diff --git "a/wav2vec2/wav2vec_data.ipynb" "b/wav2vec2/wav2vec_data.ipynb"
--- "a/wav2vec2/wav2vec_data.ipynb"
+++ "b/wav2vec2/wav2vec_data.ipynb"
@@ -26,16 +26,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Fri Sep 2 01:31:23 2022 \n",
+ "Mon Dec 26 17:51:09 2022 \n",
"+-----------------------------------------------------------------------------+\n",
- "| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |\n",
+ "| NVIDIA-SMI 515.86.01 Driver Version: 515.86.01 CUDA Version: 11.7 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 NVIDIA GeForce ... Off | 00000000:0A:00.0 On | N/A |\n",
- "| 0% 35C P5 52W / 390W | 1231MiB / 24576MiB | 34% Default |\n",
+ "| 0% 34C P5 49W / 390W | 1437MiB / 24576MiB | 2% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
@@ -44,13 +44,23 @@
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
- "| 0 N/A N/A 1216 G /usr/lib/xorg/Xorg 485MiB |\n",
- "| 0 N/A N/A 1601 G /usr/bin/kwin_x11 97MiB |\n",
- "| 0 N/A N/A 1650 G /usr/bin/plasmashell 64MiB |\n",
- "| 0 N/A N/A 1747 G telegram-desktop 4MiB |\n",
- "| 0 N/A N/A 4701 G ...5/usr/lib/firefox/firefox 175MiB |\n",
- "| 0 N/A N/A 804722 G ...RendererForSitePerProcess 363MiB |\n",
- "| 0 N/A N/A 867357 G ...996071496053229024,131072 35MiB |\n",
+ "| 0 N/A N/A 1223 G /usr/lib/xorg/Xorg 498MiB |\n",
+ "| 0 N/A N/A 2007 G /usr/bin/kwalletd5 4MiB |\n",
+ "| 0 N/A N/A 2174 G ...ec/xdg-desktop-portal-kde 4MiB |\n",
+ "| 0 N/A N/A 2211 G /usr/bin/ksmserver 4MiB |\n",
+ "| 0 N/A N/A 2213 G /usr/bin/kded5 4MiB |\n",
+ "| 0 N/A N/A 2214 G /usr/bin/kwin_x11 96MiB |\n",
+ "| 0 N/A N/A 2263 G /usr/bin/plasmashell 96MiB |\n",
+ "| 0 N/A N/A 2283 G ...de-authentication-agent-1 4MiB |\n",
+ "| 0 N/A N/A 2354 G ...x-gnu/libexec/kdeconnectd 4MiB |\n",
+ "| 0 N/A N/A 2356 G .../usr/bin/telegram-desktop 7MiB |\n",
+ "| 0 N/A N/A 2370 G /usr/bin/kaccess 4MiB |\n",
+ "| 0 N/A N/A 2377 G .../libexec/DiscoverNotifier 4MiB |\n",
+ "| 0 N/A N/A 2443 G ...1/usr/lib/firefox/firefox 96MiB |\n",
+ "| 0 N/A N/A 2704 G /usr/bin/dolphin 4MiB |\n",
+ "| 0 N/A N/A 2806 G /usr/bin/dolphin 4MiB |\n",
+ "| 0 N/A N/A 2911 G /usr/bin/dolphin 4MiB |\n",
+ "| 0 N/A N/A 6634 G ...RendererForSitePerProcess 585MiB |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
@@ -82,7 +92,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -120,7 +130,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -132,7 +142,7 @@
"})"
]
},
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -143,7 +153,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -152,7 +162,7 @@
"0"
]
},
- "execution_count": 5,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -164,7 +174,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {
"id": "kbyq6lDgQc2a"
},
@@ -176,7 +186,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 9,
"metadata": {
"id": "72737oog2F6U"
},
@@ -202,7 +212,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -236,43 +246,43 @@
"
\n",
" \n",
" 0 | \n",
- " Вона нас не лякає. | \n",
+ " Щоночі одна півсотня ночувала у лісі, друга — в селі. | \n",
"
\n",
" \n",
" 1 | \n",
- " Бейнбрідж затримався, готуючи екіпажі, й фактично не встиг узяти участі у війні. | \n",
+ " І зараз за тим днем твоя доля зміниться на краще. | \n",
"
\n",
" \n",
" 2 | \n",
- " А тепер. | \n",
+ " Оп'ять Ірисю посила: | \n",
"
\n",
" \n",
" 3 | \n",
- " Наші \"будьонівці\", ніби з цікавості, зібралися й оточили червоні шеренги. | \n",
+ " Рушниця впала по той бік плоту. | \n",
"
\n",
" \n",
" 4 | \n",
- " Серед квітів я вмирав, Серед хмар я воскресав. | \n",
+ " Загуде вона, як гром. | \n",
"
\n",
" \n",
" 5 | \n",
- " Сьогодні виробництво полімерів найбільша галузь хімічної промисловості. | \n",
+ " Дружина не витримує і сміється. | \n",
"
\n",
" \n",
" 6 | \n",
- " Хмельницький заплатив за все на цілий рік наперед. | \n",
+ " Люблю, тільки боюся говорити. | \n",
"
\n",
" \n",
" 7 | \n",
- " Соловій же залишився підпалити бікфордів шнур. | \n",
+ " Звір заревів востаннє, сіпнувся головою назад і зник під водою. | \n",
"
\n",
" \n",
" 8 | \n",
- " Тоді його слово буде хвилювати, захоплювати, піднімати людську душу. | \n",
+ " Про їзду риссю не могло бути й мови. | \n",
"
\n",
" \n",
" 9 | \n",
- " Тут були яблуні, сливи, вишні, — вишень найбільше. | \n",
+ " Ми заночували в Бондуровій, — червоні — в Баландиному. | \n",
"
\n",
" \n",
""
@@ -291,7 +301,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -317,7 +327,7 @@
"ascii": false,
"bar_format": null,
"colour": null,
- "elapsed": 0.00995326042175293,
+ "elapsed": 0.018886804580688477,
"initial": 0,
"n": 0,
"ncols": null,
@@ -331,7 +341,7 @@
"unit_scale": false
},
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4c0c81459dfb4ede8f0ec6fe25a0807e",
+ "model_id": "609c1f72241d438999ec5fffaf0f23f5",
"version_major": 2,
"version_minor": 0
},
@@ -348,7 +358,7 @@
"ascii": false,
"bar_format": null,
"colour": null,
- "elapsed": 0.007294893264770508,
+ "elapsed": 0.011157989501953125,
"initial": 0,
"n": 0,
"ncols": null,
@@ -362,7 +372,7 @@
"unit_scale": false
},
"application/vnd.jupyter.widget-view+json": {
- "model_id": "66c16ae632444339ae8ec80070398586",
+ "model_id": "29df50a982df4687ae195c2edd1145d3",
"version_major": 2,
"version_minor": 0
},
@@ -382,7 +392,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {
"id": "ZcVsD0ETElrR"
},
@@ -393,7 +403,7 @@
"{'sentence': \"привіт як у тебе справи загалом м'якотілий друже\"}"
]
},
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -465,7 +475,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -492,7 +502,7 @@
"ascii": false,
"bar_format": null,
"colour": null,
- "elapsed": 0.00716710090637207,
+ "elapsed": 0.014832735061645508,
"initial": 0,
"n": 0,
"ncols": null,
@@ -506,7 +516,7 @@
"unit_scale": false
},
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ca29db891c7f4d0cbd328a65477d2392",
+ "model_id": "0fc7a813101e45a9866279d6d06f4d4f",
"version_major": 2,
"version_minor": 0
},
@@ -530,43 +540,43 @@
" \n",
" \n",
" 0 | \n",
- " так так усе на світі кінчається | \n",
+ " простіть | \n",
"
\n",
" \n",
" 1 | \n",
- " комуністів тільки й є що воєнком та два ротні політруки | \n",
+ " полковник дасть вам проїзні документи до табору | \n",
"
\n",
" \n",
" 2 | \n",
- " уже й убитих чимало | \n",
+ " зараз тільки про це йде мова | \n",
"
\n",
" \n",
" 3 | \n",
- " трупів не закопували | \n",
+ " у мене лопата вартий на килимі до кам'янки вернутися | \n",
"
\n",
" \n",
" 4 | \n",
- " до фастова дісталася з якимось польським обозом | \n",
+ " хотілося взнати про холодний яр про долю товаришів | \n",
"
\n",
" \n",
" 5 | \n",
- " невже то ви були | \n",
+ " а той слухає уважно перепитує | \n",
"
\n",
" \n",
" 6 | \n",
- " при отій купці отої нещасної духовної братії | \n",
+ " чи то образ перемінився в чоловіка чи чоловік був у тім образі | \n",
"
\n",
" \n",
" 7 | \n",
- " вирішуємо напасти на бригаду по дорозі не допустивши до села | \n",
+ " кінь | \n",
"
\n",
" \n",
" 8 | \n",
- " де то хто таке видав аби хлопи купували панські маєтки | \n",
+ " у самців вусики трохи довші а у самок дещо коротші за тіло | \n",
"
\n",
" \n",
" 9 | \n",
- " коні пішли з коноводами в балку | \n",
+ " чорний туман що сповивав усе довкола поволі сірів | \n",
"
\n",
" \n",
""
@@ -585,7 +595,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -601,7 +611,7 @@
"ascii": false,
"bar_format": null,
"colour": null,
- "elapsed": 0.007320880889892578,
+ "elapsed": 0.012672662734985352,
"initial": 0,
"n": 0,
"ncols": null,
@@ -615,7 +625,7 @@
"unit_scale": false
},
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8261bf1a7bd747fb88f7e063c24273d4",
+ "model_id": "c10e3f80cadb49a7951a2a6863af53bf",
"version_major": 2,
"version_minor": 0
},
@@ -634,7 +644,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -668,43 +678,43 @@
" \n",
" \n",
" 0 | \n",
- " я замилувався маневруванням тачанок на полях | \n",
+ " спостерігаючи за їхніми спокійними рухами пригадую роботу чекістів у льоху для розстрілів | \n",
"
\n",
" \n",
" 1 | \n",
- " андрій із бугаєм вилізли на близький горб роздивилися | \n",
+ " сірого били дрючками селяни що упіймали його з парою крадених коней | \n",
"
\n",
" \n",
" 2 | \n",
- " вона нам потрібна як щоденний хліб | \n",
+ " мав лише револьвер далековид і планшет із мапою | \n",
"
\n",
" \n",
" 3 | \n",
- " олесеві ще більше захотілось чаю | \n",
+ " коли еней мене бажа | \n",
"
\n",
" \n",
" 4 | \n",
- " вирішуємо тут поснідати і з годину відпочити бо люди й коні потомлені | \n",
+ " ось і волость | \n",
"
\n",
" \n",
" 5 | \n",
- " воротилову сотню найдужче боявся він дрібного дощу який почав сіятись удосвіта й міг зашкодити | \n",
+ " і посмоктали кісточки | \n",
"
\n",
" \n",
" 6 | \n",
- " люта злість піднялася в душі хлопця при вигляді оцього свого тирана | \n",
+ " скакати високо | \n",
"
\n",
" \n",
" 7 | \n",
- " також цього року в столиці виникла низка профспілкових організацій і був створений робочий клуб | \n",
+ " я стрепенувся і відкрив повіки | \n",
"
\n",
" \n",
" 8 | \n",
- " накидав того літа а вони в наших плавнях затрималися всю січ мені засмерділи | \n",
+ " нападом на кінноту ми себе виявили | \n",
"
\n",
" \n",
" 9 | \n",
- " їздять коло нас | \n",
+ " червоні розгубилися і в безладі закрутилися на місці | \n",
"
\n",
" \n",
""
@@ -723,7 +733,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 16,
"metadata": {
"id": "LwCshNbbeRZR"
},
@@ -737,7 +747,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -788,7 +798,7 @@
"ascii": false,
"bar_format": null,
"colour": null,
- "elapsed": 0.009069681167602539,
+ "elapsed": 0.01583385467529297,
"initial": 0,
"n": 0,
"ncols": null,
@@ -802,7 +812,7 @@
"unit_scale": false
},
"application/vnd.jupyter.widget-view+json": {
- "model_id": "21abcee2f4f6401096ebfcc2b283f704",
+ "model_id": "3ba1ea8f16284564b96209d4ac0fe9b2",
"version_major": 2,
"version_minor": 0
},
@@ -819,7 +829,7 @@
"ascii": false,
"bar_format": null,
"colour": null,
- "elapsed": 0.007071256637573242,
+ "elapsed": 0.006883859634399414,
"initial": 0,
"n": 0,
"ncols": null,
@@ -833,7 +843,7 @@
"unit_scale": false
},
"application/vnd.jupyter.widget-view+json": {
- "model_id": "82e2b6e9482345ba913c5800eab41275",
+ "model_id": "24cf52aaa5534c3d94b2c749af19dd86",
"version_major": 2,
"version_minor": 0
},
@@ -852,7 +862,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 18,
"metadata": {
"id": "aQfneNsmlJI0"
},
@@ -863,7 +873,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -923,7 +933,7 @@
" 'ґ': 34}"
]
},
- "execution_count": 17,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -935,7 +945,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 20,
"metadata": {
"id": "npbIbBoLgaFX"
},
@@ -947,7 +957,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -973,7 +983,7 @@
"37"
]
},
- "execution_count": 19,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -986,7 +996,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 22,
"metadata": {
"id": "ehyUoh9vk191"
},
@@ -999,7 +1009,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1035,18 +1045,18 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 24,
"metadata": {
"id": "A1XApZBAF2zr"
},
"outputs": [],
"source": [
- "repo_name = \"wav2vec2-xls-r-300m-uk\""
+ "repo_name = \"wav2vec2-xls-r-base-uk\""
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 26,
"metadata": {
"id": "kAR0-2KLkopp"
},
@@ -1059,7 +1069,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 27,
"metadata": {
"id": "KYZtoW-tlZgl"
},
@@ -1072,7 +1082,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -1082,7 +1092,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -1109,7 +1119,7 @@
"'/home/robinhad/.cache/huggingface/datasets/downloads/extracted/ee7155196e5d51620d53e48cf58eb693b7839b8ff183604c8bb948d3e0aad92d/cv-corpus-10.0-2022-07-04/uk/clips/common_voice_uk_20907128.mp3'"
]
},
- "execution_count": 26,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -1120,7 +1130,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1149,7 +1159,7 @@
" 'sampling_rate': 48000}"
]
},
- "execution_count": 27,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1160,7 +1170,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 31,
"metadata": {
"id": "rrv65aj7G95i"
},
@@ -1172,7 +1182,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1201,7 +1211,7 @@
" 'sampling_rate': 16000}"
]
},
- "execution_count": 29,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -1212,7 +1222,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 33,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -1237,7 +1247,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "от би була рада\n"
+ "що ж хоч утекти можна вкупі з нею\n"
]
},
{
@@ -1245,7 +1255,7 @@
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@@ -1254,7 +1264,7 @@
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@@ -1272,7 +1282,7 @@
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- "execution_count": 31,
+ "execution_count": 34,
"metadata": {
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@@ -1296,8 +1306,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Target text: от їхало якихось із десять на конях назустріч\n",
- "Input array shape: (73152,)\n",
+ "Target text: троє\n",
+ "Input array shape: (26496,)\n",
"Sampling rate: 16000\n"
]
}
@@ -1312,7 +1322,7 @@
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@@ -1332,7 +1342,7 @@
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@@ -1446,2455 +1456,31 @@
"#common_voice_train = common_voice_train.filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])"
]
},
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "id": "tborvC9hx88e"
- },
- "outputs": [],
- "source": [
- "import torch\n",
- "\n",
- "from dataclasses import dataclass, field\n",
- "from typing import Any, Dict, List, Optional, Union\n",
- "\n",
- "@dataclass\n",
- "class DataCollatorCTCWithPadding:\n",
- " \"\"\"\n",
- " Data collator that will dynamically pad the inputs received.\n",
- " Args:\n",
- " processor (:class:`~transformers.Wav2Vec2Processor`)\n",
- " The processor used for proccessing the data.\n",
- " padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
- " Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
- " among:\n",
- " * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
- " sequence if provided).\n",
- " * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
- " maximum acceptable input length for the model if that argument is not provided.\n",
- " * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
- " different lengths).\n",
- " \"\"\"\n",
- "\n",
- " processor: Wav2Vec2Processor\n",
- " padding: Union[bool, str] = True\n",
- "\n",
- " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
- " # split inputs and labels since they have to be of different lenghts and need\n",
- " # different padding methods\n",
- " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
- " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
- "\n",
- " batch = self.processor.pad(\n",
- " input_features,\n",
- " padding=self.padding,\n",
- " return_tensors=\"pt\",\n",
- " )\n",
- " with self.processor.as_target_processor():\n",
- " labels_batch = self.processor.pad(\n",
- " label_features,\n",
- " padding=self.padding,\n",
- " return_tensors=\"pt\",\n",
- " )\n",
- "\n",
- " # replace padding with -100 to ignore loss correctly\n",
- " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
- "\n",
- " batch[\"labels\"] = labels\n",
- "\n",
- " return batch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "id": "lbQf5GuZyQ4_"
- },
- "outputs": [],
- "source": [
- "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
- ]
- },
{
"cell_type": "code",
"execution_count": 37,
- "metadata": {
- "id": "9Xsux2gmyXso"
- },
+ "metadata": {},
"outputs": [],
"source": [
- "wer_metric = load_metric(\"wer\")\n",
- "cer_metric = load_metric(\"cer\")\n",
- "metrics = [wer_metric, cer_metric]"
+ "!mkdir cached_dataset"
]
},
{
"cell_type": "code",
"execution_count": 38,
- "metadata": {
- "id": "1XZ-kjweyTy_"
- },
+ "metadata": {},
"outputs": [],
"source": [
- "def compute_metrics(pred):\n",
- " pred_logits = pred.predictions\n",
- " pred_ids = np.argmax(pred_logits, axis=-1)\n",
- "\n",
- " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
- "\n",
- " pred_str = processor.batch_decode(pred_ids)\n",
- " # we do not want to group tokens when computing the metrics\n",
- " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
- "\n",
- " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
- " cer = cer_metric.compute(predictions=pred_str, references=label_str)\n",
- "\n",
- " return {\"wer\": wer, \"cer\": cer}"
+ "common_voice_train.save_to_disk(\"cached_dataset/cv_train\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "executionInfo": {
- "elapsed": 9496,
- "status": "ok",
- "timestamp": 1641588938616,
- "user": {
- "displayName": "Yurii Paniv",
- "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
- "userId": "13095662915325887123"
- },
- "user_tz": -120
- },
- "id": "e7cqAWIayn6w",
- "outputId": "b7b20ce9-e1b2-473f-8032-2a75f98dfa9e"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['quantizer.codevectors', 'project_hid.bias', 'project_q.weight', 'quantizer.weight_proj.weight', 'quantizer.weight_proj.bias', 'project_q.bias', 'project_hid.weight']\n",
- "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
- "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
- "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.bias', 'lm_head.weight']\n",
- "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
- ]
- }
- ],
- "source": [
- "from transformers import Wav2Vec2ForCTC\n",
- "\n",
- "model = Wav2Vec2ForCTC.from_pretrained(\n",
- " \"facebook/wav2vec2-xls-r-300m\", \n",
- " attention_dropout=0.3,\n",
- " hidden_dropout=0.3,\n",
- " feat_proj_dropout=0.3,\n",
- " mask_time_prob=0.05,\n",
- " layerdrop=0.3,\n",
- " ctc_loss_reduction=\"mean\", \n",
- " pad_token_id=processor.tokenizer.pad_token_id,\n",
- " vocab_size=len(processor.tokenizer),\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "id": "oGI8zObtZ3V0"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/robinhad/Projects/Speech/wav2vec2-xls-r-ukrainian/.venv/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1618: FutureWarning: The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.Please use the equivalent `freeze_feature_encoder` method instead.\n",
- " warnings.warn(\n"
- ]
- }
- ],
- "source": [
- "model.freeze_feature_extractor()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "id": "KbeKSV7uzGPP"
- },
+ "metadata": {},
"outputs": [],
"source": [
- "from transformers import TrainingArguments\n",
- "\n",
- "training_args = TrainingArguments(\n",
- " output_dir=repo_name,\n",
- " group_by_length=True,\n",
- " per_device_train_batch_size=16,\n",
- " gradient_accumulation_steps=6,\n",
- " eval_accumulation_steps=4,\n",
- " evaluation_strategy=\"steps\",\n",
- " num_train_epochs=100,\n",
- " gradient_checkpointing=True,\n",
- " fp16=True,\n",
- " save_steps=400,\n",
- " eval_steps=400,\n",
- " logging_steps=400,\n",
- " learning_rate=3e-4,\n",
- " warmup_steps=500,\n",
- " save_total_limit=2,\n",
- " report_to=\"tensorboard\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "executionInfo": {
- "elapsed": 11063,
- "status": "ok",
- "timestamp": 1641588949674,
- "user": {
- "displayName": "Yurii Paniv",
- "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
- "userId": "13095662915325887123"
- },
- "user_tz": -120
- },
- "id": "rY7vBmFCPFgC",
- "outputId": "2e89d5ea-5b25-44bf-8492-a6220b0b1c38"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using cuda_amp half precision backend\n"
- ]
- }
- ],
- "source": [
- "from transformers import Trainer\n",
- "\n",
- "trainer = Trainer(\n",
- " model=model,\n",
- " data_collator=data_collator,\n",
- " args=training_args,\n",
- " compute_metrics=compute_metrics,\n",
- " train_dataset=common_voice_train,\n",
- " eval_dataset=common_voice_test,\n",
- " tokenizer=processor.feature_extractor,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 409
- },
- "id": "9fRr9TG5pGBl",
- "outputId": "c2a7c797-326c-4bd2-b167-9d2f41d77def"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n",
- "/home/robinhad/Projects/Speech/wav2vec2-xls-r-ukrainian/.venv/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
- " warnings.warn(\n",
- "***** Running training *****\n",
- " Num examples = 11463\n",
- " Num Epochs = 100\n",
- " Instantaneous batch size per device = 16\n",
- " Total train batch size (w. parallel, distributed & accumulation) = 96\n",
- " Gradient Accumulation steps = 6\n",
- " Total optimization steps = 11900\n"
- ]
- },
- {
- "data": {
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- "version_minor": 0
- },
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- " 0%| | 0/11900 [00:00, ?it/s]"
- ]
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- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n",
- "***** Running Evaluation *****\n",
- " Num examples = 6783\n",
- " Batch size = 8\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'loss': 5.4923, 'learning_rate': 0.0002388, 'epoch': 3.36}\n"
- ]
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- "metadata": {},
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- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Saving model checkpoint to wav2vec2-xls-r-300m-uk/checkpoint-400\n",
- "Configuration saved in wav2vec2-xls-r-300m-uk/checkpoint-400/config.json\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'eval_loss': 3.336439609527588, 'eval_wer': 1.0, 'eval_cer': 1.0, 'eval_runtime': 211.1144, 'eval_samples_per_second': 32.13, 'eval_steps_per_second': 4.017, 'epoch': 3.36}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Model weights saved in wav2vec2-xls-r-300m-uk/checkpoint-400/pytorch_model.bin\n",
- "Feature extractor saved in wav2vec2-xls-r-300m-uk/checkpoint-400/preprocessor_config.json\n",
- "Deleting older checkpoint [wav2vec2-xls-r-300m-uk/checkpoint-6800] due to args.save_total_limit\n",
- "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n",
- "***** Running Evaluation *****\n",
- " Num examples = 6783\n",
- " Batch size = 8\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- "Saving model checkpoint to wav2vec2-xls-r-300m-uk/checkpoint-800\n",
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- "Model weights saved in wav2vec2-xls-r-300m-uk/checkpoint-800/pytorch_model.bin\n",
- "Feature extractor saved in wav2vec2-xls-r-300m-uk/checkpoint-800/preprocessor_config.json\n",
- "Deleting older checkpoint [wav2vec2-xls-r-300m-uk/checkpoint-7200] due to args.save_total_limit\n",
- "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n",
- "***** Running Evaluation *****\n",
- " Num examples = 6783\n",
- " Batch size = 8\n"
- ]
- },
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- "name": "stdout",
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- "Model weights saved in wav2vec2-xls-r-300m-uk/checkpoint-1200/pytorch_model.bin\n",
- "Feature extractor saved in wav2vec2-xls-r-300m-uk/checkpoint-1200/preprocessor_config.json\n",
- "Deleting older checkpoint [wav2vec2-xls-r-300m-uk/checkpoint-400] due to args.save_total_limit\n",
- "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n",
- "***** Running Evaluation *****\n",
- " Num examples = 6783\n",
- " Batch size = 8\n"
- ]
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- "Deleting older checkpoint [wav2vec2-xls-r-300m-uk/checkpoint-10800] due to args.save_total_limit\n",
- "\n",
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- "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
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- "Dropping the following result as it does not have all the necessary fields:\n",
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- "\n",
- "loading weights file wav2vec2-xls-r-300m-uk/checkpoint-5600/pytorch_model.bin\n",
- "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n",
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- "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n",
- "loading feature extractor configuration file wav2vec2-xls-r-300m-uk/preprocessor_config.json\n",
- "Feature extractor Wav2Vec2FeatureExtractor {\n",
- " \"do_normalize\": true,\n",
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- " \"padding_value\": 0.0,\n",
- " \"return_attention_mask\": true,\n",
- " \"sampling_rate\": 16000\n",
- "}\n",
- "\n",
- "Didn't find file wav2vec2-xls-r-300m-uk/tokenizer.json. We won't load it.\n",
- "loading file wav2vec2-xls-r-300m-uk/vocab.json\n",
- "loading file wav2vec2-xls-r-300m-uk/tokenizer_config.json\n",
- "loading file wav2vec2-xls-r-300m-uk/added_tokens.json\n",
- "loading file wav2vec2-xls-r-300m-uk/special_tokens_map.json\n",
- "loading file None\n",
- "Adding to the vocabulary\n",
- "Adding to the vocabulary\n"
- ]
- }
- ],
- "source": [
- "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n",
- "model = Wav2Vec2ForCTC.from_pretrained(repo_name + \"/checkpoint-11200\").to(\"cuda\")\n",
- "processor = Wav2Vec2Processor.from_pretrained(repo_name)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jD7TZ1YS3S_K"
- },
- "source": [
- "\n",
- "Now, we will just take the first example of the test set, run it through the model and take the `argmax(...)` of the logits to retrieve the predicted token ids."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "pax07TnL3WZn"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
- ]
- }
- ],
- "source": [
- "audio_id = 10\n",
- "\n",
- "input_dict = processor(common_voice_test[\"input_values\"], return_tensors=\"pt\", padding=True)\n",
- "\n",
- "logits = model(input_dict.input_values.to(\"cuda\")).logits\n",
- "\n",
- "pred_ids = torch.argmax(logits, dim=-1)[audio_id]\n",
- "\n",
- "common_voice_test_transcription = load_dataset(\"common_voice\", \"uk\", split=\"test\")\n",
- "\n",
- "print(\"Prediction:\")\n",
- "print(processor.decode(pred_ids))\n",
- "\n",
- "print(\"\\nReference:\")\n",
- "print(common_voice_test_transcription[audio_id][\"sentence\"].lower())"
+ "common_voice_test.save_to_disk(\"cached_dataset/cv_test\")"
]
}
],