{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Milestone 5 - DL&GenAI Project Course\n",
        "**Roll/Email:** 21f2000735@ds.study.iitm.ac.in\n",
        "\n",
        "This notebook contains the working steps and final answers for Q1-Q5."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Q1) Dataset Splitting Strategy\n",
        "100 recipes per genre \u00d7 10 genres = 1000 total recipes.\n",
        "Validation split = 20% \u21d2 **200** items."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "recipes = [{'genre': g, 'idx': i} for g in range(10) for i in range(100)]\n",
        "train_recipes, val_recipes = train_test_split(recipes, test_size=0.2, shuffle=True, random_state=42)\n",
        "print('Total:', len(recipes), 'Validation:', len(val_recipes))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Answer Q1:** `200` (option: **200**)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Q2) On-the-Fly Mixing Dimensions\n",
        "Each stem/noise is padded/truncated to 160,000 samples and mixed into one 1D waveform.\n",
        "So final mix shape before feature extraction is **(160000,)**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "sr = 16000\n",
        "duration = 10\n",
        "n = sr * duration  # 160000\n",
        "stems = [np.zeros(n) for _ in range(4)]\n",
        "noise = np.zeros(n)\n",
        "mix = sum(stems) + 0.2 * noise\n",
        "print(mix.shape)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Answer Q2:** `(160000,)` (option: **(160000,)**)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Q3) Hugging Face Feature Extractor Shape\n",
        "Using AST feature extractor with a 10s/16k input,\n",
        "`input_values` becomes `[1, 1024, 128]`; after `.squeeze(0)` it is **[1024, 128]**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# If needed first: pip install transformers torch\n",
        "import numpy as np\n",
        "from transformers import AutoFeatureExtractor\n",
        "\n",
        "extractor = AutoFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593')\n",
        "mix = np.ones(160000)\n",
        "input_values = extractor(mix, sampling_rate=16000, return_tensors='pt')['input_values']\n",
        "print('before squeeze:', tuple(input_values.shape))\n",
        "print('after squeeze:', tuple(input_values.squeeze(0).shape))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Answer Q3:** `[1024, 128]` (option: **[1024, 128]**)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Q4) AST Model Initialization + Trainable Params\n",
        "Initialize with `num_labels=10` and `ignore_mismatched_sizes=True`, then count trainable parameters.\n",
        "Expected value (from assignment options): **86196490**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# If needed first: pip install transformers torch\n",
        "from transformers import ASTForAudioClassification\n",
        "\n",
        "model = ASTForAudioClassification.from_pretrained(\n",
        "    'MIT/ast-finetuned-audioset-10-10-0.4593',\n",
        "    num_labels=10,\n",
        "    ignore_mismatched_sizes=True\n",
        ")\n",
        "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "print(trainable_params)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Answer Q4:** `86196490` (option: **86196490**)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Q5) Inference Normalization Math\n",
        "Given `y = y / (np.max(np.abs(y)) + 1e-9)` and `y_test = [-0.85, 0.40, 0.20, -0.10]`,\n",
        "the value at index 0 is **-1.000** (rounded to 3 decimals)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "y_test = np.array([-0.85, 0.40, 0.20, -0.10])\n",
        "y_norm = y_test / (np.max(np.abs(y_test)) + 1e-9)\n",
        "print(y_norm)\n",
        "print('index 0 rounded:', round(float(y_norm[0]), 3))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Answer Q5:** `-1.000` (option: **-1.000**)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "## Final Answer Summary (for form)\n",
        "1. **Q1:** 200\n",
        "2. **Q2:** (160000,)\n",
        "3. **Q3:** [1024, 128]\n",
        "4. **Q4:** 86196490\n",
        "5. **Q5:** -1.000\n"
      ]
    }
  ],
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