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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating the NENA Speech Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download validated examples from Pocketbase"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pocketbase import PocketBase\n",
"\n",
"pb = PocketBase('https://pocketbase.nenadb.dev/')\n",
"\n",
"dialects = pb.collection(\"dialects\").get_full_list(query_params={\n",
" \"sort\": \"name\",\n",
"})\n",
"\n",
"examples = pb.collection(\"examples\").get_full_list(query_params={\n",
" \"expand\": \"dialect\",\n",
" \"filter\": \"validated=true\",\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create shards"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pydub import AudioSegment\n",
"import requests\n",
"import tempfile\n",
"\n",
"test_split = 0.10\n",
"dev_split = 0.10\n",
"\n",
"for i, example in enumerate(examples):\n",
" prog = i / len(examples)\n",
" \n",
" if prog < test_split:\n",
" split = 'test'\n",
" elif prog < dev_split + test_split:\n",
" split = 'dev'\n",
" else:\n",
" split = 'train'\n",
"\n",
" audio_url = pb.get_file_url(example, example.speech, {})\n",
" response = requests.get(audio_url)\n",
"\n",
" with tempfile.NamedTemporaryFile() as f:\n",
" f.write(response.content)\n",
" f.flush()\n",
" audio = AudioSegment.from_file(f.name)\n",
"\n",
" audio = audio.set_frame_rate(48000)\n",
" audio.export(f\"nena_speech_{example.id}.mp3\", format=\"mp3\")\n",
"\n",
" break"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.11.5"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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