Commit
•
9e91ea9
1
Parent(s):
fde05c0
changing and deleting files
Browse files
Generate_metadata.ipynb
ADDED
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@@ -0,0 +1,590 @@
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| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "91b21cf6",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## Generate the datasets for uploading"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": null,
|
| 14 |
+
"id": "e1a3d25b",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [],
|
| 17 |
+
"source": []
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"cell_type": "code",
|
| 21 |
+
"execution_count": 14,
|
| 22 |
+
"id": "aa925968",
|
| 23 |
+
"metadata": {
|
| 24 |
+
"scrolled": true
|
| 25 |
+
},
|
| 26 |
+
"outputs": [
|
| 27 |
+
{
|
| 28 |
+
"name": "stdout",
|
| 29 |
+
"output_type": "stream",
|
| 30 |
+
"text": [
|
| 31 |
+
"[]\n",
|
| 32 |
+
"[]\n",
|
| 33 |
+
"['kotiria000263.wav', 'kotiria000265.wav', 'kotiria000273.wav', 'kotiria000285.wav', 'kotiria000289.wav', 'kotiria000291.wav', 'kotiria000294.wav', 'kotiria000295.wav', 'kotiria000297.wav', 'kotiria000300.wav', 'kotiria000306.wav', 'kotiria000308.wav']\n",
|
| 34 |
+
"[]\n",
|
| 35 |
+
"['waikhana000740.wav', 'waikhana000745.wav', 'waikhana000746.wav']\n"
|
| 36 |
+
]
|
| 37 |
+
},
|
| 38 |
+
{
|
| 39 |
+
"data": {
|
| 40 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 41 |
+
"model_id": "15adf9d48a44440dac871ce9f432294c",
|
| 42 |
+
"version_major": 2,
|
| 43 |
+
"version_minor": 0
|
| 44 |
+
},
|
| 45 |
+
"text/plain": [
|
| 46 |
+
"Uploading the dataset shards: 0%| | 0/3 [00:00<?, ?it/s]"
|
| 47 |
+
]
|
| 48 |
+
},
|
| 49 |
+
"metadata": {},
|
| 50 |
+
"output_type": "display_data"
|
| 51 |
+
},
|
| 52 |
+
{
|
| 53 |
+
"data": {
|
| 54 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 55 |
+
"model_id": "aa491992d4fa43688c71ea1e09b25ca0",
|
| 56 |
+
"version_major": 2,
|
| 57 |
+
"version_minor": 0
|
| 58 |
+
},
|
| 59 |
+
"text/plain": [
|
| 60 |
+
"Map: 0%| | 0/1583 [00:00<?, ? examples/s]"
|
| 61 |
+
]
|
| 62 |
+
},
|
| 63 |
+
"metadata": {},
|
| 64 |
+
"output_type": "display_data"
|
| 65 |
+
},
|
| 66 |
+
{
|
| 67 |
+
"data": {
|
| 68 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 69 |
+
"model_id": "0f560606c9094daf92d9f5328f18b2dd",
|
| 70 |
+
"version_major": 2,
|
| 71 |
+
"version_minor": 0
|
| 72 |
+
},
|
| 73 |
+
"text/plain": [
|
| 74 |
+
"Creating parquet from Arrow format: 0%| | 0/16 [00:00<?, ?ba/s]"
|
| 75 |
+
]
|
| 76 |
+
},
|
| 77 |
+
"metadata": {},
|
| 78 |
+
"output_type": "display_data"
|
| 79 |
+
},
|
| 80 |
+
{
|
| 81 |
+
"data": {
|
| 82 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 83 |
+
"model_id": "538b15ad0bbe4684a09ae610fce7ab8c",
|
| 84 |
+
"version_major": 2,
|
| 85 |
+
"version_minor": 0
|
| 86 |
+
},
|
| 87 |
+
"text/plain": [
|
| 88 |
+
"Map: 0%| | 0/1583 [00:00<?, ? examples/s]"
|
| 89 |
+
]
|
| 90 |
+
},
|
| 91 |
+
"metadata": {},
|
| 92 |
+
"output_type": "display_data"
|
| 93 |
+
},
|
| 94 |
+
{
|
| 95 |
+
"data": {
|
| 96 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 97 |
+
"model_id": "391fb889ea15447ca8ec509a04de2ebe",
|
| 98 |
+
"version_major": 2,
|
| 99 |
+
"version_minor": 0
|
| 100 |
+
},
|
| 101 |
+
"text/plain": [
|
| 102 |
+
"Creating parquet from Arrow format: 0%| | 0/16 [00:00<?, ?ba/s]"
|
| 103 |
+
]
|
| 104 |
+
},
|
| 105 |
+
"metadata": {},
|
| 106 |
+
"output_type": "display_data"
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"data": {
|
| 110 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 111 |
+
"model_id": "960a0088fc564383a32d2f6f0816b215",
|
| 112 |
+
"version_major": 2,
|
| 113 |
+
"version_minor": 0
|
| 114 |
+
},
|
| 115 |
+
"text/plain": [
|
| 116 |
+
"Map: 0%| | 0/1583 [00:00<?, ? examples/s]"
|
| 117 |
+
]
|
| 118 |
+
},
|
| 119 |
+
"metadata": {},
|
| 120 |
+
"output_type": "display_data"
|
| 121 |
+
},
|
| 122 |
+
{
|
| 123 |
+
"data": {
|
| 124 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 125 |
+
"model_id": "3355cb1c65d84a24b1a146a17e43b1c4",
|
| 126 |
+
"version_major": 2,
|
| 127 |
+
"version_minor": 0
|
| 128 |
+
},
|
| 129 |
+
"text/plain": [
|
| 130 |
+
"Creating parquet from Arrow format: 0%| | 0/16 [00:00<?, ?ba/s]"
|
| 131 |
+
]
|
| 132 |
+
},
|
| 133 |
+
"metadata": {},
|
| 134 |
+
"output_type": "display_data"
|
| 135 |
+
},
|
| 136 |
+
{
|
| 137 |
+
"ename": "ValueError",
|
| 138 |
+
"evalue": "Features of the new split don't match the features of the existing splits on the hub: {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None), 'source_processed': Value(dtype='string', id=None), 'source_raw': Value(dtype='string', id=None), 'target_raw': Value(dtype='string', id=None)} != {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None)}",
|
| 139 |
+
"output_type": "error",
|
| 140 |
+
"traceback": [
|
| 141 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 142 |
+
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
| 143 |
+
"Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 38>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m a \u001b[38;5;241m=\u001b[39m flatten(a)\n\u001b[1;32m 32\u001b[0m audio_dataset \u001b[38;5;241m=\u001b[39m Dataset\u001b[38;5;241m.\u001b[39mfrom_dict({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile_name\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m 33\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m 34\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m 35\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m 36\u001b[0m },\n\u001b[1;32m 37\u001b[0m )\u001b[38;5;241m.\u001b[39mcast_column(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m, Audio())\n\u001b[0;32m---> 38\u001b[0m \u001b[43maudio_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mivangtorre/second_americas_nlp_2022\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 40\u001b[0m df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 42\u001b[0m df \u001b[38;5;241m=\u001b[39m generate_df(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquechua\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdev\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
| 144 |
+
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:5707\u001b[0m, in \u001b[0;36mDataset.push_to_hub\u001b[0;34m(self, repo_id, config_name, set_default, split, data_dir, commit_message, commit_description, private, token, revision, branch, create_pr, max_shard_size, num_shards, embed_external_files)\u001b[0m\n\u001b[1;32m 5705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m repo_info\u001b[38;5;241m.\u001b[39msplits \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(repo_info\u001b[38;5;241m.\u001b[39msplits) \u001b[38;5;241m!=\u001b[39m [split]:\n\u001b[1;32m 5706\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mfeatures \u001b[38;5;241m!=\u001b[39m repo_info\u001b[38;5;241m.\u001b[39mfeatures:\n\u001b[0;32m-> 5707\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 5708\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeatures of the new split don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt match the features of the existing splits on the hub: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m != \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_info\u001b[38;5;241m.\u001b[39mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 5709\u001b[0m )\n\u001b[1;32m 5711\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m repo_info\u001b[38;5;241m.\u001b[39msplits:\n\u001b[1;32m 5712\u001b[0m repo_info\u001b[38;5;241m.\u001b[39mdownload_size \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m deleted_size\n",
|
| 145 |
+
"\u001b[0;31mValueError\u001b[0m: Features of the new split don't match the features of the existing splits on the hub: {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None), 'source_processed': Value(dtype='string', id=None), 'source_raw': Value(dtype='string', id=None), 'target_raw': Value(dtype='string', id=None)} != {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None)}"
|
| 146 |
+
]
|
| 147 |
+
}
|
| 148 |
+
],
|
| 149 |
+
"source": [
|
| 150 |
+
"import pandas as pd\n",
|
| 151 |
+
"from datasets import Dataset, Audio\n",
|
| 152 |
+
"\n",
|
| 153 |
+
"def generate_df(language, split):\n",
|
| 154 |
+
" # QUECHUA TRAIN\n",
|
| 155 |
+
" with open(\"./../\"+language +\"_\"+split+\".tsv\") as f:\n",
|
| 156 |
+
" lines = f.read().splitlines()\n",
|
| 157 |
+
" lines2 = [l.split(\"\\t\") for l in lines if len(l.split(\"\\t\"))==4]\n",
|
| 158 |
+
" asd = [l.split(\"\\t\")[0] for l in lines if len(l.split(\"\\t\"))>4]\n",
|
| 159 |
+
" print(asd)\n",
|
| 160 |
+
" df1 = pd.DataFrame(lines2[1::], columns =lines2[0:1])\n",
|
| 161 |
+
" df1 = df1.assign(split=[split]*df1.shape[0])\n",
|
| 162 |
+
" df1 = df1.assign(subset=[language]*df1.shape[0])\n",
|
| 163 |
+
" df1 = df1.rename(columns={'wav': 'file_name'})\n",
|
| 164 |
+
" df1['file_name'] = 'data/' + language + '/' + split +'/' + df1['file_name'].astype(str)\n",
|
| 165 |
+
" return df1\n",
|
| 166 |
+
"\n",
|
| 167 |
+
"df = generate_df(\"quechua\", \"train\")\n",
|
| 168 |
+
"df = pd.concat([df, generate_df(\"guarani\", \"train\")])\n",
|
| 169 |
+
"df = pd.concat([df, generate_df(\"kotiria\", \"train\")])\n",
|
| 170 |
+
"df = pd.concat([df, generate_df(\"bribri\", \"train\")])\n",
|
| 171 |
+
"df = pd.concat([df, generate_df(\"waikhana\", \"train\")])\n",
|
| 172 |
+
"cols = df.columns.tolist()\n",
|
| 173 |
+
"cols = cols[-1:] + cols[:-1]\n",
|
| 174 |
+
"df = df[cols]\n",
|
| 175 |
+
"\n",
|
| 176 |
+
"def flatten(xss):\n",
|
| 177 |
+
" return [x for xs in xss for x in xs]\n",
|
| 178 |
+
"\n",
|
| 179 |
+
"a = flatten(df[\"file_name\"].values.tolist())\n",
|
| 180 |
+
"a = flatten(a)\n",
|
| 181 |
+
"audio_dataset = Dataset.from_dict({\"audio\": flatten(df[\"file_name\"].values.tolist()),\n",
|
| 182 |
+
" \"source_processed\": flatten(df[\"source_processed\"].values.tolist()),\n",
|
| 183 |
+
" \"source_raw\": flatten(df[\"source_raw\"].values.tolist()),\n",
|
| 184 |
+
" \"target_raw\": flatten(df[\"target_raw\"].values.tolist()),\n",
|
| 185 |
+
" },\n",
|
| 186 |
+
" ).cast_column(\"audio\", Audio())\n",
|
| 187 |
+
"audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", split=\"train\")\n",
|
| 188 |
+
"\n",
|
| 189 |
+
"df.to_csv(\"train.csv\", sep='\\t', index=None)\n",
|
| 190 |
+
"\n",
|
| 191 |
+
"df = generate_df(\"quechua\", \"dev\")\n",
|
| 192 |
+
"df = pd.concat([df, generate_df(\"guarani\", \"dev\")])\n",
|
| 193 |
+
"df = pd.concat([df, generate_df(\"kotiria\", \"dev\")])\n",
|
| 194 |
+
"df = pd.concat([df, generate_df(\"bribri\", \"dev\")])\n",
|
| 195 |
+
"df = pd.concat([df, generate_df(\"waikhana\", \"dev\")])\n",
|
| 196 |
+
"cols = df.columns.tolist()\n",
|
| 197 |
+
"cols = cols[-1:] + cols[:-1]\n",
|
| 198 |
+
"df = df[cols]\n",
|
| 199 |
+
"df.to_csv(\"dev.csv\", sep='\\t', index=None)\n",
|
| 200 |
+
"\n",
|
| 201 |
+
"a = df[\"file_name\"].values.tolist()\n",
|
| 202 |
+
"a = flatten(a)\n",
|
| 203 |
+
"#audio_dataset = Dataset.from_dict({\"audio\": a}).cast_column(\"audio\", Audio())\n",
|
| 204 |
+
"#audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", split=\"dev\")\n",
|
| 205 |
+
"\n"
|
| 206 |
+
]
|
| 207 |
+
},
|
| 208 |
+
{
|
| 209 |
+
"cell_type": "code",
|
| 210 |
+
"execution_count": 6,
|
| 211 |
+
"id": "4ce2eeb3",
|
| 212 |
+
"metadata": {},
|
| 213 |
+
"outputs": [
|
| 214 |
+
{
|
| 215 |
+
"data": {
|
| 216 |
+
"text/plain": [
|
| 217 |
+
"{'audio': {'path': 'data/quechua/train/quechua000000.wav',\n",
|
| 218 |
+
" 'array': array([0.00045776, 0.00042725, 0.00018311, ..., 0.00286865, 0.00186157,\n",
|
| 219 |
+
" 0.00253296]),\n",
|
| 220 |
+
" 'sampling_rate': 16000}}"
|
| 221 |
+
]
|
| 222 |
+
},
|
| 223 |
+
"execution_count": 6,
|
| 224 |
+
"metadata": {},
|
| 225 |
+
"output_type": "execute_result"
|
| 226 |
+
}
|
| 227 |
+
],
|
| 228 |
+
"source": [
|
| 229 |
+
"audio_dataset[0]"
|
| 230 |
+
]
|
| 231 |
+
},
|
| 232 |
+
{
|
| 233 |
+
"cell_type": "code",
|
| 234 |
+
"execution_count": 10,
|
| 235 |
+
"id": "bd39f2f4",
|
| 236 |
+
"metadata": {},
|
| 237 |
+
"outputs": [
|
| 238 |
+
{
|
| 239 |
+
"data": {
|
| 240 |
+
"text/html": [
|
| 241 |
+
"<div>\n",
|
| 242 |
+
"<style scoped>\n",
|
| 243 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 244 |
+
" vertical-align: middle;\n",
|
| 245 |
+
" }\n",
|
| 246 |
+
"\n",
|
| 247 |
+
" .dataframe tbody tr th {\n",
|
| 248 |
+
" vertical-align: top;\n",
|
| 249 |
+
" }\n",
|
| 250 |
+
"\n",
|
| 251 |
+
" .dataframe thead tr th {\n",
|
| 252 |
+
" text-align: left;\n",
|
| 253 |
+
" }\n",
|
| 254 |
+
"</style>\n",
|
| 255 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 256 |
+
" <thead>\n",
|
| 257 |
+
" <tr>\n",
|
| 258 |
+
" <th></th>\n",
|
| 259 |
+
" <th>subset</th>\n",
|
| 260 |
+
" <th>file_name</th>\n",
|
| 261 |
+
" <th>source_processed</th>\n",
|
| 262 |
+
" <th>source_raw</th>\n",
|
| 263 |
+
" <th>target_raw</th>\n",
|
| 264 |
+
" <th>split</th>\n",
|
| 265 |
+
" </tr>\n",
|
| 266 |
+
" </thead>\n",
|
| 267 |
+
" <tbody>\n",
|
| 268 |
+
" <tr>\n",
|
| 269 |
+
" <th>0</th>\n",
|
| 270 |
+
" <td>quechua</td>\n",
|
| 271 |
+
" <td>data/quechua/train/quechua000000.wav</td>\n",
|
| 272 |
+
" <td>wañuchisunchu kay suwakunata</td>\n",
|
| 273 |
+
" <td>wañuchisunchu kay suwakunata</td>\n",
|
| 274 |
+
" <td>matemos a esos ladrones</td>\n",
|
| 275 |
+
" <td>train</td>\n",
|
| 276 |
+
" </tr>\n",
|
| 277 |
+
" <tr>\n",
|
| 278 |
+
" <th>1</th>\n",
|
| 279 |
+
" <td>quechua</td>\n",
|
| 280 |
+
" <td>data/quechua/train/quechua000001.wav</td>\n",
|
| 281 |
+
" <td>imaninkichikmi qamkuna</td>\n",
|
| 282 |
+
" <td>imaninkichikmi qamkuna</td>\n",
|
| 283 |
+
" <td>que dicen ustedes</td>\n",
|
| 284 |
+
" <td>train</td>\n",
|
| 285 |
+
" </tr>\n",
|
| 286 |
+
" <tr>\n",
|
| 287 |
+
" <th>2</th>\n",
|
| 288 |
+
" <td>quechua</td>\n",
|
| 289 |
+
" <td>data/quechua/train/quechua000002.wav</td>\n",
|
| 290 |
+
" <td>hatun urqukunapi kunturkunapas uyarirqan</td>\n",
|
| 291 |
+
" <td>hatun urqukunapi kunturkunapas uyarirqan</td>\n",
|
| 292 |
+
" <td>en grandes montañas hasta los condores escuchaban</td>\n",
|
| 293 |
+
" <td>train</td>\n",
|
| 294 |
+
" </tr>\n",
|
| 295 |
+
" <tr>\n",
|
| 296 |
+
" <th>3</th>\n",
|
| 297 |
+
" <td>quechua</td>\n",
|
| 298 |
+
" <td>data/quechua/train/quechua000003.wav</td>\n",
|
| 299 |
+
" <td>ninsi winsislaw maqtaqa tumpa machasqaña</td>\n",
|
| 300 |
+
" <td>ninsi winsislaw maqtaqa tumpa machasqaña</td>\n",
|
| 301 |
+
" <td>dice el joven wessceslao cuando ya estaba borr...</td>\n",
|
| 302 |
+
" <td>train</td>\n",
|
| 303 |
+
" </tr>\n",
|
| 304 |
+
" <tr>\n",
|
| 305 |
+
" <th>4</th>\n",
|
| 306 |
+
" <td>quechua</td>\n",
|
| 307 |
+
" <td>data/quechua/train/quechua000004.wav</td>\n",
|
| 308 |
+
" <td>huk qilli chuspi chuspi misapi kimsantin suwak...</td>\n",
|
| 309 |
+
" <td>huk qilli chuspi chuspi misapi kimsantin suwak...</td>\n",
|
| 310 |
+
" <td>una sucia mosca en la mesa con los tres ladron...</td>\n",
|
| 311 |
+
" <td>train</td>\n",
|
| 312 |
+
" </tr>\n",
|
| 313 |
+
" <tr>\n",
|
| 314 |
+
" <th>...</th>\n",
|
| 315 |
+
" <td>...</td>\n",
|
| 316 |
+
" <td>...</td>\n",
|
| 317 |
+
" <td>...</td>\n",
|
| 318 |
+
" <td>...</td>\n",
|
| 319 |
+
" <td>...</td>\n",
|
| 320 |
+
" <td>...</td>\n",
|
| 321 |
+
" </tr>\n",
|
| 322 |
+
" <tr>\n",
|
| 323 |
+
" <th>1411</th>\n",
|
| 324 |
+
" <td>waikhana</td>\n",
|
| 325 |
+
" <td>data/waikhana/train/waikhana001414.wav</td>\n",
|
| 326 |
+
" <td>masiaha malia masinapea</td>\n",
|
| 327 |
+
" <td>masiaha malia masinapea, ()</td>\n",
|
| 328 |
+
" <td>Nos tambem sabemos (as historias antigas)</td>\n",
|
| 329 |
+
" <td>train</td>\n",
|
| 330 |
+
" </tr>\n",
|
| 331 |
+
" <tr>\n",
|
| 332 |
+
" <th>1412</th>\n",
|
| 333 |
+
" <td>waikhana</td>\n",
|
| 334 |
+
" <td>data/waikhana/train/waikhana001415.wav</td>\n",
|
| 335 |
+
" <td>a'lide mu:sale ya'uaha yu:'u:</td>\n",
|
| 336 |
+
" <td>a'lide mu:sale ya'uaha yu:'u:</td>\n",
|
| 337 |
+
" <td>Tudo isso estou explicando para voces.</td>\n",
|
| 338 |
+
" <td>train</td>\n",
|
| 339 |
+
" </tr>\n",
|
| 340 |
+
" <tr>\n",
|
| 341 |
+
" <th>1413</th>\n",
|
| 342 |
+
" <td>waikhana</td>\n",
|
| 343 |
+
" <td>data/waikhana/train/waikhana001416.wav</td>\n",
|
| 344 |
+
" <td>a'lide tina a'likodo pekasonoko a'li gravaka'a...</td>\n",
|
| 345 |
+
" <td>a'lide tina a'likodo pekasonoko a'li gravaka'a...</td>\n",
|
| 346 |
+
" <td>Tudo isso essa branca vai gravar.</td>\n",
|
| 347 |
+
" <td>train</td>\n",
|
| 348 |
+
" </tr>\n",
|
| 349 |
+
" <tr>\n",
|
| 350 |
+
" <th>1414</th>\n",
|
| 351 |
+
" <td>waikhana</td>\n",
|
| 352 |
+
" <td>data/waikhana/train/waikhana001417.wav</td>\n",
|
| 353 |
+
" <td>sayeotha ninokata mipe</td>\n",
|
| 354 |
+
" <td>sayeotha ninokata mipe</td>\n",
|
| 355 |
+
" <td>Ela disse que vai fazer tudo isso,</td>\n",
|
| 356 |
+
" <td>train</td>\n",
|
| 357 |
+
" </tr>\n",
|
| 358 |
+
" <tr>\n",
|
| 359 |
+
" <th>1415</th>\n",
|
| 360 |
+
" <td>waikhana</td>\n",
|
| 361 |
+
" <td>data/waikhana/train/waikhana001418.wav</td>\n",
|
| 362 |
+
" <td>yu:'u:le ~o'o ihide yu:'u: akaye</td>\n",
|
| 363 |
+
" <td>yu:'u:le ~o'o ihide yu:'u: akaye</td>\n",
|
| 364 |
+
" <td>Para mim, e' ate aqui, meus irmaos.</td>\n",
|
| 365 |
+
" <td>train</td>\n",
|
| 366 |
+
" </tr>\n",
|
| 367 |
+
" </tbody>\n",
|
| 368 |
+
"</table>\n",
|
| 369 |
+
"<p>4749 rows × 6 columns</p>\n",
|
| 370 |
+
"</div>"
|
| 371 |
+
],
|
| 372 |
+
"text/plain": [
|
| 373 |
+
" subset file_name \\\n",
|
| 374 |
+
"0 quechua data/quechua/train/quechua000000.wav \n",
|
| 375 |
+
"1 quechua data/quechua/train/quechua000001.wav \n",
|
| 376 |
+
"2 quechua data/quechua/train/quechua000002.wav \n",
|
| 377 |
+
"3 quechua data/quechua/train/quechua000003.wav \n",
|
| 378 |
+
"4 quechua data/quechua/train/quechua000004.wav \n",
|
| 379 |
+
"... ... ... \n",
|
| 380 |
+
"1411 waikhana data/waikhana/train/waikhana001414.wav \n",
|
| 381 |
+
"1412 waikhana data/waikhana/train/waikhana001415.wav \n",
|
| 382 |
+
"1413 waikhana data/waikhana/train/waikhana001416.wav \n",
|
| 383 |
+
"1414 waikhana data/waikhana/train/waikhana001417.wav \n",
|
| 384 |
+
"1415 waikhana data/waikhana/train/waikhana001418.wav \n",
|
| 385 |
+
"\n",
|
| 386 |
+
" source_processed \\\n",
|
| 387 |
+
"0 wañuchisunchu kay suwakunata \n",
|
| 388 |
+
"1 imaninkichikmi qamkuna \n",
|
| 389 |
+
"2 hatun urqukunapi kunturkunapas uyarirqan \n",
|
| 390 |
+
"3 ninsi winsislaw maqtaqa tumpa machasqaña \n",
|
| 391 |
+
"4 huk qilli chuspi chuspi misapi kimsantin suwak... \n",
|
| 392 |
+
"... ... \n",
|
| 393 |
+
"1411 masiaha malia masinapea \n",
|
| 394 |
+
"1412 a'lide mu:sale ya'uaha yu:'u: \n",
|
| 395 |
+
"1413 a'lide tina a'likodo pekasonoko a'li gravaka'a... \n",
|
| 396 |
+
"1414 sayeotha ninokata mipe \n",
|
| 397 |
+
"1415 yu:'u:le ~o'o ihide yu:'u: akaye \n",
|
| 398 |
+
"\n",
|
| 399 |
+
" source_raw \\\n",
|
| 400 |
+
"0 wañuchisunchu kay suwakunata \n",
|
| 401 |
+
"1 imaninkichikmi qamkuna \n",
|
| 402 |
+
"2 hatun urqukunapi kunturkunapas uyarirqan \n",
|
| 403 |
+
"3 ninsi winsislaw maqtaqa tumpa machasqaña \n",
|
| 404 |
+
"4 huk qilli chuspi chuspi misapi kimsantin suwak... \n",
|
| 405 |
+
"... ... \n",
|
| 406 |
+
"1411 masiaha malia masinapea, () \n",
|
| 407 |
+
"1412 a'lide mu:sale ya'uaha yu:'u: \n",
|
| 408 |
+
"1413 a'lide tina a'likodo pekasonoko a'li gravaka'a... \n",
|
| 409 |
+
"1414 sayeotha ninokata mipe \n",
|
| 410 |
+
"1415 yu:'u:le ~o'o ihide yu:'u: akaye \n",
|
| 411 |
+
"\n",
|
| 412 |
+
" target_raw split \n",
|
| 413 |
+
"0 matemos a esos ladrones train \n",
|
| 414 |
+
"1 que dicen ustedes train \n",
|
| 415 |
+
"2 en grandes montañas hasta los condores escuchaban train \n",
|
| 416 |
+
"3 dice el joven wessceslao cuando ya estaba borr... train \n",
|
| 417 |
+
"4 una sucia mosca en la mesa con los tres ladron... train \n",
|
| 418 |
+
"... ... ... \n",
|
| 419 |
+
"1411 Nos tambem sabemos (as historias antigas) train \n",
|
| 420 |
+
"1412 Tudo isso estou explicando para voces. train \n",
|
| 421 |
+
"1413 Tudo isso essa branca vai gravar. train \n",
|
| 422 |
+
"1414 Ela disse que vai fazer tudo isso, train \n",
|
| 423 |
+
"1415 Para mim, e' ate aqui, meus irmaos. train \n",
|
| 424 |
+
"\n",
|
| 425 |
+
"[4749 rows x 6 columns]"
|
| 426 |
+
]
|
| 427 |
+
},
|
| 428 |
+
"execution_count": 10,
|
| 429 |
+
"metadata": {},
|
| 430 |
+
"output_type": "execute_result"
|
| 431 |
+
}
|
| 432 |
+
],
|
| 433 |
+
"source": [
|
| 434 |
+
"df"
|
| 435 |
+
]
|
| 436 |
+
},
|
| 437 |
+
{
|
| 438 |
+
"cell_type": "code",
|
| 439 |
+
"execution_count": 2,
|
| 440 |
+
"id": "a1f02703",
|
| 441 |
+
"metadata": {
|
| 442 |
+
"scrolled": true
|
| 443 |
+
},
|
| 444 |
+
"outputs": [],
|
| 445 |
+
"source": [
|
| 446 |
+
"#from datasets import load_dataset\n",
|
| 447 |
+
"#dataset = load_dataset(\"audiofolder\", data_dir=\"second_americas_nlp_2022\")\n"
|
| 448 |
+
]
|
| 449 |
+
},
|
| 450 |
+
{
|
| 451 |
+
"cell_type": "markdown",
|
| 452 |
+
"id": "5eaa7c93",
|
| 453 |
+
"metadata": {},
|
| 454 |
+
"source": [
|
| 455 |
+
"# EVALUATE MODELS\n"
|
| 456 |
+
]
|
| 457 |
+
},
|
| 458 |
+
{
|
| 459 |
+
"cell_type": "markdown",
|
| 460 |
+
"id": "2e4e15c9",
|
| 461 |
+
"metadata": {},
|
| 462 |
+
"source": [
|
| 463 |
+
"## QUECHUA"
|
| 464 |
+
]
|
| 465 |
+
},
|
| 466 |
+
{
|
| 467 |
+
"cell_type": "code",
|
| 468 |
+
"execution_count": 8,
|
| 469 |
+
"id": "e165f4bf",
|
| 470 |
+
"metadata": {
|
| 471 |
+
"scrolled": true
|
| 472 |
+
},
|
| 473 |
+
"outputs": [
|
| 474 |
+
{
|
| 475 |
+
"data": {
|
| 476 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 477 |
+
"model_id": "9c96f2ce38474bc990e57387acd56fc8",
|
| 478 |
+
"version_major": 2,
|
| 479 |
+
"version_minor": 0
|
| 480 |
+
},
|
| 481 |
+
"text/plain": [
|
| 482 |
+
"Map: 0%| | 0/250 [00:00<?, ? examples/s]"
|
| 483 |
+
]
|
| 484 |
+
},
|
| 485 |
+
"metadata": {},
|
| 486 |
+
"output_type": "display_data"
|
| 487 |
+
},
|
| 488 |
+
{
|
| 489 |
+
"ename": "LibsndfileError",
|
| 490 |
+
"evalue": "Error opening 'data/quechua/dev/quechua000573.wav': System error.",
|
| 491 |
+
"output_type": "error",
|
| 492 |
+
"traceback": [
|
| 493 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 494 |
+
"\u001b[0;31mLibsndfileError\u001b[0m Traceback (most recent call last)",
|
| 495 |
+
"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",
|
| 496 |
+
"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",
|
| 497 |
+
"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",
|
| 498 |
+
"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",
|
| 499 |
+
"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",
|
| 500 |
+
"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",
|
| 501 |
+
"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",
|
| 502 |
+
"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",
|
| 503 |
+
"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",
|
| 504 |
+
"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",
|
| 505 |
+
"\u001b[0;31mLibsndfileError\u001b[0m: Error opening 'data/quechua/dev/quechua000573.wav': System error."
|
| 506 |
+
]
|
| 507 |
+
}
|
| 508 |
+
],
|
| 509 |
+
"source": [
|
| 510 |
+
"from datasets import load_dataset\n",
|
| 511 |
+
"from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n",
|
| 512 |
+
"import torch\n",
|
| 513 |
+
"from jiwer import cer\n",
|
| 514 |
+
"import torch.nn.functional as F\n",
|
| 515 |
+
"from datasets import load_dataset\n",
|
| 516 |
+
"import soundfile as sf\n",
|
| 517 |
+
"\n",
|
| 518 |
+
"americasnlp = load_dataset(\"ivangtorre/second_americas_nlp_2022\", split=\"dev\")\n",
|
| 519 |
+
"quechua = americasnlp.filter(lambda language: language['subset']=='quechua')\n",
|
| 520 |
+
"\n",
|
| 521 |
+
"model = Wav2Vec2ForCTC.from_pretrained(\"ivangtorre/wav2vec2-xlsr-300m-quechua\")\n",
|
| 522 |
+
"processor = Wav2Vec2Processor.from_pretrained(\"ivangtorre/wav2vec2-xlsr-300m-quechua\")\n",
|
| 523 |
+
"\n",
|
| 524 |
+
"def map_to_pred(batch):\n",
|
| 525 |
+
" wav, curr_sample_rate = sf.read(batch[\"file_name\"][0], dtype=\"float32\")\n",
|
| 526 |
+
" feats = torch.from_numpy(wav).float()\n",
|
| 527 |
+
" feats = F.layer_norm(feats, feats.shape) # Normalization performed during finetuning\n",
|
| 528 |
+
" feats = torch.unsqueeze(feats, 0)\n",
|
| 529 |
+
" logits = model(feats).logits\n",
|
| 530 |
+
" predicted_ids = torch.argmax(logits, dim=-1)\n",
|
| 531 |
+
" batch[\"transcription\"] = processor.batch_decode(predicted_ids)\n",
|
| 532 |
+
" return batch\n",
|
| 533 |
+
"\n",
|
| 534 |
+
"result = quechua.map(map_to_pred, batched=True, batch_size=1)\n",
|
| 535 |
+
"\n",
|
| 536 |
+
"print(\"CER:\", cer(result[\"source_processed\"], result[\"transcription\"]))\n"
|
| 537 |
+
]
|
| 538 |
+
},
|
| 539 |
+
{
|
| 540 |
+
"cell_type": "markdown",
|
| 541 |
+
"id": "8e29bc13",
|
| 542 |
+
"metadata": {},
|
| 543 |
+
"source": [
|
| 544 |
+
"## BRIBRI\n"
|
| 545 |
+
]
|
| 546 |
+
},
|
| 547 |
+
{
|
| 548 |
+
"cell_type": "code",
|
| 549 |
+
"execution_count": 7,
|
| 550 |
+
"id": "7cdec414",
|
| 551 |
+
"metadata": {},
|
| 552 |
+
"outputs": [
|
| 553 |
+
{
|
| 554 |
+
"data": {
|
| 555 |
+
"text/plain": [
|
| 556 |
+
"'data/quechua/dev/quechua000573.wav'"
|
| 557 |
+
]
|
| 558 |
+
},
|
| 559 |
+
"execution_count": 7,
|
| 560 |
+
"metadata": {},
|
| 561 |
+
"output_type": "execute_result"
|
| 562 |
+
}
|
| 563 |
+
],
|
| 564 |
+
"source": [
|
| 565 |
+
"quechua[0:1][\"file_name\"][0]"
|
| 566 |
+
]
|
| 567 |
+
}
|
| 568 |
+
],
|
| 569 |
+
"metadata": {
|
| 570 |
+
"kernelspec": {
|
| 571 |
+
"display_name": "Python 3 (ipykernel)",
|
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