OpenAI Lyrics Analysis

index.js

#!/bin/env node
// Use the openai package from npm to call ChatGPT
import OpenAI from "openai";
import fs from "fs";

// Create a new instance of the openai client with our API key
const openai = new OpenAI({
  apiKey: process.env.OPENAI_KEY,
});

const model = "gpt-3.5-turbo";
// const model = "gpt-4";

const file = "without-me";
// const file = "slim-shady";

// Base64 encoded https://genius.com/Eminem-the-real-slim-shady-lyrics
const text = atob(fs.readFileSync(file.".txt"));

const makeRequest = async (model) => {
  return await openai.chat.completions.create({
    model: model,
    messages: [
      {
        role: "user",
        content: `Analyze the following lyrics`,
      },
      {
        role: "user",
        content: text,
      },
    ],
    functions: [
      {
        name: "lyrict_analyze",
        parameters: {
          type: "object",
          properties: {
            is_explicit: {
              type: "boolean",
              description: "Do these lyrics have explicit content or profanity",
            },
            explicit_markers: {
              type: "array",
              items: {
                type: "string",
                enum: [
                  "sexual_content",
                  "explicit_language",
                  "drugs_references",
                ],
              },
              description: "All reasons why lyrics considered as explicit",
            },
            explicit_explanation: {
              type: "string",
              description:
                "Detailed explanation about exact topics contained in lyrics why they considered profane or explicit",
            },
            explicit_words: {
              type: "array",
              items: {
                type: "string",
              },
              description:
                "A complete list of all unique words are considered explicit or profane in these lyrics in lowercase",
            },
            short_meaning: {
              type: "string",
              maxLength: 500,
              minLength: 100,
              description:
                "A brief description of the song lyrics not longer than five hundred characters",
            },
          },
          required: [
            "is_explicit",
            "explicit_markers",
            "explicit_explanation",
            "explicit_words",
            "short_meaning",
          ],
        },
      },
    ],
    function_call: { name: "lyrict_analyze" },
  });
};

const results = [];
for (let i = 0; i < 30; i++) {
  const start = new Date().valueOf();
  const gptResponse = await makeRequest(model);
  const functionCall = gptResponse.choices[0].message.function_call;

  const json = JSON.parse(functionCall.arguments);
  const res = new Date().valueOf() - start;
  json.duration = res;
  results.push(json);
  console.log(i);
  console.log(json);
}

fs.writeFileSync(`${file}-${model}.json`, JSON.stringify(results));

package.json

{
  "name": "openai-test",
  "type": "module",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "author": "",
  "license": "ISC",
  "dependencies": {
    "openai": "^4.14.0"
  }
}

parse.js

#!/bin/env node

import fs from "fs";

const model = "gpt-3.5-turbo";
// const model = "gpt-4";

const file = "without-me";
// const file = "slim-shady";

const percent = (input, all) => Math.round((100 * input) / all) + "%";

let res = [];
res = JSON.parse(fs.readFileSync(`${file}-${model}.json`));

let len = res.length;
let positive = res.filter((item) => item.is_explicit).length;
let all_words = res.map((item) => item.explicit_words).flat();
let unique_words = new Set(all_words);

console.log("Model:", model);
console.log("Attempts:", len);
console.log("Positive results:", positive, percent(positive, len));

let meaning = res
  .map((item) => item.short_meaning.length)
  .filter((item) => item <= 500).length;

console.log("Meaning less then 500 chars:", percent(meaning, len));

let duration =
  res.map((item) => item.duration).reduce((a, b) => a + b, 0) / res.length;

console.log("Avg duration", Math.round(duration), "ms");

let all_markers = res.map((item) => item.explicit_markers).flat();
let unique_markers = new Set(all_markers);

console.log("Statistics per marker:");
let usage_markers = {};
for (let unique_marker of unique_markers) {
  usage_markers[unique_marker] = percent(
    all_markers.filter((word) => word == unique_marker).length,
    positive,
  );
}
console.table(usage_markers);

console.log("Statistics per profane word:");
let usage_words = {};
for (let unique_word of unique_words) {
  usage_words[unique_word] = percent(
    all_words.filter((word) => word == unique_word).length,
    positive,
  );
}
console.table(usage_words);

slim-shady.txt

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

without-me.txt

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