main.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "0a85f68a",
      "metadata": {
        "id": "0a85f68a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23da313d-7b95-4ad2-c55e-08c948ad6b53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mido in /usr/local/lib/python3.7/dist-packages (1.2.10)\n",
            "Collecting pretty_midi\n",
            "  Downloading pretty_midi-0.2.9.tar.gz (5.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.6 MB 5.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from pretty_midi) (1.21.6)\n",
            "Requirement already satisfied: mido>=1.1.16 in /usr/local/lib/python3.7/dist-packages (from pretty_midi) (1.2.10)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from pretty_midi) (1.15.0)\n",
            "Building wheels for collected packages: pretty-midi\n",
            "  Building wheel for pretty-midi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pretty-midi: filename=pretty_midi-0.2.9-py3-none-any.whl size=5591955 sha256=d1993e3332260e73304d0a91b46d170d91591b390dcccd392c033ac51a564830\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/74/7c/a06473ca8dcb63efb98c1e67667ce39d52100f837835ea18fa\n",
            "Successfully built pretty-midi\n",
            "Installing collected packages: pretty-midi\n",
            "Successfully installed pretty-midi-0.2.9\n",
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import gc\n",
        "import os\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import random\n",
        "import string\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "\n",
        "# if using google colab - set up path properly\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "    !pip install mido\n",
        "    !pip install pretty_midi\n",
        "\n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/drive/')\n",
        "\n",
        "    import sys\n",
        "    cwd = '/content/drive/My Drive/school/stat4984/final_proj/'\n",
        "    sys.path.append(cwd)\n",
        "\n",
        "else:\n",
        "    cwd = os.getcwd()+'/'\n",
        "\n",
        "from midi_ndarrays import *\n",
        "from midi_utils import *\n",
        "\n",
        "# set up directories\n",
        "midi_data_dir = cwd+'midi_data/'\n",
        "csv_data_dir  = cwd+'csv_data/'\n",
        "\n",
        "if not os.path.exists(midi_data_dir):\n",
        "    os.makedirs(midi_data_dir)\n",
        "\n",
        "if not os.path.exists(midi_data_dir):\n",
        "    os.makedirs(csv_data_dir)\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Utility Functions\n",
        "\n"
      ],
      "metadata": {
        "id": "E7hyJyDDm5FR"
      },
      "id": "E7hyJyDDm5FR"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c33f11e6",
      "metadata": {
        "id": "c33f11e6"
      },
      "outputs": [],
      "source": [
        "def dir_idx(dir_name, n):\n",
        "    \"\"\"\n",
        "    return the name of the nth file in a directory\n",
        "    \"\"\"\n",
        "    \n",
        "    return dir_name+os.listdir(dir_name)[n]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_GPU_memory():\n",
        "    max = torch.cuda.max_memory_allocated()\n",
        "    \n",
        "    print(0 if max == 0 else torch.cuda.memory_allocated() / max)"
      ],
      "metadata": {
        "id": "ImJHobrbm98e"
      },
      "id": "ImJHobrbm98e",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def free_GPU():\n",
        "    gc.collect()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        torch.cuda.empty_cache()\n",
        "    torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()"
      ],
      "metadata": {
        "id": "gLPUtjAbnQDk"
      },
      "id": "gLPUtjAbnQDk",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "caed195a",
      "metadata": {
        "id": "caed195a"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "dabc932d",
      "metadata": {
        "scrolled": true,
        "id": "dabc932d"
      },
      "outputs": [],
      "source": [
        "# only download midis if the batch dir doesn't exist and midi dir is empty\n",
        "if len(os.listdir(midi_data_dir)) == 0 and len(os.listdir(csv_data_dir)) == 0:\n",
        "    import mido\n",
        "    download_midis(midi_data_dir)\n",
        "\n",
        "def load_array(midi_filename):\n",
        "    \"\"\"\n",
        "    return midi_filename as a ndarray with start and end tokens\n",
        "    Replace all notes with 1 - played, or 0 - not played\n",
        "    \"\"\"\n",
        "    midi_tracks = mido.MidiFile(midi_filename, clip=True)\n",
        "    midi_array = mid2array(midi_tracks)\n",
        "    \n",
        "    # set all values to 1 or 0\n",
        "    midi_array = np.where(midi_array != 0, 1, 0).astype('uint8')\n",
        "    \n",
        "    # add padding and encode start token (first column)\n",
        "    # and end token (last column)\n",
        "    midi_array = np.pad(midi_array, 1)\n",
        "    midi_array[0, 0]   = 1\n",
        "    midi_array[-1, -1] = 1\n",
        "    \n",
        "    return midi_array\n",
        "\n",
        "# if there aren't any csv files, convert all midis arrays and save in csv\n",
        "if len(os.listdir(csv_data_dir)) == 0:\n",
        "    for i in range(len(os.listdir(midi_data_dir))):\n",
        "        midi_filename = dir_idx(midi_data_dir, i)\n",
        "        midi_array = load_array(midi_filename)\n",
        "        np.savetxt(midi_filename[:-4]+\".csv\", midi_array, fmt=\"%d\", delimiter=\",\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c4f7a8b",
      "metadata": {
        "id": "1c4f7a8b"
      },
      "source": [
        "# The Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "8f0840d8",
      "metadata": {
        "id": "8f0840d8"
      },
      "outputs": [],
      "source": [
        "class EncoderRNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_layers, dropout_p):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.input_size  = input_size\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers  = num_layers\n",
        "        \n",
        "        self.dropout   = nn.Dropout(dropout_p)\n",
        "        self.embedding = nn.Embedding(input_size, hidden_size)\n",
        "        self.rnn       = nn.LSTM(hidden_size, hidden_size, num_layers, dropout=dropout_p)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        # input dimension [1, 90]\n",
        "        embedding = self.dropout(self.embedding(x))\n",
        "        _, (h, c) = self.rnn(embedding)\n",
        "        return h, c"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DecoderRNN(nn.Module):\n",
        "    # TODO remove output_size\n",
        "    def __init__(self, input_size, hidden_size, output_size, \n",
        "                num_layers, dropout_p):\n",
        "    \n",
        "        super().__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers  = num_layers\n",
        "\n",
        "        self.dropout = nn.Dropout(dropout_p)\n",
        "        \n",
        "        self.rnn = nn.LSTM(input_size, hidden_size, num_layers, dropout=dropout_p)\n",
        "        self.fc = nn.Linear(hidden_size, 1)\n",
        "        \n",
        "    def forward(self, x, hidden, cell):\n",
        "        x = x.float()\n",
        "        x = x.unsqueeze(0).unsqueeze(-1)\n",
        "        \n",
        "        # x shape: [1, 90, 1]\n",
        "        outputs, (hidden, cell) = self.rnn(x, (hidden, cell))\n",
        "        predictions = self.fc(outputs.squeeze(0))\n",
        "\n",
        "        # sigmoid because we're predicting multiple notes\n",
        "        predictions = torch.sigmoid(predictions.squeeze(1))\n",
        "\n",
        "        return predictions, hidden, cell"
      ],
      "metadata": {
        "id": "3IwrEkNqgJOH"
      },
      "id": "3IwrEkNqgJOH",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "id": "3e057e12",
      "metadata": {
        "id": "3e057e12"
      },
      "outputs": [],
      "source": [
        "class Seq2Seq(nn.Module):\n",
        "    \"\"\"\n",
        "    Encapsulates the encoder and decoder. Pass a song matrix to the forward method, and it\n",
        "    will encode it, then output the decoder's prediction of it\n",
        "    \"\"\"\n",
        "    def __init__(self, encoder, decoder):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.encoder = encoder\n",
        "        self.decoder = decoder\n",
        "  \n",
        "    def forward(self, x, teacher_forcing_ratio=0.5):\n",
        "        \"\"\"\n",
        "        pre: x is the song on host not device - the gpu runs out of memory\n",
        "             with the whole song\n",
        "        \"\"\"\n",
        "        # x: the song with shape [len, 90]\n",
        "\n",
        "        #tensor to store decoder outputs\n",
        "        outputs = torch.zeros(x.shape)\n",
        "        \n",
        "        # encoder input song\n",
        "        len_song = x.shape[0]\n",
        "\n",
        "        # pass the song to the encoder\n",
        "        h,c = self.encoder(x.to(device))\n",
        "        \n",
        "        #first input to the decoder is the <sos> tokens\n",
        "        input = x[0,:].to(device)\n",
        "\n",
        "        # decode the length of the song and return prediction\n",
        "        for t in range(1, len_song):\n",
        "            \n",
        "            #insert input token embedding, previous hidden and previous cell states\n",
        "            #receive output tensor (predictions) and new hidden and cell states\n",
        "            output, h, c = self.decoder(input, h, c)\n",
        "\n",
        "            #place predictions in a tensor holding predictions for each token\n",
        "            outputs[t] = output.to(\"cpu\")\n",
        "            \n",
        "            #decide if we are going to use teacher forcing or not\n",
        "            teacher_force = random.random() < teacher_forcing_ratio\n",
        "            \n",
        "            #if teacher forcing, use actual next token as next input\n",
        "            #if not, use predicted token\n",
        "            input = x[t,:].to(device) if teacher_force else output\n",
        "\n",
        "        return outputs\n",
        "\n",
        "    def generate_song(self, max_length):\n",
        "        \"\"\"\n",
        "        Generate a song based on the parameters of the encoder and decoder\n",
        "        Pre: encoder and decoder should be optimized\n",
        "        Return: The song as an song_length x 88 size numpy array\n",
        "        \"\"\"\n",
        "\n",
        "        num_notes = self.encoder.input_size\n",
        "\n",
        "        #song_matrix = torch.zeros(max_length, num_notes, dtype=torch.int)\n",
        "\n",
        "        song_matrix = np.zeros((max_length, num_notes), dtype='int')\n",
        "        \n",
        "        # encode start token\n",
        "        input = torch.zeros(1, num_notes, dtype=torch.int)\n",
        "        input[0] = 1\n",
        "\n",
        "        # initial state is based on encoding the start token\n",
        "        input = input.to(device)\n",
        "\n",
        "        h, c = self.encoder(input)\n",
        "\n",
        "        input = input.squeeze(0)\n",
        "\n",
        "        # Predict the notes of this song following inputting the start token\n",
        "        for t in range(1, max_length):\n",
        "            # input is the predicted set of notes, and the input to the next\n",
        "            # prediction\n",
        "            input, h, c = self.decoder(input, h, c)\n",
        "\n",
        "            pred = input.to(\"cpu\").detach().numpy()\n",
        "            \n",
        "            # if no predicted values are greater than 0.5,\n",
        "            # update the next input to the three highest values in the array\n",
        "            if np.where(pred > 0.6)[0].size == 0:\n",
        "                highest_indices = pred.argsort()[-3:][::-1]\n",
        "                row = np.zeros_like(pred)\n",
        "                row[highest_indices] = 1\n",
        "\n",
        "            else:\n",
        "                row = np.where(pred > 0.6, 1, 0).astype('int')\n",
        "            \n",
        "            song_matrix[t] = row\n",
        "\n",
        "            # if end token is predicted: end\n",
        "            if row[-1] > 0.8 and t >= 500:\n",
        "                break\n",
        "\n",
        "            input = torch.Tensor(row).to(device)\n",
        "\n",
        "        # the song generated until the end token (or max_length)\n",
        "        song_matrix = song_matrix[:t+1,:]\n",
        "\n",
        "        # trim outer padding for start and end tokens\n",
        "        return song_matrix[1:-1, 1:-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f0ef1ec3",
      "metadata": {
        "id": "f0ef1ec3"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "5b60e717",
      "metadata": {
        "id": "5b60e717"
      },
      "outputs": [],
      "source": [
        "drop_p = 0.5\n",
        "\n",
        "# data has 88 columns for each piano note, plus two for start and end tokens\n",
        "midi_dim = 90\n",
        "\n",
        "# model hyperparameters\n",
        "hidden_size = 64\n",
        "num_layers = 2\n",
        "\n",
        "# training hyperparemeters\n",
        "num_epochs = 1\n",
        "num_songs  = 10\n",
        "\n",
        "# number of tempos of each song to pass at a time\n",
        "batch_size = 5_000\n",
        "\n",
        "save_model  = True\n",
        "load_model  = True\n",
        "train_model = True\n",
        "model_filename = \"model.pt\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "2779875c",
      "metadata": {
        "id": "2779875c"
      },
      "outputs": [],
      "source": [
        "encoder = EncoderRNN(midi_dim, hidden_size, num_layers, drop_p)\n",
        "decoder = DecoderRNN(1, hidden_size, 1, num_layers, drop_p)\n",
        "model = Seq2Seq(encoder, decoder).to(device)\n",
        "\n",
        "# model = Seq2Seq(encoder, decoder)\n",
        "\n",
        "optimizer = optim.Adam(model.parameters())\n",
        "criterion = nn.BCELoss()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_songs = min(num_songs, len(os.listdir(csv_data_dir)))\n",
        "\n",
        "def train(model, num_songs):\n",
        "    \"\"\"\n",
        "    Pass num_songs to the model and optimize at each\n",
        "    \"\"\"\n",
        "    model.train()\n",
        "\n",
        "    epoch_loss = 0\n",
        "\n",
        "    for i in range(num_songs):\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # load song as numpy array\n",
        "        midi_tensor = np.genfromtxt(dir_idx(csv_data_dir, i), delimiter=',', dtype='int')\n",
        "        \n",
        "        # convert to tensor\n",
        "        midi_tensor = torch.IntTensor(midi_tensor)\n",
        "        len_song = midi_tensor.shape[0]\n",
        "\n",
        "        # number of batches this song can be split into\n",
        "        num_batches = math.ceil(len_song / batch_size)\n",
        "\n",
        "        # decode prediction\n",
        "        for i in range(num_batches):\n",
        "            # last batch\n",
        "            if i == num_batches-1:\n",
        "                batch = midi_tensor[i*batch_size:,:]\n",
        "            \n",
        "            else:\n",
        "                batch = midi_tensor[i*batch_size:(i+1)*batch_size,:]\n",
        "\n",
        "            batch_pred = model(batch.to(device))\n",
        "\n",
        "            # optimize\n",
        "            loss = criterion(batch_pred, batch.float())\n",
        "\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "# load the model\n",
        "if load_model and not save_model and model_filename in os.listdir():\n",
        "    model.load_state_dict(torch.load(model_filename))"
      ],
      "metadata": {
        "id": "JpTbCz7XDfm7"
      },
      "id": "JpTbCz7XDfm7",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train the model\n",
        "if train_model:\n",
        "    for epoch in range(num_epochs):\n",
        "        train(model, num_songs)\n",
        "\n",
        "    # save the model\n",
        "    if save_model:\n",
        "        torch.save(model.state_dict(), cwd+model_filename)"
      ],
      "metadata": {
        "id": "1MUq_2X41gxU"
      },
      "id": "1MUq_2X41gxU",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr2 = model.generate_song(5000)"
      ],
      "metadata": {
        "id": "NO1rk8JA2LPH"
      },
      "id": "NO1rk8JA2LPH",
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_midi_array(arr2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "2KlOJyVMJce5",
        "outputId": "c52926ca-b950-42de-aa57-bda01f4d1c4b"
      },
      "id": "2KlOJyVMJce5",
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mid.save(\"generated2.mid\")"
      ],
      "metadata": {
        "id": "YvNqAoEn3t5S"
      },
      "id": "YvNqAoEn3t5S",
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "play_midi(\"generated2.mid\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "s8neU9pLG6OG",
        "outputId": "4fb1cd68-80f2-4ef7-cc39-9381df767c39"
      },
      "id": "s8neU9pLG6OG",
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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