that name\")\n",
+ " sys.exit(\"\")\n",
+ "if not save_big_file:\n",
+ " !cp {logs_folder}added_*.index /content/zips/{model_name}/\n",
+ " !cp {logs_folder}total_*.npy /content/zips/{model_name}/\n",
+ " !cp {logs_folder}{weight_file} /content/zips/{model_name}/\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",
+ "\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/Applio\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",
+ "f0_method = \"rmvpe\" # @param [\"crepe\", \"crepe-tiny\", \"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
+ }
\ No newline at end of file