{ "cells": [ { "cell_type": "code", "execution_count": 61, "id": "23346075", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3c5c662a10d94dc7a85a1e87c0f0a6a5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading dataset from disk: 0%| | 0/138 [00:00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds[0][\"audio\"]" ] }, { "cell_type": "code", "execution_count": 17, "id": "0072a6f1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datasets.features._torchcodec.AudioDecoder" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(ds[0][\"audio\"])" ] }, { "cell_type": "code", "execution_count": 6, "id": "2ed3815e", "metadata": {}, "outputs": [], "source": [ "sample = ds[1000]\n", "audio = sample['audio']\n", "decoded = audio.get_all_samples()\n", "\n", "waveform = decoded.data\n", "sampling_rate = decoded.sample_rate" ] }, { "cell_type": "code", "execution_count": 7, "id": "9084d3cd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Audio, display\n", "\n", "display(Audio(waveform.numpy(), rate=sampling_rate))" ] }, { "cell_type": "code", "execution_count": 8, "id": "63d1ce80", "metadata": {}, "outputs": [], "source": [ "from datasets import Audio\n", "\n", "ds = ds.cast_column(\"audio\", Audio(decode=False))" ] }, { "cell_type": "code", "execution_count": 56, "id": "f7dc8b55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Audio(sampling_rate=None, decode=True, num_channels=None, stream_index=None)\n" ] } ], "source": [ "print(ds.features[\"audio\"])" ] }, { "cell_type": "code", "execution_count": 11, "id": "10c3697b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'english': 'Local radio station in Russia cancels interview with LGBT activists after threats to editor'}\n", "{'english': 'Activists in Madrid protest LGBT rights violations in Russia'}\n", "{'english': 'Echo of Moscow in Yaroslavl, a local affiliate of Echo of Moscow, Russia’s oldest independent radio network, cancelled an interview with LGBT activists after receiving homophobic threats, the station’s editor Lyudmila Shabuyeva said in a Facebook post:'}\n", "{'english': 'Yesterday we received threats against our guests and ourselves if we proceed with our talk show about LGBT.'}\n" ] } ], "source": [ "from collections import defaultdict\n", "\n", "aligned = defaultdict(dict)\n", "\n", "for row in ds:\n", " sid = row[\"text_id\"][1:]\n", " lang = row[\"language\"]\n", "\n", " aligned[sid][lang] = row[\"text\"]\n", "\n", " if len(aligned) < 5:\n", " print(aligned[sid])" ] }, { "cell_type": "code", "execution_count": 12, "id": "9726402e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5249" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(aligned)" ] }, { "cell_type": "code", "execution_count": 17, "id": "069d5d12", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({'hausa': 15000, 'yoruba': 15000, 'igbo': 14000, 'english': 8000})" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import Counter\n", "\n", "Counter(ds[\"language\"])" ] }, { "cell_type": "code", "execution_count": 18, "id": "8b9b839b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['audio', 'user_id', 'language', 'text_id', 'text', 'duration', 'recorded_at', 'original_sample_rate', 'silence_ratio', 'snr_db', 'speech_rate', 'volume_db', 'split'],\n", " num_rows: 52000\n", "})" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 19, "id": "6ee419e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==== hausa ====\n", "HNX_0001 Welsh AMs ta damu game da 'yadda ake zama kamar wawaye'\n", "HNX_0002 Akwai tsoron haɗari a tsakanin wasu AMs a bisa shawarar canza muƙaminsu zuwa MWPs (Mamban Majalisar Dokoki ta Welsh).\n", "HNX_0003 Abin ya tashi ne saboda shirye-shirye na canza sunan taron zuwa Majalisar Dokoki ta Welsh.\n", "HNX_0004 AM a ɓangaren siyasa sun damu ƙwarai don zai iya haifar da ba’a.\n", "HNX_0005 Wani AM na Jam’iyyar Labour ya ce ƙungiyar ta damu \"yana rauji da Twp da kuma Pwp.\"\n", "\n", "==== yoruba ====\n", "YTR_0001 Ní àwọn agbègbè kan ní àwọn ìgbèríko Bujumbura, olúìlú Burundi, ẹ̀kún-omi máa ń ṣẹlẹ̀ lóòrèkóòrè tí ìjọba kò sì rí ọ̀nà-àbáyọ alálòpẹ́ ṣe síi.\n", "YTR_0002 Ó ba ni lọ́kàn jẹ́ pé, èyí kìí ṣe ìgbà àkọ́kọ́ tí wọ́n máa kọjú irúfẹ́ àjálù bẹ́ẹ̀: ìdúnkòokò omíyalé ní Gatumba bẹ̀rẹ̀ ní ọdún 2016.\n", "YTR_0003 Àwọn ọ̀ràn tí wọ́n ń wáyé léraléra náà ti dá ìṣẹ̀lẹ̀ ìṣínípò tipátipá sílẹ̀ fún àwọn ará agbègbè náà.\n", "YTR_0004 Àwọn èèyàn kan ń gbé ní ẹ̀bá-ọ̀nà náà, a ṣẹ̀ṣẹ̀ kúrò ní àwọn àyè ìpéjọ náà ni.\n", "YTR_0005 Àwọn yòókù péjò sí àyè tí ó wà fún ìgbà díẹ̀ ní àwọn ìgbèríko ibi tí omíyalé wà, wọ́n ń dúró de ibòmíràn tí wọn yóò tèdó sí ṣùgbọ́n tí àwọn aláṣẹ kò tíì fi ìdí rẹ̀ múlẹ̀.\n", "\n", "==== english ====\n", "EMD_0001 Local radio station in Russia cancels interview with LGBT activists after threats to editor\n", "EMD_0002 Activists in Madrid protest LGBT rights violations in Russia\n", "EMD_0003 Echo of Moscow in Yaroslavl, a local affiliate of Echo of Moscow, Russia’s oldest independent radio network, cancelled an interview with LGBT activists after receiving homophobic threats, the station’s editor Lyudmila Shabuyeva said in a Facebook post:\n", "EMD_0004 Yesterday we received threats against our guests and ourselves if we proceed with our talk show about LGBT.\n", "EMD_0005 I’m cancelling the show.\n", "\n", "==== igbo ====\n", "ITR_0001 Ụfọdụ ndị agbataobi bi na mpụga Bujumbura, bụ isi obodo Burundi, iju mmiri na-abaịakarị ebe ahụ mana ndị gọọmenti anaghị achọ ụzọ a ga-esi gbochie ya gbam gbam.\n", "ITR_0002 Ọ dị mwute, n'ihi na nke a abụghị izizi ha hụtara ụdị ọdachi ahụ: iyi egwu nke ide mmiri na Gatumba malitere na 2016.\n", "ITR_0003 Ọgba aghara ugboro ugboro ndị a ebutela ọnọdụ ịchịpụ ndị bi n'ime obodo n'ike.\n", "ITR_0004 Ụfọdụ ndị mmadụ bi n'akụkụ okporo ụzọ, anyị n'ebe ọgbakọ ahụ pụọ na nsoso a.\n", "ITR_0005 Ndị ọzọ na-ezukọ n'ebe ndị njem na-agafe na mpụga mpaghara ebe ide mmiri ahụ kpuchiri, na-eche ka a kpọfee ha n'ebe ọzọ ndị ọchịchị n'ekpebibeghị.\n" ] } ], "source": [ "languages = set(ds[\"language\"])\n", "\n", "for lang in languages:\n", " print(\"\\n====\", lang, \"====\")\n", "\n", " count = 0\n", " for row in ds:\n", " if row[\"language\"] == lang:\n", " print(row[\"text_id\"], row[\"text\"])\n", " count += 1\n", " if count == 5:\n", " break" ] }, { "cell_type": "code", "execution_count": 20, "id": "65ae682d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "Shared ID: MD_0001\n", "english : Local radio station in Russia cancels interview with LGBT activists after threats to editor\n", "hausa : Wani gidan rediyo a ƙasar Rasha ya soke ganawar da ya shirya da masu rajin kare haƙƙin masu auren jinsi bayan an yi wa editan barazana\n", "igbo : Ụlọọrụ redio dị na Russia kagburu ajụjụọnụ ya na ndị n'akwado ikike LGBT mgbe ha yichara onye nchịkọta akụkọ egwu.\n", "yoruba : Ilé-iṣẹ́ Agbóhùnsáfẹ́fẹ́ Ìbílẹ̀ Fagilé Ìfọ̀rọ̀wọ́rọ̀ pẹ̀lú àwọn ajìjàǹgbara LGBT lẹ́yìn ìhalẹ̀mọ́ sí olóòtú\n", "================================================================================\n", "Shared ID: MD_0002\n", "english : Activists in Madrid protest LGBT rights violations in Russia\n", "hausa : Masu fafutukar kare haƙƙi a Madrid sun yi ƙyamaci take haƙƙin masu auren jinsi a Rasha\n", "igbo : Ndị nkwado na Madrid na-eme ngagharị iwe megide mmebi ikike LGBT na Russia.\n", "yoruba : Àwọn ajìjàǹgbara ní Madrid fi ẹ̀hónú hàn lórí ìrúfin ẹ̀tọ́ LGBT ní Russia\n", "================================================================================\n", "Shared ID: MD_0003\n", "english : Echo of Moscow in Yaroslavl, a local affiliate of Echo of Moscow, Russia’s oldest independent radio network, cancelled an interview with LGBT activists after receiving homophobic threats, the station’s editor Lyudmila Shabuyeva said in a Facebook post:\n", "hausa : Gidan rediyon Eecho of Moscow a Yyaroslaɓi, wani yanki na Echo of Moscow, wanda shi ne mafi tsufan gidan rediyo mai zaman kansa a Rasha, ya soke ganawa da masu rajin kare haƙƙin 'yan luwaɗi bayan ya sami wata barazana, editan gidan rediyon, Lyudmila Shabuyeɓa ne ya bayyana hakan a shafinsa na Fesbuk:\n", "igbo : Echo nke Moscow na Yaroslavl, òtù onye ụlọọrụ nke Echo nke Moscow, netwọk redio nọọrọ onwe ya kacha ochie na Russia, kagburu ajụjụọnụ ya na ndị nkwado LGBT mgbe ha natara iyi egwu ịkpọasị ndị mmekọ LGBT, onye nchịkọta akụkọ nke ụlọọrụ ahụ bụ Lyudmila Shabuyeva kwuru na mbipụta ya na Facebook:\n", "yoruba : Ilé-iṣẹ́ Echo ti Moscow ní Yaroslavl tí ó jẹ́ ẹ̀ka ilé-iṣẹ́ Agbóhùnsáfẹ́fẹ́ Echo ti Moscow ìsopọ̀ tí ó ti dá dúró fún ìgbà pípẹ́ ní Russia fagi lé ìfọ̀rọ̀wọ́rọ̀ pẹ̀lú àwọn ajìjàǹgbara LGBT lẹ́yìn tí wọ́n gba ìhalẹ̀ láti ọ̀dọ̀ àwọn tí ó kórìíra-ìbálòpọ̀-láàárín-akọ-àti-akọ, olóòtú ilé-iṣẹ́ náà Lyudmila Shabuyeva sọ̀rọ̀ nínú àkọsílẹ̀ Facebook kan:\n", "================================================================================\n", "Shared ID: MD_0004\n", "english : Yesterday we received threats against our guests and ourselves if we proceed with our talk show about LGBT.\n", "hausa : A jiya mun samu barazana da ta shafi baƙinmu da mu kanmu idan muka cigaba da shirinmu na hirarraki game da 'yan luwaɗi.\n", "igbo : Ụnyaahụ, anyị natara iyi egwu megide ndịọbịa anyị na onwe anyị ma ọ bụrụ na anyị gaa n'ihu mee ihe ngosi anyị gbasara LGBT.\n", "yoruba : Ní àná, a gba ìhàlẹ̀ kan tí ó ń lérí mọ́ àwa àti àwọn àlejòo wa tí a bá tẹ̀síwajú pẹ̀lú ètò ìfọ̀rọ̀wọ́rọ̀ọ wa nípa LGBT.\n", "================================================================================\n", "Shared ID: MD_0005\n", "english : I’m cancelling the show.\n", "hausa : Ina rufe wannan shirin.\n", "igbo : A na m akagbu ihe ngosi a.\n", "yoruba : Mò ń fagi lé ètò náà.\n", "================================================================================\n", "Shared ID: MD_0006\n", "english : According to independent newspaper Novaya Gazeta, the show featuring Yaroslavl’s LGBT activists was scheduled to air in the early morning of Wednesday, January 23.\n", "hausa : Kamar yadda wata jarida mai zaman kanta ta Noɓaya Gazeta ta bayyana, shirin da yake kawo masu rajin kare haƙƙin 'yan luwaɗi na Yaroslaɓi, an yi shirin kawo shi ne a safiyar Laraba, 23 ga Janairu.\n", "igbo : Dịka akwụkwọozi nọọrọ onwe ya bụ Novaya Gazeta si kwuo, a haziri ka ihe ngosi ahụ nke ndị na-akwado LGBT nke Yaroslavl gosipụta n'isi ụtụtụ Wenezdee, Jenụwarị 23.\n", "yoruba : Gẹ́gẹ́ bí ìwé ìròyìn tí ò sí ní abẹ́ ìṣàkóso ìjọba Novaya Gazeta ti ṣe sọ, ó yẹ kí ètò náà tí yóò ṣe àfihàn àwọn ajìjàǹgbara LGBT ní Yaroslavl wáyé ní òwúrọ̀ Ọjọ́rú 23, ní oṣù Ṣẹẹrẹ.\n", "================================================================================\n", "Shared ID: MD_0007\n", "english : The same activists had recently picketed the town’s main square to protest against the persecution of gay people in Russia, notably in the republic of Chechnya.\n", "hausa : Su dai waɗannan 'yan rajin kare haƙƙin sun mamaye babban dandalin taro na ƙauyen domin su yi zanga-zangar nuna ƙin jinin yadda ake ƙuntatawa masu auren jinsi a Rasha, musamman a tarayyar Chechnya.\n", "igbo : Ndị òtù nkwado ahụ mere ngagharị iwe n'ámá obodo n'oge n'adịbeghị anya megide mkpagbu a na-akpagbu ndị susupe bi na Russịa, ọkachasị na mba Chechnya\n", "yoruba : Àwọn ajìjàǹgbara yìí kan náà ṣẹ̀ṣẹ̀ fi ẹ̀hónú hàn ní gbàgede ìlú láti tako ìfìyàjẹ àwọn aṣe-ìbálòpọ̀-akọ-àti-akọ ní Russia, pàápàá jùlọ ní orílẹ̀ Chechnya.\n", "================================================================================\n", "Shared ID: MD_0008\n", "english : The station invited them to be interviewed about the protest and their experience of being openly gay in provincial Russia.\n", "hausa : Gidan rediyon ya gayyace su domin a gana da su game da zanga-zangar da kuma irin ƙwarewarsu wajen iya nuna cewa su masu auren jinsi ne a fili a cikin lardin Rasha.\n", "igbo : Ụlọọrụ redio ahụ kpọrọ ha òkù ka a gbaa ha ajụjụọnụ gbasara ngagharị iwe ha na ahụmịhe ha nwere ịbụ ndị na-edina ụdị ha n'ihu ọha na mpaghara Russia.\n", "yoruba : Ilé-iṣẹ́ agbóhùnsáfẹ́fẹ́ náà pè wọ́n láti fi ọ̀rọ̀ wá wọn lẹ́nu wò nípa ìfẹ̀hónúhàn àti ìrírí wọn fún jíjáde sí gbangba gẹ́gẹ́ bíi aṣe-ìbálòpọ̀-akọ-àti-akọ ní ẹ̀ka-ìlúu Russia.\n", "================================================================================\n", "Shared ID: MD_0009\n", "english : Shabuyeva’s initial announcement of the show attracted a torrent of homophobic abuse in the comments, including some from local officials, but that didn’t put her off, she told Novaya Gazeta.\n", "hausa : Sanarwar shirin Shabuyeɓa ta farko ta janyo ruwan zage-zagen ƙin jinin 'yan luwaɗi a wajen mayar da martani, da wasu ma daga mahukuntan cikin gida, amma wannan bai dakatar da ita ba, kamar yadda ta bayyanawa Noɓaya Gazeta.\n", "igbo : Nkwupụta mbụ Shabuyeva mere gbasara ihe ngosi ahụ dọtara ọtụtụ mkparị ndị kpọrọ ndị mmekọ nwoke na nwoke asị na ngalaba nkwupụta, ndị a, gụnyere ụfọdụ n'ime ndịọrụ obodo, mana nke ahụ emeghị ka ọ kwụsị, ka ọ gwara Novaya Gazeta.\n", "yoruba : Ìkéde Shebuyeva àkọ́kọ́ nípa ètò náà fa ẹgbẹlẹmùkù èébú ìkórìíra-ìbálòpọ̀-láàárín-akọ́-àti-akọ nínú àwọn èsì, tí èsì sì wá láti ọ̀dọ̀ àwọn òṣìṣẹ́ ọba kan bákan náà, ṣùgbọ́n ìyẹn ò mú ọkàn-an rẹ̀ kúrò, ó sọ fún Novaya Gazeta.\n", "================================================================================\n", "Shared ID: MD_0010\n", "english : However, late in the night before the show, Shabuyeva says, a stranger called her on the phone from an unidentified number and told her that if she were to proceed with the scheduled programming, her guests would be met outside the studio with baseball bats.\n", "hausa : Amma duk da haka, a cikin dare kafin fara shirin, Shabuyeɓa ta ce, wani wanda ba ta sani ba ya kira ta a waya da wata ɓoyayyiyar lamba kuma ya gaya mata cewa idan har ta cigaba da wannan shirin da aka shirya, to baƙinta za su gamu da gamonsu in sun fito daga sitidiyon.\n", "igbo : N'agbanyeghị, n'abalị tupu emume ngosi ahụ, Shabuyeva kwuru na otu onye ọ n'amaghị kpọrọ ya n'ekwentị site na nọmba nzuzo were gwa ya na ọ bụrụ na ọ ga n'ihu n'emume ahụ a haziri, a ga-eji osisi bọọlụ zute ndịọbịa ya n'èzí ụlọọrụ ya.\n", "yoruba : Ṣùgbọ́n ní alẹ́ ọjọ́ tí ètò náà ku ọ̀la, Shabuyeva sọ̀rọ̀ pé òun gba ìpè àjèjì láti orí òpó ẹ̀rọ-ìbánisọ̀rọ̀ kan tí kò ṣe é dámọ̀, ẹni náà sì sọ fún òun pé bí òun bá tẹ̀síwajú pẹ̀lú ètò náà bí ó ṣe ti pinnu, wọ́n máa fi igi ìgbábọ́ọ̀lù-afọwọ́jù pàdé àwọn àlejò rẹ ní ìta ilé-iṣẹ́ náà.\n" ] } ], "source": [ "from collections import defaultdict\n", "\n", "groups = defaultdict(dict)\n", "\n", "for row in ds:\n", " shared_id = row[\"text_id\"][1:] # remove E/Y/I/H\n", "\n", " groups[shared_id][row[\"language\"]] = row[\"text\"]\n", "\n", "# Print first 10 groups\n", "for i, (k, v) in enumerate(groups.items()):\n", " print(\"=\" * 80)\n", " print(\"Shared ID:\", k)\n", "\n", " for lang, text in v.items():\n", " print(f\"{lang:8}: {text}\")\n", "\n", " if i == 9:\n", " break" ] }, { "cell_type": "code", "execution_count": 25, "id": "00de7e8c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['audio', 'user_id', 'language', 'text_id', 'text', 'duration', 'recorded_at', 'original_sample_rate', 'silence_ratio', 'snr_db', 'speech_rate', 'volume_db', 'split'],\n", " num_rows: 52000\n", "})" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 64, "id": "371fe3d2", "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "\n", "aligned = defaultdict(dict)\n", "\n", "for i in range(len(ds)):\n", " row = ds[i]\n", " shared_id = row[\"text_id\"][1:]\n", "\n", " if row[\"language\"] == \"english\":\n", " aligned[shared_id][\"idx_en\"] = i\n", " aligned[shared_id][\"text_en\"] = row[\"text\"]\n", "\n", " elif row[\"language\"] == \"yoruba\":\n", " aligned[shared_id][\"idx_yo\"] = i\n", " aligned[shared_id][\"text_yo\"] = row[\"text\"]\n", "\n", "rows = [\n", " {\n", " \"idx_en\": d[\"idx_en\"],\n", " \"idx_yo\": d[\"idx_yo\"],\n", " \"text_en\": d[\"text_en\"],\n", " \"text_yo\": d[\"text_yo\"],\n", " }\n", " for d in aligned.values()\n", " if \"idx_en\" in d and \"idx_yo\" in d\n", " ]" ] }, { "cell_type": "code", "execution_count": 65, "id": "66cfe475", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "500" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(rows)" ] }, { "cell_type": "code", "execution_count": 66, "id": "d9821898", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['idx_en', 'idx_yo', 'text_en', 'text_yo'],\n", " num_rows: 500\n", "})" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datasets import Dataset, Features, Value, Sequence\n", "translation_ds = Dataset.from_list(rows)\n", "translation_ds" ] }, { "cell_type": "code", "execution_count": 69, "id": "a86342c9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "48b14fe935744d9186016bf6db4b28ad", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Saving the dataset (0/1 shards): 0%| | 0/500 [00:00\n", " \n", " Your browser does not support the audio element.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "=== Audio and Text Summary ===\n", "Audio shape: (1, 669768)\n", "Sampling rate: 48000 Hz\n", "English: There are several women in this group, including Nigerian Ola Orekunrin, a physician and the founder of the first air ambulance company in West Africa.\n", "Yoruba: Ọ̀gọ̀ọ̀rọ̀ àwọn obìnrin ni wọ́n wà nínú ẹgbẹ́ yìí, lára wọn ni Ọlá Ọ̀rẹ́kunrin tíì ṣe ọmọ orílẹ̀ èdè Nàìjíríà, oníṣègùn àti olùdásílẹ̀ ilé iṣẹ́ ọkọ̀ aláìsàn òfurufú àkọ́kọ́ ní ìwọ̀-oòrùn Áfíríkà.\n" ] } ], "source": [ "# Function to play audio from dataset using index from translation dataset\n", "def play_audio_from_translation_index(index=0):\n", " \"\"\"\n", " Play audio from the main dataset using index from translation dataset\n", " \"\"\"\n", " # Get the translation sample\n", " if index >= len(translation_ds):\n", " print(f\"Index {index} out of range. Dataset has {len(translation_ds)} entries.\")\n", " return None, None\n", "\n", " row = translation_ds[index]\n", " idx_en = row['idx_yo'] # Get the index from translation dataset\n", " \n", " # Get the audio sample using the index\n", " sample = ds[idx_en]\n", " audio = sample['audio']\n", "\n", " # Decode audio\n", " decoded = audio.get_all_samples()\n", " waveform = decoded.data\n", " sampling_rate = decoded.sample_rate\n", "\n", " # Convert to numpy array for playback\n", " if hasattr(waveform, 'numpy'):\n", " audio_data = waveform.numpy()\n", " else:\n", " audio_data = waveform\n", "\n", " # Play audio\n", " print(f\"Playing audio sample {idx_en} (from translation index {index})\")\n", " print(f\"Sampling rate: {sampling_rate} Hz\")\n", " print(f\"Audio duration: {len(audio_data[0]) / sampling_rate:.2f} seconds\")\n", "\n", " # Display audio in Jupyter notebook\n", " display(Audio(audio_data, rate=sampling_rate))\n", "\n", " return audio_data, sampling_rate\n", "\n", "# Function to get text from translation dataset\n", "def get_translation_text(index=0):\n", " \"\"\"\n", " Get text from translation dataset at given index\n", " \"\"\"\n", " if index >= len(translation_ds):\n", " print(f\"Index {index} out of range. Dataset has {len(translation_ds)} entries.\")\n", " return None\n", "\n", " row = translation_ds[index]\n", " print(f\"English text: {row['text_en']}\")\n", " print(f\"Yoruba text: {row['text_yo']}\")\n", "\n", " return row['text_en'], row['text_yo']\n", "\n", "# Example usage\n", "if __name__ == \"__main__\":\n", " # Get first sample from translation dataset\n", " print(\"=== Translation Dataset Text ===\")\n", " english_text, yoruba_text = get_translation_text(1000)\n", "\n", " print(\"\\n=== Main Dataset Audio ===\")\n", " audio_data, sampling_rate = play_audio_from_translation_index(1000)\n", "\n", " print(\"\\n=== Audio and Text Summary ===\")\n", " if audio_data is not None:\n", " print(f\"Audio shape: {audio_data.shape}\")\n", " print(f\"Sampling rate: {sampling_rate} Hz\")\n", " print(f\"English: {english_text}\")\n", " print(f\"Yoruba: {yoruba_text}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "6a92072f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "53f37c6b", "metadata": {}, "source": [ "# QA data cleaning" ] }, { "cell_type": "code", "execution_count": 47, "id": "c2ff1500", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset({\n", " features: ['question', 'answer'],\n", " num_rows: 200\n", "})\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "da35e3585caf49ddb4eb84ff74318418", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Saving the dataset (0/1 shards): 0%| | 0/200 [00:00\n", " \n", " Your browser does not support the audio element.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Audio, display\n", "\n", "display(Audio(waveform.numpy(), rate=sampling_rate))" ] }, { "cell_type": "code", "execution_count": 4, "id": "90e59391", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9d7de5bbf28543ba9e8f1cc644aa1ac8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading weights: 0%| | 0/876 [00:00 \u001b[39m\u001b[32m12\u001b[39m model = Qwen2AudioForConditionalGeneration.from_pretrained(\n\u001b[32m 13\u001b[39m model_path,\n\u001b[32m 14\u001b[39m torch_dtype=torch.bfloat16,\n\u001b[32m 15\u001b[39m device_map=\u001b[33m\"auto\"\u001b[39m,\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/audio llm/audiollm-env/lib/python3.12/site-packages/transformers/modeling_utils.py:4296\u001b[39m, in \u001b[36mPreTrainedModel.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, fusion_config, disable_mmap, *model_args, **kwargs)\u001b[39m\n\u001b[32m 4278\u001b[39m \u001b[38;5;66;03m# Finalize model weight initialization\u001b[39;00m\n\u001b[32m 4279\u001b[39m load_config = LoadStateDictConfig(\n\u001b[32m 4280\u001b[39m pretrained_model_name_or_path=pretrained_model_name_or_path,\n\u001b[32m 4281\u001b[39m ignore_mismatched_sizes=ignore_mismatched_sizes,\n\u001b[32m (...)\u001b[39m\u001b[32m 4294\u001b[39m disable_mmap=disable_mmap,\n\u001b[32m 4295\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m4296\u001b[39m loading_info, disk_offload_index = \u001b[30;43mcls\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_load_pretrained_model\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstate_dict\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcheckpoint_files\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mload_config\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 4297\u001b[39m loading_info = \u001b[38;5;28mcls\u001b[39m._finalize_model_loading(model, load_config, loading_info)\n\u001b[32m 4298\u001b[39m model.eval() \u001b[38;5;66;03m# Set model in evaluation mode to deactivate Dropout modules by default\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/audio llm/audiollm-env/lib/python3.12/site-packages/transformers/modeling_utils.py:4426\u001b[39m, in \u001b[36mPreTrainedModel._load_pretrained_model\u001b[39m\u001b[34m(model, state_dict, checkpoint_files, load_config, expected_keys)\u001b[39m\n\u001b[32m 4423\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 4424\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNeither a state dict nor checkpoint files were found.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m4426\u001b[39m loading_info, disk_offload_index = \u001b[30;43mconvert_and_load_state_dict_in_model\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 4427\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 4428\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstate_dict\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmerged_state_dict\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 4429\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mload_config\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mload_config\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 4430\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtp_plan\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mtp_plan\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 4431\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mdisk_offload_index\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdisk_offload_index\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 4432\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 4434\u001b[39m \u001b[38;5;66;03m# finally close all opened file pointers\u001b[39;00m\n\u001b[32m 4435\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m all_pointer:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/audio llm/audiollm-env/lib/python3.12/site-packages/transformers/core_model_loading.py:1449\u001b[39m, in \u001b[36mconvert_and_load_state_dict_in_model\u001b[39m\u001b[34m(model, state_dict, load_config, tp_plan, disk_offload_index)\u001b[39m\n\u001b[32m 1447\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m first_param_name, mapping \u001b[38;5;129;01min\u001b[39;00m tqdm(param_name_to_load.items(), desc=\u001b[33m\"\u001b[39m\u001b[33mLoading weights\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 1448\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1449\u001b[39m realized_value = \u001b[30;43mmapping\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mconvert\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1450\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mfirst_param_name\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1451\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1452\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1453\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mhf_quantizer\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mhf_quantizer\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1454\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mloading_info\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mloading_info\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1455\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1456\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m target_name, param \u001b[38;5;129;01min\u001b[39;00m realized_value.items():\n\u001b[32m 1457\u001b[39m param = param[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(param, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m param\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/audio llm/audiollm-env/lib/python3.12/site-packages/transformers/core_model_loading.py:819\u001b[39m, in \u001b[36mWeightRenaming.convert\u001b[39m\u001b[34m(self, layer_name, model, config, hf_quantizer, loading_info)\u001b[39m\n\u001b[32m 809\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mconvert\u001b[39m(\n\u001b[32m 810\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 811\u001b[39m layer_name: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 817\u001b[39m \u001b[38;5;66;03m# Collect the tensors here - we use a new dictionary to avoid keeping them in memory in the internal\u001b[39;00m\n\u001b[32m 818\u001b[39m \u001b[38;5;66;03m# attribute during the whole process\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m819\u001b[39m collected_tensors = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mmaterialize_tensors\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 821\u001b[39m \u001b[38;5;66;03m# Perform renaming op (for a simple WeightRenaming, `self.source_patterns` and `self.target_patterns` can\u001b[39;00m\n\u001b[32m 822\u001b[39m \u001b[38;5;66;03m# only be of length 1, and are actually the full key names - we also have only 1 single related tensor)\u001b[39;00m\n\u001b[32m 823\u001b[39m target_key = \u001b[38;5;28mself\u001b[39m.target_patterns[\u001b[32m0\u001b[39m]\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/audio llm/audiollm-env/lib/python3.12/site-packages/transformers/core_model_loading.py:783\u001b[39m, in \u001b[36mWeightTransform.materialize_tensors\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 781\u001b[39m \u001b[38;5;66;03m# Async loading\u001b[39;00m\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tensors[\u001b[32m0\u001b[39m], Future):\n\u001b[32m--> \u001b[39m\u001b[32m783\u001b[39m tensors = [future.result() \u001b[38;5;28;01mfor\u001b[39;00m future \u001b[38;5;129;01min\u001b[39;00m tensors \u001b[38;5;28;01mif\u001b[39;00m \u001b[30;43mfuture\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mresult\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[32m 784\u001b[39m \u001b[38;5;66;03m# Sync loading\u001b[39;00m\n\u001b[32m 785\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(tensors[\u001b[32m0\u001b[39m]):\n", "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/concurrent/futures/_base.py:451\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m 449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.__get_result()\n\u001b[32m--> \u001b[39m\u001b[32m451\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_condition\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mwait\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n", "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/threading.py:355\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m355\u001b[39m \u001b[30;43mwaiter\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43macquire\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 356\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ "#load model \n", "\n", "import torch\n", "from transformers import AutoProcessor, AutoModelForCausalLM\n", "\n", "from transformers import Qwen2AudioForConditionalGeneration\n", "\n", "model_path = \"../training/yor_tuned_audio/final_merged_model\"\n", "\n", "processor = AutoProcessor.from_pretrained( model_path, sampling_rate=16000)\n", "\n", "model = Qwen2AudioForConditionalGeneration.from_pretrained(\n", " model_path,\n", " torch_dtype=torch.bfloat16,\n", " device_map=\"auto\",\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "id": "7ea43ff6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 418/418 [21:02<00:00, 3.02s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Final WER: 0.6738\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import torch\n", "from tqdm import tqdm\n", "from evaluate import load\n", "import librosa\n", "\n", "# WER metric\n", "wer_metric = load(\"wer\")\n", "\n", "TARGET_SR = 16000\n", "\n", "predictions = []\n", "references = []\n", "\n", "# Put model in evaluation mode\n", "model.eval()\n", "\n", "for sample in tqdm(dataset):\n", "\n", " # -----------------------------\n", " # Decode audio\n", " # -----------------------------\n", " decoded = sample[\"audio\"].get_all_samples()\n", "\n", " audio = decoded.data.numpy().squeeze()\n", " sampling_rate = decoded.sample_rate\n", "\n", " reference = sample[\"sentence\"]\n", "\n", " if sampling_rate != TARGET_SR:\n", " audio = librosa.resample(\n", " audio,\n", " orig_sr=sampling_rate,\n", " target_sr=TARGET_SR\n", " )\n", " sampling_rate = TARGET_SR\n", "\n", " reference = sample[\"sentence\"]\n", "\n", " \n", " conversation = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"audio\", \"audio\": audio},\n", " {\"type\": \"text\", \"text\": \"Transcribe this audio.\"},\n", " ],\n", " }\n", " ]\n", "\n", " text = processor.apply_chat_template(\n", " conversation,\n", " add_generation_prompt=True,\n", " tokenize=False,\n", " )\n", "\n", " # -----------------------------\n", " # Process input\n", " # -----------------------------\n", " inputs = processor(\n", " text=text,\n", " audio=[audio],\n", " sampling_rate=sampling_rate,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " inputs = {k: v.to(model.device) for k, v in inputs.items()}\n", "\n", " # -----------------------------\n", " # Generate prediction\n", " # -----------------------------\n", " with torch.no_grad():\n", "\n", " generated_ids = model.generate(\n", " **inputs,\n", " max_new_tokens=256,\n", " )\n", "\n", " generated_ids = generated_ids[:, inputs[\"input_ids\"].shape[1]:]\n", "\n", " prediction = processor.batch_decode(\n", " generated_ids,\n", " skip_special_tokens=True,\n", " )[0]\n", "\n", " predictions.append(prediction)\n", " references.append(reference)\n", "\n", "# -----------------------------\n", "# Compute WER\n", "# -----------------------------\n", "wer = wer_metric.compute(\n", " predictions=predictions,\n", " references=references,\n", ")\n", "\n", "print(f\"\\nFinal WER: {wer:.4f}\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "5eb8fc06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Word Error Rate: 0.6738303111708082\n" ] } ], "source": [ "import jiwer\n", "\n", "output = jiwer.process_words(references, predictions)\n", "\n", "print(f\"Word Error Rate: {output.wer}\") " ] }, { "cell_type": "code", "execution_count": 17, "id": "70c30780", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reference: Mo bọ́ sínú ọkọ̀, mo kéé gidi gan.\n", "Predictions: mo gbọ́ sínú ọkọ̀ mo kégi dí gan-an\n", "Reference: Àlòkù táyà ọkọ̀ ló mú mi rí nǹkan ṣe lásìkò ìgbélé ààrùn yìí.\n", "Predictions: alòkù táyà ọkọ̀ ló mú mi rí nǹkan ṣe lásìkò ìgbélé àrùn yìí\n", "Reference: Gbajúmọ̀ oníṣòwò ilé ijó kan ní Oshòdì ti ṣàlàyé ìgbésí ayé rẹ̀\n", "Predictions: gbajúmọ̀ oníṣòwò ilé ìjọ kan ní oṣù dìtì ṣàlàyé ìgbésí ayé rẹ̀.\n", "Reference: Tí ìjọba àpapọ̀ bá ṣ'àìtọ́ nílé mi ìbọn á sọ.\n", "Predictions: tí ìjọba àpapọ̀ bá ṣàìtọ́ nílé mi ìbọn á sọ\n", "Reference: Ọ̀pọ̀ ọ̀dọ́ Nàíjíríà ń bèèrè láti ṣàtìpó ní Ògbómọ̀ṣọ́.\n", "Predictions: ọpọ ọdọ nàìjíríà ń bẹrẹ láti ṣe àtìpò ní ògbómọṣọ\n", "Reference: Bàbá Sùwé ti sọ fún ọmọ olóògbé pé kó má ṣe wàhálà mọ́\n", "Predictions: bàbá súwé ti sọ fún wọn mọ olóògbé pé kó má ṣe wàhálà mọ́\n", "Reference: Kúnlé àti Afọláyan gbéná wojú ara wọn lórí ayélujára.\n", "Predictions: kúnlẹ̀ àti afọlàyàn gbénáwó ojú ara wọn lórí ayélujára\n", "Reference: Fásitì Ifẹ̀ lé Ọ̀jọ̀gbọ́n tó ṣe aṣemáṣe pẹ̀lú akẹ́kọ̀ọ́.\n", "Predictions: fásitì ife lè ọ̀jọ̀gbọ́n tó ṣe àṣemáṣe pẹ̀lú akẹ́kọ̀ọ́.\n", "Reference: Ìdílé Aníwúrà àtàwọn mìíràn rí owó tabua nínú òwò ẹrú ṣíṣe.\n", "Predictions: ìdílé a ní rírà àtàwọn míràn bí owó tàbá nínú òwò [?] irú ṣíṣe.\n", "Reference: Wọ́n gbàgbé pé ilé àti ọ̀nà ọ̀tọ̀ọ̀tọ̀ ní gbogbo wọ́n ti wá.\n", "Predictions: wọn gbàgbé pé ilé àti ọ̀nà ọ̀tọ̀ọ̀tọ̀ ni gbogbo wọn ti wá\n" ] } ], "source": [ "for i in range(10):\n", " print(\"Reference:\", references[i])\n", " print(\"Predictions:\", predictions[i])" ] } ], "metadata": { "kernelspec": { "display_name": "Python \n (audiollm-env)", "language": "python", "name": "audiollm-env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }