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bjorn_training

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Josh Cole commited on
Commit
ee02bc0
·
1 Parent(s): 60ab637

Test

Browse files
Files changed (8) hide show
  1. Generator.ipynb +150 -0
  2. README.md +0 -0
  3. clips/1.mp3 +0 -3
  4. clips/1.wav +1 -1
  5. 1.wav → output/dataset.arrow +2 -2
  6. output/dataset_info.json +35 -0
  7. output/state.json +14 -0
  8. test.tsv +0 -2
Generator.ipynb ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 36,
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+ "id": "bbd1b7a1-dbb7-4243-99e0-70a6cd47d573",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "bc0613fcb64f4a5e8cd4ad69698f7715",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "from huggingface_hub import notebook_login\n",
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+ "notebook_login()"
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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": 93,
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+ "id": "306958c8-4603-4b9b-b941-6a824777164d",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import librosa\n",
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+ "import math\n",
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+ "import pyarrow as pa\n",
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+ "import pandas as pd\n",
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+ "from datasets import load_dataset_builder, SplitGenerator, Split, Dataset, table"
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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": 131,
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+ "id": "4ac69d3b-38c6-49af-aefe-63755bf3f0e9",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "SAMPLE_RATE = 16_000\n",
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+ "MAX_LENGTH_IN_SECONDS = 20.0\n",
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+ "\n",
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+ "def add_audio(file, words):\n",
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+ " audio, _ = librosa.load(file, sr=SAMPLE_RATE)\n",
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+ " return {\n",
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+ " \"audio\": audio,\n",
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+ " }"
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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": 162,
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+ "id": "9192b631-388f-4306-b975-9ba770b9dc4d",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "audio, _ = librosa.load('clips/1.wav', sr=SAMPLE_RATE)\n",
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+ " \n",
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+ "df = pd.DataFrame({\n",
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+ " 'audio': audio,\n",
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+ "})\n",
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+ "tbl = table.InMemoryTable(\n",
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+ " pa.Table.from_pandas(df)\n",
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+ ")\n",
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+ "ds = Dataset(tbl)"
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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": 163,
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+ "id": "eb7979e4-c00a-4657-a1d4-b2bffd894363",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "ename": "TypeError",
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+ "evalue": "Couldn't cast array of type\nlist<item: float>\nto\nfloat",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
91
+ "Input \u001b[0;32mIn [163]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43madd_audio\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mclips/1.wav\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbjorn\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
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+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:518\u001b[0m, in \u001b[0;36mtransmit_format.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 511\u001b[0m self_format \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 512\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_type,\n\u001b[1;32m 513\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_kwargs,\n\u001b[1;32m 514\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_columns,\n\u001b[1;32m 515\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_all_columns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_all_columns,\n\u001b[1;32m 516\u001b[0m }\n\u001b[1;32m 517\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 518\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 519\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m 520\u001b[0m \u001b[38;5;66;03m# re-apply format to the output\u001b[39;00m\n",
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+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/fingerprint.py:458\u001b[0m, in \u001b[0;36mfingerprint_transform.<locals>._fingerprint.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 452\u001b[0m kwargs[fingerprint_name] \u001b[38;5;241m=\u001b[39m update_fingerprint(\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fingerprint, transform, kwargs_for_fingerprint\n\u001b[1;32m 454\u001b[0m )\n\u001b[1;32m 456\u001b[0m \u001b[38;5;66;03m# Call actual function\u001b[39;00m\n\u001b[0;32m--> 458\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 460\u001b[0m \u001b[38;5;66;03m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[39;00m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace: \u001b[38;5;66;03m# update after calling func so that the fingerprint doesn't change if the function fails\u001b[39;00m\n",
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+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:4624\u001b[0m, in \u001b[0;36mDataset.add_item\u001b[0;34m(self, item, new_fingerprint)\u001b[0m\n\u001b[1;32m 4619\u001b[0m dset_features, item_features \u001b[38;5;241m=\u001b[39m _align_features([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures, Features\u001b[38;5;241m.\u001b[39mfrom_arrow_schema(item_table\u001b[38;5;241m.\u001b[39mschema)])\n\u001b[1;32m 4620\u001b[0m \u001b[38;5;66;03m# Cast to align the schemas of the tables and concatenate the tables\u001b[39;00m\n\u001b[1;32m 4621\u001b[0m table \u001b[38;5;241m=\u001b[39m concat_tables(\n\u001b[1;32m 4622\u001b[0m [\n\u001b[1;32m 4623\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39mcast(dset_features\u001b[38;5;241m.\u001b[39marrow_schema) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures \u001b[38;5;241m!=\u001b[39m dset_features \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data,\n\u001b[0;32m-> 4624\u001b[0m \u001b[43mitem_table\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem_features\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marrow_schema\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 4625\u001b[0m ]\n\u001b[1;32m 4626\u001b[0m )\n\u001b[1;32m 4627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_indices \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4628\u001b[0m indices_table \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:834\u001b[0m, in \u001b[0;36mInMemoryTable.cast\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcast\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 822\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 823\u001b[0m \u001b[38;5;124;03m Cast table values to another schema\u001b[39;00m\n\u001b[1;32m 824\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[38;5;124;03m :class:`datasets.table.Table`:\u001b[39;00m\n\u001b[1;32m 833\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 834\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m InMemoryTable(\u001b[43mtable_cast\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n",
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+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1897\u001b[0m, in \u001b[0;36mtable_cast\u001b[0;34m(table, schema)\u001b[0m\n\u001b[1;32m 1885\u001b[0m \u001b[38;5;124;03m\"\"\"Improved version of pa.Table.cast.\u001b[39;00m\n\u001b[1;32m 1886\u001b[0m \n\u001b[1;32m 1887\u001b[0m \u001b[38;5;124;03mIt supports casting to feature types stored in the schema metadata.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1894\u001b[0m \u001b[38;5;124;03m table (:obj:`pyarrow.Table`): the casted table\u001b[39;00m\n\u001b[1;32m 1895\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m table\u001b[38;5;241m.\u001b[39mschema \u001b[38;5;241m!=\u001b[39m schema:\n\u001b[0;32m-> 1897\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcast_table_to_schema\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschema\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1898\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m table\u001b[38;5;241m.\u001b[39mschema\u001b[38;5;241m.\u001b[39mmetadata \u001b[38;5;241m!=\u001b[39m schema\u001b[38;5;241m.\u001b[39mmetadata:\n\u001b[1;32m 1899\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39mreplace_schema_metadata(schema\u001b[38;5;241m.\u001b[39mmetadata)\n",
97
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1880\u001b[0m, in \u001b[0;36mcast_table_to_schema\u001b[0;34m(table, schema)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(table\u001b[38;5;241m.\u001b[39mcolumn_names) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28msorted\u001b[39m(features):\n\u001b[1;32m 1879\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtable\u001b[38;5;241m.\u001b[39mschema\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mto\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mbecause column names don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1880\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [cast_array_to_feature(table[name], feature) \u001b[38;5;28;01mfor\u001b[39;00m name, feature \u001b[38;5;129;01min\u001b[39;00m features\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[1;32m 1881\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mTable\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, schema\u001b[38;5;241m=\u001b[39mschema)\n",
98
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1880\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(table\u001b[38;5;241m.\u001b[39mcolumn_names) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28msorted\u001b[39m(features):\n\u001b[1;32m 1879\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtable\u001b[38;5;241m.\u001b[39mschema\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mto\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mbecause column names don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1880\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [\u001b[43mcast_array_to_feature\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtable\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m name, feature \u001b[38;5;129;01min\u001b[39;00m features\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[1;32m 1881\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mTable\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, schema\u001b[38;5;241m=\u001b[39mschema)\n",
99
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1673\u001b[0m, in \u001b[0;36m_wrap_for_chunked_arrays.<locals>.wrapper\u001b[0;34m(array, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1671\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(array, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1672\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(array, pa\u001b[38;5;241m.\u001b[39mChunkedArray):\n\u001b[0;32m-> 1673\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mchunked_array([func(chunk, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m array\u001b[38;5;241m.\u001b[39mchunks])\n\u001b[1;32m 1674\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(array, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
100
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1673\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1671\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(array, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1672\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(array, pa\u001b[38;5;241m.\u001b[39mChunkedArray):\n\u001b[0;32m-> 1673\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mchunked_array([\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m array\u001b[38;5;241m.\u001b[39mchunks])\n\u001b[1;32m 1674\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(array, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
101
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1845\u001b[0m, in \u001b[0;36mcast_array_to_feature\u001b[0;34m(array, feature, allow_number_to_str)\u001b[0m\n\u001b[1;32m 1843\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array_cast(array, get_nested_type(feature), allow_number_to_str\u001b[38;5;241m=\u001b[39mallow_number_to_str)\n\u001b[1;32m 1844\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(feature, (Sequence, \u001b[38;5;28mdict\u001b[39m, \u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[0;32m-> 1845\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marray_cast\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_number_to_str\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_number_to_str\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1846\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast array of type\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00marray\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mto\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfeature\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
102
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1675\u001b[0m, in \u001b[0;36m_wrap_for_chunked_arrays.<locals>.wrapper\u001b[0;34m(array, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1673\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mchunked_array([func(chunk, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m array\u001b[38;5;241m.\u001b[39mchunks])\n\u001b[1;32m 1674\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
103
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/table.py:1755\u001b[0m, in \u001b[0;36marray_cast\u001b[0;34m(array, pa_type, allow_number_to_str)\u001b[0m\n\u001b[1;32m 1753\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast array of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00marray\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpa_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1754\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array\u001b[38;5;241m.\u001b[39mcast(pa_type)\n\u001b[0;32m-> 1755\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast array of type\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00marray\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mto\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mpa_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
104
+ "\u001b[0;31mTypeError\u001b[0m: Couldn't cast array of type\nlist<item: float>\nto\nfloat"
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