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Changes to be committed: 뉴 토크나이저 적용해 재 전처리

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modified: DIVA_dataset.pt
modified: preprocessing.ipynb
modified: tokenized_midi_data.json

Files changed (3) hide show
  1. DIVA_dataset.pt +2 -2
  2. preprocessing.ipynb +80 -62
  3. tokenized_midi_data.json +909 -850
DIVA_dataset.pt CHANGED
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preprocessing.ipynb CHANGED
@@ -156,7 +156,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 5,
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  "id": "21a9ba89",
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  "metadata": {},
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  "outputs": [],
@@ -169,7 +169,7 @@
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  },
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  "metadata": {},
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  "outputs": [],
@@ -277,23 +277,10 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": null,
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  "id": "8cba97a6",
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  "metadata": {},
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- "outputs": [
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- {
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- "ename": "ValueError",
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- "evalue": "min() arg is an empty sequence",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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- "\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
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- "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 7\u001b[39m MIDI = Tokenizer()\n\u001b[32m 8\u001b[39m MIDI.set_midi(os.path.join(\u001b[33m\"\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m\"\u001b[39m, filename))\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m item[\u001b[33m'\u001b[39m\u001b[33mtoken\u001b[39m\u001b[33m'\u001b[39m] = \u001b[43mMIDI\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtoken_id\u001b[49m\n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# try:\u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# except:\u001b[39;00m\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# print(f\"{item['name']} 을 토크나이즈 하는데 실패함.\")\u001b[39;00m\n",
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- "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\rrayy\\anaconda3\\envs\\diva\\Lib\\site-packages\\HarmonyMIDIToken\\tokenizer.py:164\u001b[39m, in \u001b[36mHarmonyMIDIToken.token_id\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 161\u001b[39m durs = \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m([m_dur, c_dur, b_dur]))\n\u001b[32m 162\u001b[39m durs = [item \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m durs \u001b[38;5;28;01mif\u001b[39;00m item != \u001b[32m0\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m164\u001b[39m main_time += \u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdurs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 166\u001b[39m melody_time += m_dur\n\u001b[32m 167\u001b[39m chord_time += c_dur\n",
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- "\u001b[31mValueError\u001b[39m: min() arg is an empty sequence"
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- ]
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- }
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- ],
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  "source": [
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  "from HarmonyMIDIToken import HarmonyMIDIToken as Tokenizer\n",
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  "import os\n",
@@ -318,9 +305,22 @@
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  "## 데이터 전처리"
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  ]
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  },
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "cell_type": "code",
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- "execution_count": 9,
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  "id": "f7b77c0c",
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  "metadata": {},
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  "outputs": [
@@ -328,20 +328,67 @@
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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0, 1, 5, 0, 1], [82, 2, 0, 1, 0, 56, 2], [82, 2, 0, 1, 0, 56, 1], [80, 2, 60, 2, 2, 0, 1], [0, 0, 0, 1, 0, 51, 2], [0, 2, 0, 1, 2, 0, 2], [80, 1, 60, 2, 1, 51, 1], [0, 1, 0, 1, 3, 0, 1], [87, 1, 0, 1, 0, 51, 1], [0, 1, 60, 2, 3, 0, 1], [87, 3, 0, 1, 0, 51, 3], [84, 2, 60, 2, 2, 51, 2], [82, 2, 60, 2, 2, 51, 2]], [[80, 2, 64, 1, 2, 52, 4], [0, 5, 0, 1, 5, 0, 0], [80, 1, 64, 1, 1, 0, 1], [0, 1, 0, 1, 4, 52, 1], [80, 1, 0, 1, 0, 52, 1], [0, 2, 0, 1, 0, 52, 1], [0, 0, 0, 1, 0, 0, 1], [73, 2, 64, 1, 2, 52, 3], [75, 2, 64, 1, 2, 52, 2], [80, 2, 64, 1, 2, 52, 2], [0, 5, 0, 1, 5, 52, 4], [0, 0, 0, 1, 0, 0, 1], [80, 1, 64, 1, 1, 52, 1], [0, 1, 0, 1, 1, 52, 1], [80, 1, 64, 1, 1, 52, 1], [0, 2, 0, 1, 2, 0, 1], [0, 0, 0, 1, 0, 52, 3], [73, 2, 64, 1, 2, 52, 2], [75, 2, 64, 1, 2, 52, 2], [83, 2, 68, 2, 2, 56, 4], [0, 5, 0, 1, 5, 0, 1], [0, 0, 0, 1, 0, 56, 1], [0, 0, 0, 1, 0, 56, 1], [83, 1, 68, 2, 1, 56, 1], [0, 1, 0, 1, 1, 0, -1], [0, 0, 0, 1, 0, 56, 1], [0, 0, 0, 1, 0, 0, 1], [83, 1, 68, 2, 2, 56, 2], [0, 2, 0, 1, 0, 0, 0], [75, 2, 0, 1, 1, 0, 1], [0, 0, 68, 2, 2, 56, 2], [78, 2, 68, 2, 2, 56, 2], [83, 2, 66, 1, 2, 54, 4], [0, 5, 0, 1, 5, 0, 0], [83, 1, 66, 1, 1, 0, 1], [0, 1, 0, 1, 1, 54, 1], [83, 1, 66, 1, 1, 54, 1], [0, 2, 0, 1, 2, 54, 1], [85, 2, 66, 1, 2, 0, 1], [0, 0, 0, 1, 0, 54, 2], [87, 2, 66, 1, 2, 0, 0], [87, 2, 66, 1, 2, 54, 2], [87, 2, 66, 1, 2, 54, 2]], [[78, 2, 66, 1, 4, 54, 4], [0, 2, 0, 1, 0, 0, 0], [78, 2, 66, 1, 2, 54, 2], [75, 2, 0, 1, 2, 0, 2], [78, 2, 66, 1, 4, 49, 4], [80, 2, 0, 1, 0, 0, 0], [82, 2, 66, 1, 2, 49, 2], [82, 2, 0, 1, 2, 0, 2], [0, 2, 63, 2, 4, 51, 4], [78, 2, 0, 1, 0, 0, 0], [78, 2, 63, 2, 2, 51, 2], [75, 2, 0, 1, 2, 0, 2], [78, 2, 71, 1, 4, 47, 4], [80, 2, 0, 1, 0, 0, 0], [82, 2, 71, 1, 2, 47, 2], [82, 2, 0, 1, 2, 0, 2], [0, 2, 66, 1, 4, 54, 4], [78, 2, 0, 1, 0, 0, 0], [78, 2, 66, 1, 2, 54, 2], [75, 2, 0, 1, 2, 0, 2], [78, 2, 66, 1, 4, 49, 4], [75, 2, 0, 1, 0, 0, 0], [73, 2, 66, 1, 2, 49, 2], [83, 2, 0, 1, 2, 0, 2], [0, 2, 71, 1, 4, 47, 4], [83, 2, 0, 1, 0, 0, 0], [82, 2, 71, 1, 2, 47, 2], [80, 2, 0, 1, 2, 0, 2], [0, 2, 66, 1, 4, 54, 4], [80, 2, 0, 1, 0, 0, 0], [78, 2, 66, 1, 2, 54, 2]], [[82, 2, 70, 1, 4, 46, 4], [0, 4, 0, 1, 0, 0, 0], [77, 2, 0, 1, 2, 46, 2], [84, 2, 70, 1, 2, 0, 2], [0, 2, 70, 1, 2, 46, 4], [84, 2, 0, 1, 2, 46, 2], [0, 1, 70, 1, 2, 0, 1], [82, 1, 0, 1, 1, 53, 1], [82, 1, 70, 8, 1, 53, 4], [0, 5, 70, 8, 4, 0, 0], [0, 0, 0, 1, 2, 53, 2], [77, 2, 70, 8, 2, 0, 2], [84, 2, 70, 8, 2, 53, 4], [0, 2, 0, 1, 2, 0, 0], [84, 2, 70, 8, 2, 53, 2], [0, 1, 0, 1, 1, 0, 1], [86, 1, 67, 2, 1, 55, 1], [86, 4, 67, 2, 4, 55, 4], [0, 2, 0, 1, 2, 55, 2], [86, 2, 67, 2, 2, 0, 2], [84, 4, 67, 2, 2, 55, 4], [0, 0, 0, 1, 2, 0, 0], [81, 2, 67, 2, 2, 55, 2], [0, 1, 0, 1, 1, 0, 1], [82, 1, 63, 1, 1, 51, 1], [82, 4, 63, 1, 4, 51, 4], [0, 2, 0, 1, 2, 51, 2], [77, 2, 63, 1, 2, 0, 2], [82, 4, 70, 8, 2, 53, 4], [0, 0, 0, 1, 2, 0, 0], [84, 2, 70, 8, 2, 53, 2], [82, 2, 70, 8, 2, 53, 2]], [[80, 2, 68, 2, 2, 56, 2], [0, 2, 0, 1, 2, 0, 2], [80, 2, 68, 2, 2, 56, 2], [78, 2, 68, 2, 2, 56, 2], [80, 2, 68, 2, 2, 56, 2], [0, 2, 0, 1, 2, 0, 2], [83, 2, 68, 2, 2, 56, 2], [0, 1, 0, 1, 1, 0, 1], [82, 1, 63, 2, 1, 54, 1], [82, 1, 63, 2, 1, 54, 1], [0, 3, 0, 1, 3, 0, 3], [80, 2, 63, 2, 2, 54, 2], [78, 2, 63, 2, 2, 54, 2], [80, 1, 63, 2, 1, 54, 1], [0, 1, 0, 1, 1, 0, 1], [83, 1, 63, 2, 1, 54, 1], [82, 2, 63, 2, 2, 54, 2], [78, 2, 63, 2, 2, 54, 2], [80, 1, 68, 2, 1, 56, 1], [80, 1, 68, 2, 1, 56, 1], [0, 3, 0, 1, 3, 0, 3], [80, 2, 68, 2, 2, 56, 2], [78, 2, 68, 2, 2, 56, 2], [80, 1, 68, 2, 1, 56, 1], [0, 1, 0, 1, 1, 0, 1], [83, 1, 68, 2, 1, 56, 1], [82, 2, 68, 2, 2, 56, 2], [78, 2, 68, 2, 2, 56, 2], [75, 1, 63, 2, 1, 51, 1], [75, 1, 63, 2, 1, 51, 1], [0, 3, 0, 1, 3, 0, 3], [75, 2, 63, 2, 2, 51, 2], [73, 2, 63, 2, 2, 51, 2], [73, 1, 63, 5, 1, 54, 1], [0, 1, 0, 1, 1, 0, 1], [78, 1, 63, 5, 1, 54, 1], [82, 1, 63, 5, 1, 54, 1], [0, 1, 0, 1, 1, 0, 1], [78, 2, 63, 5, 2, 54, 2]], [[74, 2, 62, 2, 2, 50, 2], [0, 2, 0, 1, 2, 0, 2], [76, 2, 62, 2, 2, 50, 2], [77, 2, 62, 2, 2, 50, 2], [77, 1, 65, 1, 1, 53, 1], [0, 1, 0, 1, 1, 0, 1], [74, 1, 65, 1, 1, 53, 1], [81, 1, 65, 1, 1, 53, 1], [0, 1, 0, 1, 1, 0, 1], [74, 2, 65, 1, 2, 53, 2], [0, 1, 0, 1, 1, 0, 1], [74, 2, 62, 2, 2, 57, 2], [0, 2, 0, 1, 2, 0, 2], [76, 2, 62, 2, 2, 57, 2], [77, 2, 62, 2, 2, 57, 2], [77, 1, 67, 2, 1, 55, 1], [0, 1, 0, 1, 1, 0, 1], [74, 1, 67, 2, 1, 55, 1], [84, 1, 67, 2, 1, 55, 1], [0, 1, 0, 1, 1, 0, 1], [79, 2, 67, 2, 2, 55, 2], [0, 1, 0, 1, 1, 0, 1], [81, 2, 62, 2, 2, 50, 2], [0, 14, 0, 1, 14, 0, -2], [0, 0, 0, 1, 0, 50, 2], [0, 0, 0, 1, 0, 0, 2], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [81, 2, 62, 2, 2, 50, 2], [81, 2, 62, 2, 2, 0, 2], [81, 2, 62, 2, 2, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1], [0, 0, 0, 1, 0, 50, 1]], [[0, 4, 0, 1, 4, 53, 4], [91, 2, 0, 1, 2, 53, 2], [0, 2, 0, 1, 2, 0, 2], [91, 2, 0, 1, 2, 53, 2], [84, 2, 0, 1, 2, 53, 2], [0, 2, 0, 1, 2, 0, 2], [84, 2, 0, 1, 2, 53, 2], [0, 4, 60, 1, 4, 57, 4], [91, 2, 60, 1, 2, 57, 2], [0, 2, 0, 1, 2, 0, 2], [91, 2, 60, 1, 2, 57, 2], [84, 2, 60, 1, 2, 57, 2], [0, 2, 0, 1, 2, 0, 2], [88, 2, 60, 1, 2, 57, 2], [0, 4, 0, 1, 4, 55, 4], [91, 2, 0, 1, 2, 55, 2], [0, 2, 0, 1, 2, 0, 2], [91, 2, 0, 1, 2, 55, 2], [84, 2, 0, 1, 2, 55, 2], [0, 2, 0, 1, 2, 0, 2], [83, 2, 0, 1, 2, 55, 2], [0, 4, 60, 1, 4, 52, 4], [88, 2, 60, 1, 2, 52, 2], [0, 2, 0, 1, 2, 0, 2], [86, 2, 60, 1, 2, 52, 2], [83, 2, 60, 1, 2, 52, 2], [0, 2, 0, 1, 2, 0, 2], [84, 2, 60, 1, 2, 52, 2]], [[75, 2, 68, 1, 2, 51, 2], [0, 2, 0, 1, 2, 0, 2], [84, 2, 68, 1, 2, 51, 2], [0, 1, 0, 1, 1, 0, 1], [82, 2, 68, 1, 2, 51, 2], [0, 3, 0, 1, 3, 0, 3], [82, 2, 68, 1, 2, 51, 2], [75, 2, 68, 1, 2, 51, 2], [75, 2, 63, 1, 2, 46, 2], [0, 2, 0, 1, 2, 0, 2], [84, 2, 63, 1, 2, 46, 2], [0, 1, 0, 1, 1, 0, 1], [82, 2, 63, 1, 2, 46, 2], [0, 1, 0, 1, 1, 0, 1], [82, 2, 63, 1, 2, 46, 2], [80, 2, 63, 1, 2, 46, 2], [79, 2, 63, 1, 2, 46, 2], [79, 2, 60, 2, 2, 48, 2], [0, 2, 0, 1, 2, 0, 2], [79, 2, 60, 2, 2, 48, 2], [0, 1, 0, 1, 1, 0, 1], [79, 2, 60, 2, 2, 48, 2], [0, 3, 0, 1, 3, 0, 3], [82, 2, 60, 2, 2, 48, 2], [75, 2, 60, 2, 2, 48, 2], [79, 2, 70, 1, 2, 50, 2], [0, 2, 0, 1, 2, 0, 2], [79, 2, 70, 1, 2, 50, 2], [0, 1, 0, 1, 1, 0, 1], [75, 2, 70, 1, 2, 50, 2], [0, 1, 0, 1, 1, 0, 1], [82, 2, 70, 1, 2, 50, 2], [74, 2, 70, 1, 2, 50, 2], [75, 2, 70, 1, 2, 50, 2]]]\n"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
332
  ]
333
  }
334
  ],
335
  "source": [
336
  "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n",
337
  "from sklearn.compose import ColumnTransformer\n",
 
338
  "import torch\n",
339
  "import pandas as pd\n",
340
  "\n",
341
  "vector_df = pd.DataFrame([item['vector'] for item in tokenized_data])\n",
342
  "\n",
343
- "tokens = [item['token'] for item in tokenized_data]\n",
344
- "\n",
345
  "# 전처리 파이프라인\n",
346
  "preprocessor = ColumnTransformer([\n",
347
  " (\"cat\", OneHotEncoder(sparse_output=False), [\"mode\", \"mood\", \"key\"]),\n",
@@ -350,22 +397,20 @@
350
  "\n",
351
  "X = preprocessor.fit_transform(vector_df)\n",
352
  "\n",
353
- "# # 토큰 시퀀스 패딩 (-1으로 뒤에 채우기)\n",
354
- "# max_len = max(len(seq) for seq in tokens)\n",
355
- "# padded_tokens = [seq + [-1]*(max_len - len(seq)) for seq in tokens]\n",
356
  "\n",
357
- "# # 각 샘플의 실제 길이\n",
358
- "# lengths = torch.tensor([len(seq) for seq in tokens], dtype=torch.long)\n",
359
  "\n",
360
- "# Tensor 변환\n",
361
- "#X_tensor = torch.tensor(X, dtype=torch.float32)\n",
362
- "#Y_tensor = torch.tensor(padded_tokens, dtype=torch.long)\n",
363
- "print(tokens)"
364
  ]
365
  },
366
  {
367
  "cell_type": "code",
368
- "execution_count": 5,
369
  "id": "dd840788",
370
  "metadata": {},
371
  "outputs": [
@@ -374,45 +419,18 @@
374
  "output_type": "stream",
375
  "text": [
376
  "X shape: torch.Size([34, 25])\n",
377
- "X sample: tensor([1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000, 0.0000,\n",
378
- " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000,\n",
379
- " 0.0000, 0.0000, 0.0000, 1.0000, 0.1667, 1.0000, 0.1186])\n"
380
  ]
381
  }
382
  ],
383
  "source": [
384
  "print(\"X shape:\", X_tensor.shape)\n",
385
- "print(\"X sample:\", X_tensor[0])"
386
- ]
387
- },
388
- {
389
- "cell_type": "code",
390
- "execution_count": 6,
391
- "id": "769af33a",
392
- "metadata": {},
393
- "outputs": [
394
- {
395
- "name": "stdout",
396
- "output_type": "stream",
397
- "text": [
398
- "min target: 0\n",
399
- "unique values: tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16,\n",
400
- " 22, 31, 35, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n",
401
- " 53, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68,\n",
402
- " 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83,\n",
403
- " 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 100,\n",
404
- " 102, 130, 202, 302])\n"
405
- ]
406
- }
407
- ],
408
- "source": [
409
- "print(\"min target:\", Y_tensor.min().item())\n",
410
- "print(\"unique values:\", torch.unique(Y_tensor))"
411
  ]
412
  },
413
  {
414
  "cell_type": "code",
415
- "execution_count": 9,
416
  "id": "4f5f5dc1",
417
  "metadata": {},
418
  "outputs": [],
@@ -422,7 +440,7 @@
422
  "torch.save({\n",
423
  " \"X\": X_tensor,\n",
424
  " \"Y\": Y_tensor,\n",
425
- " \"lengths\": lengths\n",
426
  "}, \"DIVA_dataset.pt\")"
427
  ]
428
  },
 
156
  },
157
  {
158
  "cell_type": "code",
159
+ "execution_count": 3,
160
  "id": "21a9ba89",
161
  "metadata": {},
162
  "outputs": [],
 
169
  },
170
  {
171
  "cell_type": "code",
172
+ "execution_count": 1,
173
  "id": "dea532d4",
174
  "metadata": {},
175
  "outputs": [],
 
277
  },
278
  {
279
  "cell_type": "code",
280
+ "execution_count": 2,
281
  "id": "8cba97a6",
282
  "metadata": {},
283
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
284
  "source": [
285
  "from HarmonyMIDIToken import HarmonyMIDIToken as Tokenizer\n",
286
  "import os\n",
 
305
  "## 데이터 전처리"
306
  ]
307
  },
308
+ {
309
+ "cell_type": "markdown",
310
+ "id": "7e802a13",
311
+ "metadata": {},
312
+ "source": [
313
+ "## 📬 요약: 지금까지 한 대화\n",
314
+ "\n",
315
+ "1. 너는 처음에 벡터 → 시퀀스 구조를 **LSTM 기반 PyTorch 모델**로 구현하려고 했고,\n",
316
+ "2. 나는 autoregressive 구조, teacher forcing, packing 등을 포함한 버전을 설명했어.\n",
317
+ "3. 이후 너가 `Vector2MIDI`라는 실질적인 구현 코드를 공유했고,\n",
318
+ "4. 나는 그 코드를 리뷰하고, 우리가 나눈 개념을 반영해서 리팩토링한 버전을 다시 작성해줬어.\n"
319
+ ]
320
+ },
321
  {
322
  "cell_type": "code",
323
+ "execution_count": 2,
324
  "id": "f7b77c0c",
325
  "metadata": {},
326
  "outputs": [
 
328
  "name": "stdout",
329
  "output_type": "stream",
330
  "text": [
331
+ "tensor([[[81, 3, 65, ..., 3, 53, 3],\n",
332
+ " [ 0, 1, 0, ..., 1, 0, 1],\n",
333
+ " [81, 2, 65, ..., 2, 53, 2],\n",
334
+ " ...,\n",
335
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
336
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
337
+ " [-1, -1, -1, ..., -1, -1, -1]],\n",
338
+ "\n",
339
+ " [[77, 2, 65, ..., 2, 53, 2],\n",
340
+ " [ 0, 2, 0, ..., 2, 0, 2],\n",
341
+ " [89, 1, 65, ..., 1, 53, 1],\n",
342
+ " ...,\n",
343
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
344
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
345
+ " [-1, -1, -1, ..., -1, -1, -1]],\n",
346
+ "\n",
347
+ " [[78, 2, 63, ..., 2, 51, 2],\n",
348
+ " [ 0, 2, 0, ..., 2, 0, 2],\n",
349
+ " [78, 1, 63, ..., 1, 51, 2],\n",
350
+ " ...,\n",
351
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
352
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
353
+ " [-1, -1, -1, ..., -1, -1, -1]],\n",
354
+ "\n",
355
+ " ...,\n",
356
+ "\n",
357
+ " [[74, 2, 62, ..., 2, 50, 2],\n",
358
+ " [ 0, 2, 0, ..., 2, 0, 2],\n",
359
+ " [76, 2, 62, ..., 2, 50, 2],\n",
360
+ " ...,\n",
361
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
362
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
363
+ " [-1, -1, -1, ..., -1, -1, -1]],\n",
364
+ "\n",
365
+ " [[ 0, 4, 0, ..., 4, 53, 4],\n",
366
+ " [91, 2, 0, ..., 2, 53, 2],\n",
367
+ " [ 0, 2, 0, ..., 2, 0, 2],\n",
368
+ " ...,\n",
369
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
370
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
371
+ " [-1, -1, -1, ..., -1, -1, -1]],\n",
372
+ "\n",
373
+ " [[75, 2, 68, ..., 2, 51, 2],\n",
374
+ " [ 0, 2, 0, ..., 2, 0, 2],\n",
375
+ " [84, 2, 68, ..., 2, 51, 2],\n",
376
+ " ...,\n",
377
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
378
+ " [-1, -1, -1, ..., -1, -1, -1],\n",
379
+ " [-1, -1, -1, ..., -1, -1, -1]]])\n"
380
  ]
381
  }
382
  ],
383
  "source": [
384
  "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n",
385
  "from sklearn.compose import ColumnTransformer\n",
386
+ "from torch.nn.utils.rnn import pad_sequence\n",
387
  "import torch\n",
388
  "import pandas as pd\n",
389
  "\n",
390
  "vector_df = pd.DataFrame([item['vector'] for item in tokenized_data])\n",
391
  "\n",
 
 
392
  "# 전처리 파이프라인\n",
393
  "preprocessor = ColumnTransformer([\n",
394
  " (\"cat\", OneHotEncoder(sparse_output=False), [\"mode\", \"mood\", \"key\"]),\n",
 
397
  "\n",
398
  "X = preprocessor.fit_transform(vector_df)\n",
399
  "\n",
400
+ "#Tensor 변환\n",
401
+ "X_tensor = torch.tensor(X, dtype=torch.float32)\n",
402
+ "Y_tensor = [torch.tensor(item['token'], dtype=torch.long) for item in tokenized_data]\n",
403
  "\n",
404
+ "seq_lengths = [len(seq) for seq in Y_tensor]\n",
 
405
  "\n",
406
+ "# 패딩 처리\n",
407
+ "padded_Y = pad_sequence(Y_tensor, batch_first=True, padding_value=-1) # (batch_size, max_len, 7)\n",
408
+ "print(padded_Y)"
 
409
  ]
410
  },
411
  {
412
  "cell_type": "code",
413
+ "execution_count": 3,
414
  "id": "dd840788",
415
  "metadata": {},
416
  "outputs": [
 
419
  "output_type": "stream",
420
  "text": [
421
  "X shape: torch.Size([34, 25])\n",
422
+ "Y shape: torch.Size([34, 125, 7])\n"
 
 
423
  ]
424
  }
425
  ],
426
  "source": [
427
  "print(\"X shape:\", X_tensor.shape)\n",
428
+ "print(\"Y shape:\", padded_Y.shape)"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
429
  ]
430
  },
431
  {
432
  "cell_type": "code",
433
+ "execution_count": 4,
434
  "id": "4f5f5dc1",
435
  "metadata": {},
436
  "outputs": [],
 
440
  "torch.save({\n",
441
  " \"X\": X_tensor,\n",
442
  " \"Y\": Y_tensor,\n",
443
+ " \"lengths\": seq_lengths\n",
444
  "}, \"DIVA_dataset.pt\")"
445
  ]
446
  },
tokenized_midi_data.json CHANGED
@@ -2565,856 +2565,915 @@
2565
  "mood": "Aggressive"
2566
  },
2567
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