147 lines
4.0 KiB
Plaintext
147 lines
4.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "24915fba-1f44-46af-9233-66b896d7fa41",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"with open(\"ucs-community/json/en.json\") as f:\n",
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" ucs = json.load(f)\n",
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" cat_ids = [x['CatID'] for x in ucs]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3a4b887d-5e72-4136-a7d9-650667619b12",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ucs_catid(path: str) -> Optional[str]:\n",
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" import os.path\n",
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" 'True if the file at `path` has a valid UCS filename'\n",
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"\n",
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" basename = os.path.basename(path)\n",
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" first_component = basename.split(\"_\")[0]\n",
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"\n",
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" if first_component in cat_ids:\n",
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" return first_component\n",
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" else:\n",
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" return False"
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]
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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": "faedeeb7-8c4d-4e60-ab7a-9baf9add008a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Optional\n",
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"\n",
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"def description(path: str) -> Optional[str]:\n",
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" import json, subprocess\n",
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" result = subprocess.run(['ffprobe', '-show_format', '-of', 'json', path], capture_output=True)\n",
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" try:\n",
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" result.check_returncode()\n",
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" except:\n",
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" return None\n",
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" \n",
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" stream = json.loads(result.stdout)\n",
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" fmt = stream.get(\"format\", None)\n",
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" if fmt:\n",
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" tags = fmt.get(\"tags\", None)\n",
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" if tags:\n",
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" return tags.get(\"comment\", None)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "f57c7c75-8cda-441a-bfb3-179e0afde861",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Optional, Tuple\n",
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"\n",
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"def test_data_for_file(path: str) -> Optional[Tuple[str, str]]:\n",
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" 'CatID and description if both are present'\n",
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"\n",
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" catid = ucs_catid(path)\n",
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" if catid is None:\n",
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" return None\n",
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" \n",
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" desc = description(path)\n",
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"\n",
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" if desc is not None:\n",
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" return (catid, desc)\n",
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" else:\n",
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" return None\n",
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"\n",
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"def collect_dataset(scan_root: str, set_name: str):\n",
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" \"\"\"\n",
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" Scans scan_root recursively and collects all catid/description pairs\n",
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" it can find.\n",
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" \"\"\"\n",
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" import os, csv\n",
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" test_data = []\n",
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" for root, _, files in os.walk(scan_root):\n",
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" for file in files:\n",
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" if file.endswith(\".wav\") or file.endswith(\".flac\"):\n",
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" if test_datum := test_data_for_file(os.path.join(root,file)):\n",
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" test_data += [test_datum]\n",
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"\n",
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" with open(set_name + '.csv', 'w') as f:\n",
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" writer = csv.writer(f)\n",
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" writer.writerow(['Category', 'Description'])\n",
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" for row in test_data:\n",
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" writer.writerow(row)\n",
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "1e05629d-15a5-406b-8064-879900e4b3c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"collect_dataset(\"/Volumes/NAS SFX Library/JAMIELIB Libraries by Studio/_Designers/Jamie Hardt\",\"jamie_files\")"
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]
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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": "7340f734-5ba7-4db0-a012-9e2bd46a4fc5",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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