KeywordExtractor_byList.ipynb

{
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  "metadata": {
    "colab": {
      "name": "KeywordExtractor_byList.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO011Ij8tgh7goDDjNZ8s44",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/toedtli/rg_text_to_sound/blob/main/KeywordExtractor_byList.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAyPJN9X-C7j"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\"\"\"\n",
        "import tensorflow_text as text  # Registers the ops.\n",
        "from tts_pipeline.pipelines.waterfall.pipeline import (\n",
        "    WaterfallKeywordExtractor,\n",
        "    WaterfallEmbedder,\n",
        "    WaterfallDimensionalityReducer\n",
        ")\n",
        "import spacy\n",
        "#from nltk.corpus import wordnet as wn\n",
        "\"\"\"\n",
        "class KeywordExtractorByList(object):\n",
        "    def build(self):\n",
        "\n",
        "        # Load English tokenizer, tagger, parser and NER\n",
        "        self.nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "    def predict(self, sentence: str) -> dict:\n",
        "        tokens      = sentence.split(\" \")\n",
        "        lengths     = [len(x) for x in tokens]\n",
        "        max_len_idx = [i for i,l in enumerate(lengths) if l==max(lengths)][0]\n",
        "        instrument   = tokens[max_len_idx]\n",
        "\n",
        "        doc1 = nlp(\"slow, super slow, snail, unhurried, leisurely, measured, moderate, deliberate, steady, sedate, slow-moving, slow-going, easy, relaxed, unrushed, gentle, undemanding, comfortable, ponderous, plodding, laboured, dawdling, loitering, lagging, laggard, sluggish, sluggardly, snail-like, tortoise-like, leaden-footed, leaden, creeping, laggy, lollygagging, calm, gently, docile, friendly, easy, dull, tediously, lazy, sleepily, tardy, indolent, graceful, largo, adagio, sluggish, relaxed, casual, belatedly, tardily, ritardando, latterly, lately, lenient, poor, dully, lethargically\")\n",
        "        doc2 = nlp(\"speedy, quick, swift, rapid, brisk, nimble, sprightly, lively, fast-moving, high-speed, turbo, sporty, accelerated, express, flying, whirlwind, blistering, breakneck, pell-mell, meteoric, smart, hasty, hurried, unhesitating, expeditious, fleet-footed, nippy, zippy, spanking, scorching, blinding, supersonic, cracking, fleet, tantivy, alacritous, volant, secure, secured, fastened, tight, firmly fixed, stuck, jammed, immovable, unbudgeable, stiff, closed, shut, to, attach, fasten, fix, affix, join, connect, couple, link, tie, tie up, bind, fetter, strap, rope, tether, truss, lash, hitch, moor, anchor, yoke, chain, quickly, rapidly, swiftly, speedily, briskly, at speed, at full speed, at full tilt, energetically, hastily,with all haste, in haste, hurriedly, in a hurry, post-haste, pell-mell, without delay, expeditiously, with dispatch, like a shot, like a flash, in a flash, in the blink of an eye, in a wink, in a trice, in no time (at all), on the double, at the speed of light, like an arrow from a bow, double quick, in double quick time, p.d.q. (pretty damn quick), nippily, like (greased) lightning, at warp speed, hell for leather, like mad, like crazy, like the wind, like a bomb, like nobody's business, like a scalded cat, like the deuce, a mile a minute, like a bat out of hell, like the clappers, at a rate of knots, like billy-o, lickety-split, apace, 2., securely, tightly, immovably, fixedly\" )\n",
        "        sim_doc1 = doc.similarity(doc1)\n",
        "        sim_doc2 = doc.similarity(doc2)\n",
        "\n",
        "        velocity = 0+100*(sim_doc1-sim_doc2)/(sim_doc1+sim_doc2)\n",
        "        return {\n",
        "            \"soundquality\": tokens,\n",
        "            \"instrument\"  : instrument,\n",
        "            \"velocity\"    : 75,\n",
        "            \"pitch\"       : 60\n",
        "        }\n",
        "\n",
        "    def dispose(self):\n",
        "        pass\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlcYGyJj-4Dr"
      },
      "source": [
        ""
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "9Hbg69VQ_Nft",
        "outputId": "bacd549f-4224-43a7-96a8-1c36bfc00532"
      },
      "source": [
        "spacy.__version__"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.2.4'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYdqQECV-05w",
        "outputId": "515dc06d-22a5-4299-af49-054cd94d7b85"
      },
      "source": [
        "!python -m spacy download en_core_web_lg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting en_core_web_lg==2.2.5\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.2.5/en_core_web_lg-2.2.5.tar.gz (827.9MB)\n",
            "\u001b[K     |████████████████████████████████| 827.9MB 1.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_lg==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.5)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.0.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (54.1.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.8.2)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.7.2)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.4.1)\n",
            "Building wheels for collected packages: en-core-web-lg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6iQUNKFt-H1u",
        "outputId": "2207635d-ce8f-4db8-eaed-08902ed76a81"
      },
      "source": [
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_lg\")\n",
        "doc1 = nlp(\"slow, super slow, snail, unhurried, leisurely, measured, moderate, deliberate, steady, sedate, slow-moving, slow-going, easy, relaxed, unrushed, gentle, undemanding, comfortable, ponderous, plodding, laboured, dawdling, loitering, lagging, laggard, sluggish, sluggardly, snail-like, tortoise-like, leaden-footed, leaden, creeping, laggy, lollygagging, calm, gently, docile, friendly, easy, dull, tediously, lazy, sleepily, tardy, indolent, graceful, largo, adagio, sluggish, relaxed, casual, belatedly, tardily, ritardando, latterly, lately, lenient, poor, dully, lethargically\")\n",
        "doc2 = nlp(\"speedy, quick, swift, rapid, brisk, nimble, sprightly, lively, fast-moving, high-speed, turbo, sporty, accelerated, express, flying, whirlwind, blistering, breakneck, pell-mell, meteoric, smart, hasty, hurried, unhesitating, expeditious, fleet-footed, nippy, zippy, spanking, scorching, blinding, supersonic, cracking, fleet, tantivy, alacritous, volant, secure, secured, fastened, tight, firmly fixed, stuck, jammed, immovable, unbudgeable, stiff, closed, shut, to, attach, fasten, fix, affix, join, connect, couple, link, tie, tie up, bind, fetter, strap, rope, tether, truss, lash, hitch, moor, anchor, yoke, chain, quickly, rapidly, swiftly, speedily, briskly, at speed, at full speed, at full tilt, energetically, hastily,with all haste, in haste, hurriedly, in a hurry, post-haste, pell-mell, without delay, expeditiously, with dispatch, like a shot, like a flash, in a flash, in the blink of an eye, in a wink, in a trice, in no time (at all), on the double, at the speed of light, like an arrow from a bow, double quick, in double quick time, p.d.q. (pretty damn quick), nippily, like (greased) lightning, at warp speed, hell for leather, like mad, like crazy, like the wind, like a bomb, like nobody's business, like a scalded cat, like the deuce, a mile a minute, like a bat out of hell, like the clappers, at a rate of knots, like billy-o, lickety-split, apace, 2., securely, tightly, immovably, fixedly\" )\n",
        "\n",
        "doc =  nlp('give me a quick guitar')\n",
        "#doc =  nlp('give me a lazy guitar')\n",
        "sim_doc1 = doc.similarity(doc1)\n",
        "sim_doc2 = doc.similarity(doc2)\n",
        "sim_doc1,sim_doc2"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.7/runpy.py:193: ModelsWarning: [W007] The model you're using has no word vectors loaded, so the result of the Doc.similarity method will be based on the tagger, parser and NER, which may not give useful similarity judgements. This may happen if you're using one of the small models, e.g. `en_core_web_sm`, which don't ship with word vectors and only use context-sensitive tensors. You can always add your own word vectors, or use one of the larger models instead if available.\n",
            "  \"__main__\", mod_spec)\n",
            "/usr/lib/python3.7/runpy.py:193: ModelsWarning: [W007] The model you're using has no word vectors loaded, so the result of the Doc.similarity method will be based on the tagger, parser and NER, which may not give useful similarity judgements. This may happen if you're using one of the small models, e.g. `en_core_web_sm`, which don't ship with word vectors and only use context-sensitive tensors. You can always add your own word vectors, or use one of the larger models instead if available.\n",
            "  \"__main__\", mod_spec)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.10975228735110713, 0.24395974478106727)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWysvk6F-THz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}