{ "cells": [ { "cell_type": "code", "execution_count": 13, "id": "fe25c190", "metadata": {}, "outputs": [], "source": [ "import json\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 12, "id": "15ae12c2", "metadata": {}, "outputs": [], "source": [ "metas = []\n", "with open(\"/data/suno/data/harvest/genius/de_project/youtube_metas.jsonl\") as f:\n", " for line in f:\n", " line = line.strip()\n", " if len(line) == 0:\n", " continue\n", " m = json.loads(line)\n", " metas.append({\n", " \"id\": m[\"id\"],\n", " \"success\": m[\"success\"],\n", " \"runtime_s\": m[\"runtime_s\"],\n", " })" ] }, { "cell_type": "code", "execution_count": 24, "id": "8503476f", "metadata": {}, "outputs": [], "source": [ "with open(\"/data/suno/data/harvest/genius/de_project/youtube_metas.jsonl\") as f:\n", " for line in f:\n", " line = line.strip()\n", " if len(line) == 0:\n", " continue\n", " m = json.loads(line)\n", " if m[\"id\"] == \"U70JIQSZkNw\":\n", " break" ] }, { "cell_type": "code", "execution_count": 30, "id": "176f8aff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found\n" ] } ], "source": [ "with open(\"/data/suno/data/harvest/genius/de_project/song_metas.jsonl\") as f:\n", " for line in f:\n", " line = line.strip()\n", " if len(line) == 0:\n", " continue\n", " m = json.loads(line)\n", " if \"U70JIQSZkNw\" in m[\"youtube_url\"]:\n", " print(\"found\")\n", " break" ] }, { "cell_type": "code", "execution_count": null, "id": "48e29246", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "639ddc44", "metadata": {}, "outputs": [], "source": [ "\n", " song_metas.append(json.loads(line))\n", "youtube_ids = [\n", " youtube_id\n", " for m in song_metas \n", " if (\n", " m[\"lang\"] == \"de\" and \n", " m[\"lang_lyrics\"] == \"de\" and \n", " m[\"youtube_start\"] == \"0\" and \n", " len((youtube_id := m[\"youtube_url\"].split(\"=\")[-1])) == 11\n", " )\n", "]\n", "print(len(youtube_ids), \"items loaded.\")\n", "# 38753 items loaded." ] }, { "cell_type": "code", "execution_count": 15, "id": "ca96c182", "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(metas)" ] }, { "cell_type": "code", "execution_count": 16, "id": "f1e93649", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id success runtime_s\n", "0 tsJuuDqHqUI True 3.9\n", "1 GRAdRvs2N5Y False 1.8\n", "2 UB0c9jakDbQ True 4.9\n", "3 MgTvwl9nr0E True 6.0\n", "4 Ny8_ICN-JSA True 4.7" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 21, "id": "5742bae6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.1" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"runtime_s\"].median()" ] }, { "cell_type": "code", "execution_count": 19, "id": "ed9d3050", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "291.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"runtime_s\"].max()" ] }, { "cell_type": "code", "execution_count": 20, "id": "339b232c", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df[\"runtime_s\"] >= 150][\"runtime_s\"].hist()" ] }, { "cell_type": "code", "execution_count": 18, "id": "0e641601", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"runtime_s\"].hist(bins=50)" ] }, { "cell_type": "code", "execution_count": 22, "id": "155ff1cf", "metadata": {}, "outputs": [], "source": [ "_df = df[df[\"runtime_s\"] >= 100]" ] }, { "cell_type": "code", "execution_count": 23, "id": "da507078", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idsuccessruntime_s
11892U70JIQSZkNwTrue192.2
119012C-98kSAnvATrue136.4
16976y3YTvuUKv1kTrue137.3
17223JtPVUxsMkE8True110.6
20699UBtXUelpE08True291.0
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" ], "text/plain": [ " id success runtime_s\n", "11892 U70JIQSZkNw True 192.2\n", "11901 2C-98kSAnvA True 136.4\n", "16976 y3YTvuUKv1k True 137.3\n", "17223 JtPVUxsMkE8 True 110.6\n", "20699 UBtXUelpE08 True 291.0\n", "21251 0W5bnuKUFSA True 204.7" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_df" ] }, { "cell_type": "code", "execution_count": null, "id": "398e0837", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "e26f55f2", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "id": "3c281d4e", "metadata": {}, "outputs": [], "source": [ "# with open(os.path.join(\"/data/suno/data/harvest/genius\", \"de_project\", \"song_metas.jsonl\")) as f:\n", "# for line in f:\n", "# line = line.strip()\n", "# if len(line) == 0:\n", "# continue\n", "# m = json.loads(line)\n", "# if m[\"youtube_url\"].endswith(\"9Ew461CAlmQ\"):\n", "# print(m)\n", "# break" ] }, { "cell_type": "code", "execution_count": null, "id": "f0c6d69a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f14c96e8", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "c8652d23", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "68d6f01d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }