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Browse files- Generate_metadata.ipynb +0 -883
Generate_metadata.ipynb
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"## Generate the datasets for uploading"
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"CommitInfo(commit_url='https://huggingface.co/datasets/ivangtorre/second_americas_nlp_2022/commit/edd8ca4dc1e477443d98f7eace86ee02daf62347', commit_message='Upload dataset', commit_description='', oid='edd8ca4dc1e477443d98f7eace86ee02daf62347', pr_url=None, pr_revision=None, pr_num=None)"
|
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-
},
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| 663 |
-
"execution_count": 21,
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| 664 |
-
"metadata": {},
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| 665 |
-
"output_type": "execute_result"
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| 666 |
-
}
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| 667 |
-
],
|
| 668 |
-
"source": [
|
| 669 |
-
"import pandas as pd\n",
|
| 670 |
-
"from datasets import Dataset, Audio\n",
|
| 671 |
-
"\n",
|
| 672 |
-
"def flatten(xss):\n",
|
| 673 |
-
" return [x for xs in xss for x in xs]\n",
|
| 674 |
-
"\n",
|
| 675 |
-
"def create_dataset(df):\n",
|
| 676 |
-
" audio_dataset = Dataset.from_dict({\"audio\": flatten(df[\"file_name\"].values.tolist()),\n",
|
| 677 |
-
" \"subset\": flatten(df[\"subset\"].values.tolist()),\n",
|
| 678 |
-
" \"source_processed\": flatten(df[\"source_processed\"].values.tolist()),\n",
|
| 679 |
-
" \"source_raw\": flatten(df[\"source_raw\"].values.tolist()),\n",
|
| 680 |
-
" \"target_raw\": flatten(df[\"target_raw\"].values.tolist()),\n",
|
| 681 |
-
" \"split\": flatten(df[\"split\"].values.tolist()),\n",
|
| 682 |
-
" },\n",
|
| 683 |
-
" ).cast_column(\"audio\", Audio())\n",
|
| 684 |
-
" return(audio_dataset)\n",
|
| 685 |
-
"\n",
|
| 686 |
-
"\n",
|
| 687 |
-
"def generate_df(language, split):\n",
|
| 688 |
-
" # QUECHUA TRAIN\n",
|
| 689 |
-
" with open(\"./../\"+language +\"_\"+split+\".tsv\") as f:\n",
|
| 690 |
-
" lines = f.read().splitlines()\n",
|
| 691 |
-
" lines2 = [l.split(\"\\t\") for l in lines if len(l.split(\"\\t\"))==4]\n",
|
| 692 |
-
" asd = [l.split(\"\\t\")[0] for l in lines if len(l.split(\"\\t\"))>4]\n",
|
| 693 |
-
" print(asd)\n",
|
| 694 |
-
" df1 = pd.DataFrame(lines2[1::], columns =lines2[0:1])\n",
|
| 695 |
-
" df1 = df1.assign(split=[split]*df1.shape[0])\n",
|
| 696 |
-
" df1 = df1.assign(subset=[language]*df1.shape[0])\n",
|
| 697 |
-
" df1 = df1.rename(columns={'wav': 'file_name'})\n",
|
| 698 |
-
" df1['file_name'] = 'data/' + language + '/' + split +'/' + df1['file_name'].astype(str)\n",
|
| 699 |
-
" audio_dataset = create_dataset(df)\n",
|
| 700 |
-
" return audio_dataset\n",
|
| 701 |
-
"\n",
|
| 702 |
-
"\n",
|
| 703 |
-
"\n",
|
| 704 |
-
"audio_dataset = generate_df(\"quechua\", \"train\")\n",
|
| 705 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"quechua\", split=\"train\")\n",
|
| 706 |
-
"audio_dataset = generate_df(\"quechua\", \"dev\")\n",
|
| 707 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"quechua\", split=\"dev\")\n",
|
| 708 |
-
"\n",
|
| 709 |
-
"audio_dataset = generate_df(\"guarani\", \"train\")\n",
|
| 710 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"guarani\", split=\"train\")\n",
|
| 711 |
-
"audio_dataset = generate_df(\"guarani\", \"dev\")\n",
|
| 712 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"guarani\", split=\"dev\")\n",
|
| 713 |
-
"\n",
|
| 714 |
-
"audio_dataset = generate_df(\"kotiria\", \"dev\")\n",
|
| 715 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"kotiria\", split=\"train\")\n",
|
| 716 |
-
"audio_dataset = generate_df(\"kotiria\", \"dev\")\n",
|
| 717 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"kotiria\", split=\"dev\")\n",
|
| 718 |
-
"\n",
|
| 719 |
-
"audio_dataset = generate_df(\"bribri\", \"train\")\n",
|
| 720 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"bribri\", split=\"train\")\n",
|
| 721 |
-
"audio_dataset = generate_df(\"bribri\", \"dev\")\n",
|
| 722 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"bribri\", split=\"dev\")\n",
|
| 723 |
-
"\n",
|
| 724 |
-
"audio_dataset = generate_df(\"waikhana\", \"dev\")\n",
|
| 725 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"waikhana\", split=\"train\")\n",
|
| 726 |
-
"audio_dataset = generate_df(\"waikhana\", \"dev\")\n",
|
| 727 |
-
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", \"waikhana\", split=\"dev\")\n"
|
| 728 |
-
]
|
| 729 |
-
},
|
| 730 |
-
{
|
| 731 |
-
"cell_type": "code",
|
| 732 |
-
"execution_count": 2,
|
| 733 |
-
"id": "a1f02703",
|
| 734 |
-
"metadata": {
|
| 735 |
-
"scrolled": true
|
| 736 |
-
},
|
| 737 |
-
"outputs": [],
|
| 738 |
-
"source": [
|
| 739 |
-
"#from datasets import load_dataset\n",
|
| 740 |
-
"#dataset = load_dataset(\"audiofolder\", data_dir=\"second_americas_nlp_2022\")\n"
|
| 741 |
-
]
|
| 742 |
-
},
|
| 743 |
-
{
|
| 744 |
-
"cell_type": "markdown",
|
| 745 |
-
"id": "5eaa7c93",
|
| 746 |
-
"metadata": {},
|
| 747 |
-
"source": [
|
| 748 |
-
"# EVALUATE MODELS\n"
|
| 749 |
-
]
|
| 750 |
-
},
|
| 751 |
-
{
|
| 752 |
-
"cell_type": "markdown",
|
| 753 |
-
"id": "2e4e15c9",
|
| 754 |
-
"metadata": {},
|
| 755 |
-
"source": [
|
| 756 |
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"## QUECHUA"
|
| 757 |
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]
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| 758 |
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},
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| 759 |
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{
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| 760 |
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"cell_type": "code",
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| 761 |
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"execution_count": 8,
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| 762 |
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"id": "e165f4bf",
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| 763 |
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"metadata": {
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| 764 |
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"scrolled": true
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| 765 |
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},
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| 766 |
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"outputs": [
|
| 767 |
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{
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| 768 |
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"data": {
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| 769 |
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"application/vnd.jupyter.widget-view+json": {
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| 770 |
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"model_id": "9c96f2ce38474bc990e57387acd56fc8",
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| 771 |
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"version_major": 2,
|
| 772 |
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"version_minor": 0
|
| 773 |
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},
|
| 774 |
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"text/plain": [
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| 775 |
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"Map: 0%| | 0/250 [00:00<?, ? examples/s]"
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| 776 |
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]
|
| 777 |
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},
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| 778 |
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"metadata": {},
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| 779 |
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"output_type": "display_data"
|
| 780 |
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},
|
| 781 |
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{
|
| 782 |
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"ename": "LibsndfileError",
|
| 783 |
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"evalue": "Error opening 'data/quechua/dev/quechua000573.wav': System error.",
|
| 784 |
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"output_type": "error",
|
| 785 |
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"traceback": [
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| 786 |
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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| 787 |
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"\u001b[0;31mLibsndfileError\u001b[0m Traceback (most recent call last)",
|
| 788 |
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"Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 22\u001b[0m batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtranscription\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m processor\u001b[38;5;241m.\u001b[39mbatch_decode(predicted_ids)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m batch\n\u001b[0;32m---> 25\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mquechua\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_to_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCER:\u001b[39m\u001b[38;5;124m\"\u001b[39m, cer(result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m], result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtranscription\u001b[39m\u001b[38;5;124m\"\u001b[39m]))\n",
|
| 789 |
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"File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:602\u001b[0m, in \u001b[0;36mtransmit_tasks.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 600\u001b[0m \u001b[38;5;28mself\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 601\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 602\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 603\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 604\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[1;32m 605\u001b[0m \u001b[38;5;66;03m# Remove task templates if a column mapping of the template is no longer valid\u001b[39;00m\n",
|
| 790 |
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"File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:567\u001b[0m, in \u001b[0;36mtransmit_format.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 560\u001b[0m self_format \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 561\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 562\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 563\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 564\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 565\u001b[0m }\n\u001b[1;32m 566\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 567\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 568\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 569\u001b[0m \u001b[38;5;66;03m# re-apply format to the output\u001b[39;00m\n",
|
| 791 |
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"File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:3156\u001b[0m, in \u001b[0;36mDataset.map\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m 3150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m transformed_dataset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3151\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m hf_tqdm(\n\u001b[1;32m 3152\u001b[0m unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m examples\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3153\u001b[0m total\u001b[38;5;241m=\u001b[39mpbar_total,\n\u001b[1;32m 3154\u001b[0m desc\u001b[38;5;241m=\u001b[39mdesc \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMap\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3155\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m pbar:\n\u001b[0;32m-> 3156\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m rank, done, content \u001b[38;5;129;01min\u001b[39;00m Dataset\u001b[38;5;241m.\u001b[39m_map_single(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdataset_kwargs):\n\u001b[1;32m 3157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[1;32m 3158\u001b[0m shards_done \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
|
| 792 |
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"File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:3547\u001b[0m, in \u001b[0;36mDataset._map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m 3543\u001b[0m indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m 3544\u001b[0m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mslice\u001b[39m(i, i \u001b[38;5;241m+\u001b[39m batch_size)\u001b[38;5;241m.\u001b[39mindices(shard\u001b[38;5;241m.\u001b[39mnum_rows)))\n\u001b[1;32m 3545\u001b[0m ) \u001b[38;5;66;03m# Something simpler?\u001b[39;00m\n\u001b[1;32m 3546\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3547\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[43mapply_function_on_filtered_inputs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3548\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3549\u001b[0m \u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3550\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_same_num_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mshard\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_indexes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3551\u001b[0m \u001b[43m \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3552\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3553\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NumExamplesMismatchError:\n\u001b[1;32m 3554\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DatasetTransformationNotAllowedError(\n\u001b[1;32m 3555\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3556\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
| 793 |
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"File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:3416\u001b[0m, in \u001b[0;36mDataset._map_single.<locals>.apply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m 3414\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_rank:\n\u001b[1;32m 3415\u001b[0m additional_args \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (rank,)\n\u001b[0;32m-> 3416\u001b[0m processed_inputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfn_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_args\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[43mfn_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed_inputs, LazyDict):\n\u001b[1;32m 3418\u001b[0m processed_inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 3419\u001b[0m k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mkeys_to_format\n\u001b[1;32m 3420\u001b[0m }\n",
|
| 794 |
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"Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36mmap_to_pred\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmap_to_pred\u001b[39m(batch):\n\u001b[0;32m---> 16\u001b[0m wav, curr_sample_rate \u001b[38;5;241m=\u001b[39m \u001b[43msf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfile_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfloat32\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 17\u001b[0m feats \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(wav)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m 18\u001b[0m feats \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mlayer_norm(feats, feats\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;66;03m# Normalization performed during finetuning\u001b[39;00m\n",
|
| 795 |
-
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/soundfile.py:285\u001b[0m, in \u001b[0;36mread\u001b[0;34m(file, frames, start, stop, dtype, always_2d, fill_value, out, samplerate, channels, format, subtype, endian, closefd)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(file, frames\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, stop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat64\u001b[39m\u001b[38;5;124m'\u001b[39m, always_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 200\u001b[0m fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, samplerate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, subtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, endian\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, closefd\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 202\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Provide audio data from a sound file as NumPy array.\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \n\u001b[1;32m 204\u001b[0m \u001b[38;5;124;03m By default, the whole file is read from the beginning, but the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 283\u001b[0m \n\u001b[1;32m 284\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 285\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mSoundFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msamplerate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 286\u001b[0m \u001b[43m \u001b[49m\u001b[43msubtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mendian\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosefd\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 287\u001b[0m frames \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39m_prepare_read(start, stop, frames)\n\u001b[1;32m 288\u001b[0m data \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread(frames, dtype, always_2d, fill_value, out)\n",
|
| 796 |
-
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/soundfile.py:658\u001b[0m, in \u001b[0;36mSoundFile.__init__\u001b[0;34m(self, file, mode, samplerate, channels, subtype, endian, format, closefd)\u001b[0m\n\u001b[1;32m 655\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[1;32m 656\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info \u001b[38;5;241m=\u001b[39m _create_info_struct(file, mode, samplerate, channels,\n\u001b[1;32m 657\u001b[0m \u001b[38;5;28mformat\u001b[39m, subtype, endian)\n\u001b[0;32m--> 658\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode_int\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosefd\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 659\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mset\u001b[39m(mode)\u001b[38;5;241m.\u001b[39missuperset(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseekable():\n\u001b[1;32m 660\u001b[0m \u001b[38;5;66;03m# Move write position to 0 (like in Python file objects)\u001b[39;00m\n\u001b[1;32m 661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n",
|
| 797 |
-
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/soundfile.py:1216\u001b[0m, in \u001b[0;36mSoundFile._open\u001b[0;34m(self, file, mode_int, closefd)\u001b[0m\n\u001b[1;32m 1213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file_ptr \u001b[38;5;241m==\u001b[39m _ffi\u001b[38;5;241m.\u001b[39mNULL:\n\u001b[1;32m 1214\u001b[0m \u001b[38;5;66;03m# get the actual error code\u001b[39;00m\n\u001b[1;32m 1215\u001b[0m err \u001b[38;5;241m=\u001b[39m _snd\u001b[38;5;241m.\u001b[39msf_error(file_ptr)\n\u001b[0;32m-> 1216\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LibsndfileError(err, prefix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError opening \u001b[39m\u001b[38;5;132;01m{0!r}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m 1217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode_int \u001b[38;5;241m==\u001b[39m _snd\u001b[38;5;241m.\u001b[39mSFM_WRITE:\n\u001b[1;32m 1218\u001b[0m \u001b[38;5;66;03m# Due to a bug in libsndfile version <= 1.0.25, frames != 0\u001b[39;00m\n\u001b[1;32m 1219\u001b[0m \u001b[38;5;66;03m# when opening a named pipe in SFM_WRITE mode.\u001b[39;00m\n\u001b[1;32m 1220\u001b[0m \u001b[38;5;66;03m# See http://github.com/erikd/libsndfile/issues/77.\u001b[39;00m\n\u001b[1;32m 1221\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mframes \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
|
| 798 |
-
"\u001b[0;31mLibsndfileError\u001b[0m: Error opening 'data/quechua/dev/quechua000573.wav': System error."
|
| 799 |
-
]
|
| 800 |
-
}
|
| 801 |
-
],
|
| 802 |
-
"source": [
|
| 803 |
-
"from datasets import load_dataset\n",
|
| 804 |
-
"from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n",
|
| 805 |
-
"import torch\n",
|
| 806 |
-
"from jiwer import cer\n",
|
| 807 |
-
"import torch.nn.functional as F\n",
|
| 808 |
-
"from datasets import load_dataset\n",
|
| 809 |
-
"import soundfile as sf\n",
|
| 810 |
-
"\n",
|
| 811 |
-
"americasnlp = load_dataset(\"ivangtorre/second_americas_nlp_2022\", split=\"dev\")\n",
|
| 812 |
-
"quechua = americasnlp.filter(lambda language: language['subset']=='quechua')\n",
|
| 813 |
-
"\n",
|
| 814 |
-
"model = Wav2Vec2ForCTC.from_pretrained(\"ivangtorre/wav2vec2-xlsr-300m-quechua\")\n",
|
| 815 |
-
"processor = Wav2Vec2Processor.from_pretrained(\"ivangtorre/wav2vec2-xlsr-300m-quechua\")\n",
|
| 816 |
-
"\n",
|
| 817 |
-
"def map_to_pred(batch):\n",
|
| 818 |
-
" wav, curr_sample_rate = sf.read(batch[\"file_name\"][0], dtype=\"float32\")\n",
|
| 819 |
-
" feats = torch.from_numpy(wav).float()\n",
|
| 820 |
-
" feats = F.layer_norm(feats, feats.shape) # Normalization performed during finetuning\n",
|
| 821 |
-
" feats = torch.unsqueeze(feats, 0)\n",
|
| 822 |
-
" logits = model(feats).logits\n",
|
| 823 |
-
" predicted_ids = torch.argmax(logits, dim=-1)\n",
|
| 824 |
-
" batch[\"transcription\"] = processor.batch_decode(predicted_ids)\n",
|
| 825 |
-
" return batch\n",
|
| 826 |
-
"\n",
|
| 827 |
-
"result = quechua.map(map_to_pred, batched=True, batch_size=1)\n",
|
| 828 |
-
"\n",
|
| 829 |
-
"print(\"CER:\", cer(result[\"source_processed\"], result[\"transcription\"]))\n"
|
| 830 |
-
]
|
| 831 |
-
},
|
| 832 |
-
{
|
| 833 |
-
"cell_type": "markdown",
|
| 834 |
-
"id": "8e29bc13",
|
| 835 |
-
"metadata": {},
|
| 836 |
-
"source": [
|
| 837 |
-
"## BRIBRI\n"
|
| 838 |
-
]
|
| 839 |
-
},
|
| 840 |
-
{
|
| 841 |
-
"cell_type": "code",
|
| 842 |
-
"execution_count": 7,
|
| 843 |
-
"id": "7cdec414",
|
| 844 |
-
"metadata": {},
|
| 845 |
-
"outputs": [
|
| 846 |
-
{
|
| 847 |
-
"data": {
|
| 848 |
-
"text/plain": [
|
| 849 |
-
"'data/quechua/dev/quechua000573.wav'"
|
| 850 |
-
]
|
| 851 |
-
},
|
| 852 |
-
"execution_count": 7,
|
| 853 |
-
"metadata": {},
|
| 854 |
-
"output_type": "execute_result"
|
| 855 |
-
}
|
| 856 |
-
],
|
| 857 |
-
"source": [
|
| 858 |
-
"quechua[0:1][\"file_name\"][0]"
|
| 859 |
-
]
|
| 860 |
-
}
|
| 861 |
-
],
|
| 862 |
-
"metadata": {
|
| 863 |
-
"kernelspec": {
|
| 864 |
-
"display_name": "Python 3 (ipykernel)",
|
| 865 |
-
"language": "python",
|
| 866 |
-
"name": "python3"
|
| 867 |
-
},
|
| 868 |
-
"language_info": {
|
| 869 |
-
"codemirror_mode": {
|
| 870 |
-
"name": "ipython",
|
| 871 |
-
"version": 3
|
| 872 |
-
},
|
| 873 |
-
"file_extension": ".py",
|
| 874 |
-
"mimetype": "text/x-python",
|
| 875 |
-
"name": "python",
|
| 876 |
-
"nbconvert_exporter": "python",
|
| 877 |
-
"pygments_lexer": "ipython3",
|
| 878 |
-
"version": "3.10.12"
|
| 879 |
-
}
|
| 880 |
-
},
|
| 881 |
-
"nbformat": 4,
|
| 882 |
-
"nbformat_minor": 5
|
| 883 |
-
}
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