{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "Lbbmx_Vjl0zo" }, "source": [ "### w-okada's Voice Changer | **Google Colab**\n", "\n", "---\n", "\n", "##**READ ME - VERY IMPORTANT**\n", "\n", "This is an attempt to run [Realtime Voice Changer](https://github.com/w-okada/voice-changer) on Google Colab, still not perfect but is totally usable, you can use the following settings for better results:\n", "\n", "If you're using a index: `f0: RMVPE_ONNX | Chunk: 112 or higher | Extra: 8192`\\\n", "If you're not using a index: `f0: RMVPE_ONNX | Chunk: 96 or higher | Extra: 16384`\\\n", "**Don't forget to select your Colab GPU in the GPU field (Tesla T4, for free users)*\n", "> Seems that PTH models performance better than ONNX for now, you can still try ONNX models and see if it satisfies you\n", "\n", "\n", "*You can always [click here](https://github.com/YunaOneeChan/Voice-Changer-Settings) to check if these settings are up-to-date*\n", "

\n", "\n", "---\n", "\n", "###Always use Colab GPU (**VERY VERY VERY IMPORTANT!**)\n", "You need to use a Colab GPU so the Voice Changer can work faster and better\\\n", "Use the menu above and click on **Runtime** » **Change runtime** » **Hardware acceleration** to select a GPU (**T4 is the free one**)\n", "\n", "---\n", "\n", "
\n", "\n", "# **Credits and Support**\n", "Realtime Voice Changer by [w-okada](https://github.com/w-okada)\\\n", "Colab files updated by [rafacasari](https://github.com/Rafacasari)\\\n", "Recommended settings by [YunaOneeChan](https://github.com/YunaOneeChan)\n", "\n", "Need help? [AI Hub Discord](https://discord.gg/aihub) » ***#help-realtime-vc***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "RhdqDSt-LfGr" }, "outputs": [], "source": [ "# @title **[Optional]** Connect to Google Drive\n", "# @markdown Using Google Drive can improve load times a bit and your models will be stored, so you don't need to re-upload every time that you use.\n", "import os\n", "from google.colab import drive\n", "\n", "if not os.path.exists('/content/drive'):\n", " drive.mount('/content/drive')\n", "\n", "%cd /content/drive/MyDrive" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "86wTFmqsNMnD", "cellView": "form" }, "outputs": [], "source": [ "# @title **[1]** Clone repository and install dependencies\n", "# @markdown This first step will download the latest version of Voice Changer and install the dependencies. **It will take around 2 minutes to complete.**\n", "\n", "!git clone --depth 1 https://github.com/w-okada/voice-changer.git &> /dev/null\n", "\n", "%cd voice-changer/server/\n", "print(\"\\033[92mSuccessfully cloned the repository\")\n", "\n", "!apt-get install libportaudio2 &> /dev/null\n", "!pip install onnxruntime-gpu uvicorn faiss-gpu fairseq jedi google-colab moviepy decorator==4.4.2 sounddevice numpy==1.23.5 pyngrok --quiet\n", "!pip install -r requirements.txt --no-build-isolation --quiet\n", "# Maybe install Tensor packages?\n", "#!pip install torch-tensorrt\n", "#!pip install TensorRT\n", "print(\"\\033[92mSuccessfully installed all packages!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lLWQuUd7WW9U", "cellView": "form" }, "outputs": [], "source": [ "# @title **[2]** Start Server **using ngrok** (Recommended | **need a ngrok account**)\n", "# @markdown This cell will start the server, the first time that you run it will download the models, so it can take a while (~1-2 minutes)\n", "\n", "# @markdown ---\n", "# @markdown You'll need a ngrok account, but **it's free**!\n", "# @markdown ---\n", "# @markdown **1** - Create a **free** account at [ngrok](https://dashboard.ngrok.com/signup)\\\n", "# @markdown **2** - If you didn't logged in with Google or Github, you will need to **verify your e-mail**!\\\n", "# @markdown **3** - Click [this link](https://dashboard.ngrok.com/get-started/your-authtoken) to get your auth token, copy it and place it here:\n", "from pyngrok import conf, ngrok\n", "\n", "Token = '' # @param {type:\"string\"}\n", "# @markdown **4** - Still need further tests, but maybe region can help a bit on latency?\\\n", "# @markdown `Default Region: us - United States (Ohio)`\n", "Region = \"us - United States (Ohio)\" # @param [\"ap - Asia/Pacific (Singapore)\", \"au - Australia (Sydney)\",\"eu - Europe (Frankfurt)\", \"in - India (Mumbai)\",\"jp - Japan (Tokyo)\",\"sa - South America (Sao Paulo)\", \"us - United States (Ohio)\"]\n", "\n", "MyConfig = conf.PyngrokConfig()\n", "\n", "MyConfig.auth_token = Token\n", "MyConfig.region = Region[0:2]\n", "\n", "conf.get_default().authtoken = Token\n", "conf.get_default().region = Region[0:2]\n", "\n", "conf.set_default(MyConfig);\n", "\n", "# @markdown ---\n", "# @markdown If you want to automatically clear the output when the server loads, check this option.\n", "Clear_Output = True # @param {type:\"boolean\"}\n", "\n", "import portpicker, subprocess, threading, time, socket, urllib.request\n", "PORT = portpicker.pick_unused_port()\n", "\n", "from IPython.display import clear_output, Javascript\n", "\n", "from pyngrok import ngrok\n", "ngrokConnection = ngrok.connect(PORT)\n", "public_url = ngrokConnection.public_url\n", "\n", "def iframe_thread(port):\n", " while True:\n", " time.sleep(0.5)\n", " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", " result = sock.connect_ex(('127.0.0.1', port))\n", " if result == 0:\n", " break\n", " sock.close()\n", " clear_output()\n", " print(\"------- SERVER READY! -------\")\n", " print(\"Your server is available at:\")\n", " print(public_url)\n", " print(\"-----------------------------\")\n", " display(Javascript('window.open(\"{url}\", \\'_blank\\');'.format(url=public_url)))\n", "\n", "threading.Thread(target=iframe_thread, daemon=True, args=(PORT,)).start()\n", "\n", "!python3 MMVCServerSIO.py \\\n", " -p {PORT} \\\n", " --https False \\\n", " --content_vec_500 pretrain/checkpoint_best_legacy_500.pt \\\n", " --content_vec_500_onnx pretrain/content_vec_500.onnx \\\n", " --content_vec_500_onnx_on true \\\n", " --hubert_base pretrain/hubert_base.pt \\\n", " --hubert_base_jp pretrain/rinna_hubert_base_jp.pt \\\n", " --hubert_soft pretrain/hubert/hubert-soft-0d54a1f4.pt \\\n", " --nsf_hifigan pretrain/nsf_hifigan/model \\\n", " --crepe_onnx_full pretrain/crepe_onnx_full.onnx \\\n", " --crepe_onnx_tiny pretrain/crepe_onnx_tiny.onnx \\\n", " --rmvpe pretrain/rmvpe.pt \\\n", " --model_dir model_dir \\\n", " --samples samples.json" ] }, { "cell_type": "code", "source": [ "# @title **[Optional]** Start Server **using localtunnel** (ngrok alternative | no account needed)\n", "# @markdown This cell will start the server, the first time that you run it will download the models, so it can take a while (~1-2 minutes)\n", "\n", "# @markdown ---\n", "!npm config set update-notifier false\n", "!npm install -g localtunnel\n", "print(\"\\033[92mLocalTunnel installed!\")\n", "# @markdown If you want to automatically clear the output when the server loads, check this option.\n", "Clear_Output = True # @param {type:\"boolean\"}\n", "\n", "import portpicker, subprocess, threading, time, socket, urllib.request\n", "PORT = portpicker.pick_unused_port()\n", "\n", "from IPython.display import clear_output, Javascript\n", "\n", "def iframe_thread(port):\n", " while True:\n", " time.sleep(0.5)\n", " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", " result = sock.connect_ex(('127.0.0.1', port))\n", " if result == 0:\n", " break\n", " sock.close()\n", " clear_output()\n", " print(\"Use the following endpoint to connect to localtunnel:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", " for line in p.stdout:\n", " print(line.decode(), end='')\n", "\n", "threading.Thread(target=iframe_thread, daemon=True, args=(PORT,)).start()\n", "\n", "\n", "!python3 MMVCServerSIO.py \\\n", " -p {PORT} \\\n", " --https False \\\n", " --content_vec_500 pretrain/checkpoint_best_legacy_500.pt \\\n", " --content_vec_500_onnx pretrain/content_vec_500.onnx \\\n", " --content_vec_500_onnx_on true \\\n", " --hubert_base pretrain/hubert_base.pt \\\n", " --hubert_base_jp pretrain/rinna_hubert_base_jp.pt \\\n", " --hubert_soft pretrain/hubert/hubert-soft-0d54a1f4.pt \\\n", " --nsf_hifigan pretrain/nsf_hifigan/model \\\n", " --crepe_onnx_full pretrain/crepe_onnx_full.onnx \\\n", " --crepe_onnx_tiny pretrain/crepe_onnx_tiny.onnx \\\n", " --rmvpe pretrain/rmvpe.pt \\\n", " --model_dir model_dir \\\n", " --samples samples.json \\\n", " --colab True" ], "metadata": { "cellView": "form", "id": "ZwZaCf4BeZi2" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# In Development | **Need contributors**" ], "metadata": { "id": "iuf9pBHYpTn-" } }, { "cell_type": "code", "source": [ "# @title **[BROKEN]** Start Server using Colab Tunnels (trying to fix this TwT)\n", "# @markdown **Issue:** Everything starts correctly, but when you try to use the client, you'll see in your browser console a bunch of errors **(Error 500 - Not Allowed.)**\n", "\n", "import portpicker, subprocess, threading, time, socket, urllib.request\n", "PORT = portpicker.pick_unused_port()\n", "\n", "def iframe_thread(port):\n", " while True:\n", " time.sleep(0.5)\n", " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", " result = sock.connect_ex(('127.0.0.1', port))\n", " if result == 0:\n", " break\n", " sock.close()\n", " from google.colab.output import serve_kernel_port_as_window\n", " serve_kernel_port_as_window(PORT)\n", "\n", "threading.Thread(target=iframe_thread, daemon=True, args=(PORT,)).start()\n", "\n", "!python3 MMVCServerSIO.py \\\n", " -p {PORT} \\\n", " --https False \\\n", " --content_vec_500 pretrain/checkpoint_best_legacy_500.pt \\\n", " --content_vec_500_onnx pretrain/content_vec_500.onnx \\\n", " --content_vec_500_onnx_on true \\\n", " --hubert_base pretrain/hubert_base.pt \\\n", " --hubert_base_jp pretrain/rinna_hubert_base_jp.pt \\\n", " --hubert_soft pretrain/hubert/hubert-soft-0d54a1f4.pt \\\n", " --nsf_hifigan pretrain/nsf_hifigan/model \\\n", " --crepe_onnx_full pretrain/crepe_onnx_full.onnx \\\n", " --crepe_onnx_tiny pretrain/crepe_onnx_tiny.onnx \\\n", " --rmvpe pretrain/rmvpe.pt \\\n", " --model_dir model_dir \\\n", " --samples samples.json" ], "metadata": { "id": "P2BN-iWvDrMM", "cellView": "form" }, "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "provenance": [], "private_outputs": true, "include_colab_link": true, "gpuType": "T4", "collapsed_sections": [ "iuf9pBHYpTn-" ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "nbformat": 4, "nbformat_minor": 0 }