{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tortoise-tts.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Welcome to Tortoise! 🐢🐢🐢🐢\n",
        "\n",
        "Before you begin, I **strongly** recommend you turn on a GPU runtime.\n",
        "\n",
        "There's a reason this is called \"Tortoise\" - this model takes up to a minute to perform inference for a single sentence on a GPU. Expect waits on the order of hours on a CPU."
      ],
      "metadata": {
        "id": "_pIZ3ZXNp7cf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrK20I32grP6"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/neonbjb/tortoise-tts.git\n",
        "%cd tortoise-tts\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imports used through the rest of the notebook.\n",
        "import torch\n",
        "import torchaudio\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import IPython\n",
        "\n",
        "from api import TextToSpeech\n",
        "from utils.audio import load_audio, get_voices\n",
        "\n",
        "# This will download all the models used by Tortoise from the HF hub.\n",
        "tts = TextToSpeech()"
      ],
      "metadata": {
        "id": "Gen09NM4hONQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List all the voices available. These are just some random clips I've gathered\n",
        "# from the internet as well as a few voices from the training dataset.\n",
        "# Feel free to add your own clips to the voices/ folder.\n",
        "%ls voices"
      ],
      "metadata": {
        "id": "SSleVnRAiEE2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This is the text that will be spoken.\n",
        "text = \"Joining two modalities results in a surprising increase in generalization! What would happen if we combined them all?\"\n",
        "\n",
        "# Here's something for the poetically inclined.. (set text=)\n",
        "\"\"\"\n",
        "Then took the other, as just as fair,\n",
        "And having perhaps the better claim,\n",
        "Because it was grassy and wanted wear;\n",
        "Though as for that the passing there\n",
        "Had worn them really about the same,\"\"\"\n",
        "\n",
        "# Pick one of the voices from above\n",
        "voice = 'train_dotrice'\n",
        "# Pick a \"preset mode\" to determine quality. Options: {\"ultra_fast\", \"fast\" (default), \"standard\", \"high_quality\"}. See docs in api.py\n",
        "preset = \"fast\""
      ],
      "metadata": {
        "id": "bt_aoxONjfL2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetch the voice references and forward execute!\n",
        "voices = get_voices()\n",
        "cond_paths = voices[voice]\n",
        "conds = []\n",
        "for cond_path in cond_paths:\n",
        "    c = load_audio(cond_path, 22050)\n",
        "    conds.append(c)\n",
        "\n",
        "gen = tts.tts_with_preset(text, conds, preset)\n",
        "torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)\n",
        "IPython.display.Audio('generated.wav')"
      ],
      "metadata": {
        "id": "KEXOKjIvn6NW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# You can add as many conditioning voices as you want together. Combining\n",
        "# clips from multiple voices takes the mean of the latent space for all\n",
        "# voices. This creates a novel voice that is a combination of the two inputs.\n",
        "#\n",
        "# Lets see what it would sound like if Picard and Kirk had a kid with a penchant for philosophy:\n",
        "conds = []\n",
        "for v in ['pat', 'william']:\n",
        "  cond_paths = voices[v]\n",
        "  for cond_path in cond_paths:\n",
        "      c = load_audio(cond_path, 22050)\n",
        "      conds.append(c)\n",
        "\n",
        "gen = tts.tts_with_preset(\"They used to say that if man was meant to fly, he’d have wings. But he did fly. He discovered he had to.\", conds, preset)\n",
        "torchaudio.save('captain_kirkard.wav', gen.squeeze(0).cpu(), 24000)\n",
        "IPython.display.Audio('captain_kirkard.wav')"
      ],
      "metadata": {
        "id": "fYTk8KUezUr5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}