Create setup.py

setup.py

@@ -0,0 +1,185 @@
+{
+  "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",
+        "!pip3 install -r requirements.txt\n",
+        "!python3 setup.py install"
+      ]
+    },
+    {
+      "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 tortoise.api import TextToSpeech\n",
+        "from tortoise.utils.audio import load_audio, load_voice, load_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": [
+        "# 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 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": [
+        "# Tortoise will attempt to mimic voices you provide. It comes pre-packaged\n",
+        "# with some voices you might recognize.\n",
+        "\n",
+        "# Let's 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 tortoise/voices\n",
+        "\n",
+        "IPython.display.Audio('tortoise/voices/tom/1.wav')"
+      ],
+      "metadata": {
+        "id": "SSleVnRAiEE2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Pick one of the voices from the output above\n",
+        "voice = 'tom'\n",
+        "\n",
+        "# Load it and send it through Tortoise.\n",
+        "voice_samples, conditioning_latents = load_voice(voice)\n",
+        "gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents, \n",
+        "                          preset=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": [
+        "# Tortoise can also generate speech using a random voice. The voice changes each time you execute this!\n",
+        "# (Note: random voices can be prone to strange utterances)\n",
+        "gen = tts.tts_with_preset(text, voice_samples=None, conditioning_latents=None, preset=preset)\n",
+        "torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)\n",
+        "IPython.display.Audio('generated.wav')"
+      ],
+      "metadata": {
+        "id": "16Xs2SSC3BXa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# You can also combine conditioning voices. Combining voices produces a new voice\n",
+        "# with traits from all the parents.\n",
+        "#\n",
+        "# Lets see what it would sound like if Picard and Kirk had a kid with a penchant for philosophy:\n",
+        "voice_samples, conditioning_latents = load_voices(['pat', 'william'])\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.\", \n",
+        "                          voice_samples=None, conditioning_latents=None, preset=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": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "del tts  # Will break other cells, but necessary to conserve RAM if you want to run this cell.\n",
+        "\n",
+        "# Tortoise comes with some scripts that does a lot of the lifting for you. For example,\n",
+        "# read.py will read a text file for you.\n",
+        "!python3 tortoise/read.py --voice=train_atkins --textfile=tortoise/data/riding_hood.txt --preset=ultra_fast --output_path=.\n",
+        "\n",
+        "IPython.display.Audio('train_atkins/combined.wav')\n",
+        "# This will take awhile.."
+      ],
+      "metadata": {
+        "id": "t66yqWgu68KL"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}