{ "aboutHtml": "

Whisper large-v3 — taxminan 1,55 milliard parametrli, OpenAI chiqargan nutqni tanish checkpoint’i. Bu ko‘p tilli kodlovchi-dekodlovchi model: audio kodlovchiga kiradi, dekodlovchi esa matn va vazifaga oid tokenlarni bashorat qiladi. Asosiy vazifalar — nutqni manba tilida transkripsiya qilish va nutqni ingliz tiliga tarjima qilish. Bu vaznlari ochiq modelning identifikatsiyasi bo‘lib, xizmat sifatida taqdim etiladigan model identifikatori yoki o‘sha xizmatning yuklash va javob formatlari qoidalaridan alohida saqlanadi.

Large-v3 oldingi large Whisper checkpoint’lari arxitekturasini ikki ko‘rsatilgan o‘zgarish bilan saqlaydi: uning spektrogramma kirishi 80 o‘rniga 128 Mel chastota oralig‘idan foydalanadi va kanton tili tokenini qo‘shadi. Ishlab chiqaruvchining model kartasi bir million soat zaif belgilangan audio va Whisper large-v2 bilan tayyorlangan to‘rt million soat soxta yorliqli audio ustida o‘qitishni tavsiflaydi. Bu aralashma ikki o‘qitish epoxasi uchun ishlatilgan. Chiqarilish Whisper oilasidagi har bir modelning umumiy nomi emas, muayyan large-v3 checkpoint’idir.

Hujjatlashtirilgan inferensiya ish jarayoni standart holatda transkripsiya bajarib, yozuvda aytilgan tilni saqlaydi. U manba tilini avtomatik aniqlashi yoki ma’lum til tanlovini qabul qilishi mumkin. Tarjima uchun ko‘p tilli large model inglizcha bo‘lmagan nutqdan inglizcha matn natijasini beradi. Ushbu vazifani aniq tanlang: bir xil tildagi transkript va inglizcha tarjima turli natijalardir. Intervyu uchun asl tildagi so‘zlarni saqlang; inglizcha ma’lumotnoma hujjati uchun kerakli natija aytilgan kontent tarjimasi ekanini belgilang.

Audioni tayyorlash kirish yo‘lining bir qismidir. OpenAI yuklovchisi yozuvni mono to‘lqin shakliga dekodlaydi va 16 kHz’ga qayta namunalaydi. Modelning akustik qabul maydoni 30 soniya. Bu butun intervyu yoki ma’ruzaning maksimal uzunligi emas, qayta ishlash oynasidir. Uzun material inferensiyasi sirpanuvchi oynalar orqali ketma-ket oldinga yurishi yoki uzun yozuvni ustma-ust tushadigan bo‘laklarga ajratib, ularning transkriptlarini birlashtirishi mumkin. Ishlab chiqaruvchining model kartasi aniqlik ustuvor bo‘lganda ketma-ket qayta ishlashni, bitta uzun yozuvni tayyorlash vaqti muhimroq bo‘lganda esa bo‘laklab qayta ishlashni tavsiflaydi.

Chiqarilgan konfiguratsiya alohida dekodlash ketma-ketligi uchun 448 dekodlovchi pozitsiyasini ko‘rsatadi. Uzunroq yozuvlar bu sonni umumiy transkript limiti deb hisoblash bilan emas, uzun material jarayoni orqali qayta ishlanadi. Hujjatlashtirilgan ish jarayoni tanilgan matn bilan birga segment yoki so‘z darajasidagi vaqt belgilarini qaytarishi mumkin. Bu vaqt natijalari yozuv bo‘ylab o‘tish va subtitrlar tayyorlash uchun foydali, ammo yakuniy subtitr konteyneri yoki transkript fayli formati alohida chiqishni formatlash tanlovidir. Transkript asl materialga moslashtiriladigan bo‘lsa, audio tayyorlashda yozuvning vaqt tuzilishini saqlang.

Whisper’ning mo‘ljallangan vazifasi nutqni tanish va tarjima qilishdir. Ma’ruza transkripti, intervyu yozuvi yoki qidirish mumkin bo‘lgan og‘zaki materiallar to‘plami amaliy chiqishdir; matnni qisqacha bayon qilish yoki talqin qilish boshqa keyingi bosqich bo‘lishi mumkin. Model kartasi ishonchli avtomatik so‘zlovchi tasnifi yoki diarizatsiyani bazaviy checkpoint’ning standart chiqishi sifatida belgilamaydi. Vazifaga so‘zlovchi yorliqlari kerak bo‘lsa, bu talab aniq transkript bor degan da’vodan alohida hal qilinishi kerak. Foydali topshiriq manba tilini, transkripsiya yoki inglizcha tarjima kerakligini va vaqt ma’lumoti zarurligini ko‘rsatadi.

Muhim cheklovlar hech qachon aytilmagan to‘qima matn va takroriy matnni o‘z ichiga oladi; bular shovqinli ma’lumotlarda zaif nazoratli o‘qitish va ketma-ketlikdan ketma-ketlikka tuzilishi bilan bog‘liq. Tanish sifati tillar, aksentlar va shevalar bo‘yicha farqlanadi, o‘qitish materiali kamroq joylarda natijalar zaifroq bo‘ladi. Shuning uchun moslikni baholash uchun ishlatiladigan namunada shovqin, sohaga xos lug‘at va ustma-ust nutq aks etishi kerak. Ismlar, miqdorlar va muhim bayonotlarni yozuvga solishtiring va ravon transkriptni har bir so‘z eshitilganining isboti deb hisoblash o‘rniga, nutqsiz parchalar yoki takroriy satrlarni tekshiring.

To‘liq natija tanilgan matnni, uning tilini va so‘ralgan vaqt ma’lumotini asl yozuvga havola bilan saqlaydi. Transkripsiyani keyingi tahrirdan ajrating: nashr uchun tuzatilgan transkript to‘ldiruvchi so‘zlarni olib tashlashi mumkin, tanilgan transkript esa model haqiqatan qaytargan narsani saqlaydi. Large-v3’ning identifikatsiyasi va tarjima imkoniyati tezroq Turbo nashridan ham alohida qolishi kerak. Haqiqiy qayta ishlash tezligi til, nutq sur’ati, uskuna va inferensiya usuliga bog‘liq; checkpoint nomining o‘zidan xizmatning muayyan kechikishi, maksimal yuklash hajmi yoki yoqilgan ilova yo‘li kelib chiqmaydi.

", "aboutText": "Whisper large-v3 — taxminan 1,55 milliard parametrli, OpenAI chiqargan nutqni tanish checkpoint’i. Bu ko‘p tilli kodlovchi-dekodlovchi model: audio kodlovchiga kiradi, dekodlovchi esa matn va vazifaga oid tokenlarni bashorat qiladi. Asosiy vazifalar — nutqni manba tilida transkripsiya qilish va nutqni ingliz tiliga tarjima qilish. Bu vaznlari ochiq modelning identifikatsiyasi bo‘lib, xizmat sifatida taqdim etiladigan model identifikatori yoki o‘sha xizmatning yuklash va javob formatlari qoidalaridan alohida saqlanadi.\n\nLarge-v3 oldingi large Whisper checkpoint’lari arxitekturasini ikki ko‘rsatilgan o‘zgarish bilan saqlaydi: uning spektrogramma kirishi 80 o‘rniga 128 Mel chastota oralig‘idan foydalanadi va kanton tili tokenini qo‘shadi. Ishlab chiqaruvchining model kartasi bir million soat zaif belgilangan audio va Whisper large-v2 bilan tayyorlangan to‘rt million soat soxta yorliqli audio ustida o‘qitishni tavsiflaydi. Bu aralashma ikki o‘qitish epoxasi uchun ishlatilgan. Chiqarilish Whisper oilasidagi har bir modelning umumiy nomi emas, muayyan large-v3 checkpoint’idir.\n\nHujjatlashtirilgan inferensiya ish jarayoni standart holatda transkripsiya bajarib, yozuvda aytilgan tilni saqlaydi. U manba tilini avtomatik aniqlashi yoki ma’lum til tanlovini qabul qilishi mumkin. Tarjima uchun ko‘p tilli large model inglizcha bo‘lmagan nutqdan inglizcha matn natijasini beradi. Ushbu vazifani aniq tanlang: bir xil tildagi transkript va inglizcha tarjima turli natijalardir. Intervyu uchun asl tildagi so‘zlarni saqlang; inglizcha ma’lumotnoma hujjati uchun kerakli natija aytilgan kontent tarjimasi ekanini belgilang.\n\nAudioni tayyorlash kirish yo‘lining bir qismidir. OpenAI yuklovchisi yozuvni mono to‘lqin shakliga dekodlaydi va 16 kHz’ga qayta namunalaydi. Modelning akustik qabul maydoni 30 soniya. Bu butun intervyu yoki ma’ruzaning maksimal uzunligi emas, qayta ishlash oynasidir. Uzun material inferensiyasi sirpanuvchi oynalar orqali ketma-ket oldinga yurishi yoki uzun yozuvni ustma-ust tushadigan bo‘laklarga ajratib, ularning transkriptlarini birlashtirishi mumkin. Ishlab chiqaruvchining model kartasi aniqlik ustuvor bo‘lganda ketma-ket qayta ishlashni, bitta uzun yozuvni tayyorlash vaqti muhimroq bo‘lganda esa bo‘laklab qayta ishlashni tavsiflaydi.\n\nChiqarilgan konfiguratsiya alohida dekodlash ketma-ketligi uchun 448 dekodlovchi pozitsiyasini ko‘rsatadi. Uzunroq yozuvlar bu sonni umumiy transkript limiti deb hisoblash bilan emas, uzun material jarayoni orqali qayta ishlanadi. Hujjatlashtirilgan ish jarayoni tanilgan matn bilan birga segment yoki so‘z darajasidagi vaqt belgilarini qaytarishi mumkin. Bu vaqt natijalari yozuv bo‘ylab o‘tish va subtitrlar tayyorlash uchun foydali, ammo yakuniy subtitr konteyneri yoki transkript fayli formati alohida chiqishni formatlash tanlovidir. Transkript asl materialga moslashtiriladigan bo‘lsa, audio tayyorlashda yozuvning vaqt tuzilishini saqlang.\n\nWhisper’ning mo‘ljallangan vazifasi nutqni tanish va tarjima qilishdir. Ma’ruza transkripti, intervyu yozuvi yoki qidirish mumkin bo‘lgan og‘zaki materiallar to‘plami amaliy chiqishdir; matnni qisqacha bayon qilish yoki talqin qilish boshqa keyingi bosqich bo‘lishi mumkin. Model kartasi ishonchli avtomatik so‘zlovchi tasnifi yoki diarizatsiyani bazaviy checkpoint’ning standart chiqishi sifatida belgilamaydi. Vazifaga so‘zlovchi yorliqlari kerak bo‘lsa, bu talab aniq transkript bor degan da’vodan alohida hal qilinishi kerak. Foydali topshiriq manba tilini, transkripsiya yoki inglizcha tarjima kerakligini va vaqt ma’lumoti zarurligini ko‘rsatadi.\n\nMuhim cheklovlar hech qachon aytilmagan to‘qima matn va takroriy matnni o‘z ichiga oladi; bular shovqinli ma’lumotlarda zaif nazoratli o‘qitish va ketma-ketlikdan ketma-ketlikka tuzilishi bilan bog‘liq. Tanish sifati tillar, aksentlar va shevalar bo‘yicha farqlanadi, o‘qitish materiali kamroq joylarda natijalar zaifroq bo‘ladi. Shuning uchun moslikni baholash uchun ishlatiladigan namunada shovqin, sohaga xos lug‘at va ustma-ust nutq aks etishi kerak. Ismlar, miqdorlar va muhim bayonotlarni yozuvga solishtiring va ravon transkriptni har bir so‘z eshitilganining isboti deb hisoblash o‘rniga, nutqsiz parchalar yoki takroriy satrlarni tekshiring.\n\nTo‘liq natija tanilgan matnni, uning tilini va so‘ralgan vaqt ma’lumotini asl yozuvga havola bilan saqlaydi. Transkripsiyani keyingi tahrirdan ajrating: nashr uchun tuzatilgan transkript to‘ldiruvchi so‘zlarni olib tashlashi mumkin, tanilgan transkript esa model haqiqatan qaytargan narsani saqlaydi. Large-v3’ning identifikatsiyasi va tarjima imkoniyati tezroq Turbo nashridan ham alohida qolishi kerak. Haqiqiy qayta ishlash tezligi til, nutq sur’ati, uskuna va inferensiya usuliga bog‘liq; checkpoint nomining o‘zidan xizmatning muayyan kechikishi, maksimal yuklash hajmi yoki yoqilgan ilova yo‘li kelib chiqmaydi.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "15d1f7c8d51dccfd63bebe6fc2b88e9085c1d063d9f9c2409ef9fcd48cac87a4", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
O

OpenAI: Whisper Large V3

OpenAI /openai/whisper-large-v3

Boshlash

Whisper large-v3 — transkripsiya va nutqni ingliz tiliga tarjima qilish uchun OpenAI’ning vaznlari ochiq ko‘p tilli nutqni tanish checkpoint’i. U yozib olingan nutqni matnga aylantiradi; til tanlash, vaqt belgilari va 30 soniyali akustik oynadan uzunroq materialni qayta ishlash hujjatlashtirilgan.

Qabul qilinadigan kirish ma’lumotlari
Audio
Natijalar
transcription
Model haqida

Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.

Whisper large-v3 — taxminan 1,55 milliard parametrli, OpenAI chiqargan nutqni tanish checkpoint’i. Bu ko‘p tilli kodlovchi-dekodlovchi model: audio kodlovchiga kiradi, dekodlovchi esa matn va vazifaga oid tokenlarni bashorat qiladi. Asosiy vazifalar — nutqni manba tilida transkripsiya qilish va nutqni ingliz tiliga tarjima qilish. Bu vaznlari ochiq modelning identifikatsiyasi bo‘lib, xizmat sifatida taqdim etiladigan model identifikatori yoki o‘sha xizmatning yuklash va javob formatlari qoidalaridan alohida saqlanadi.

Large-v3 oldingi large Whisper checkpoint’lari arxitekturasini ikki ko‘rsatilgan o‘zgarish bilan saqlaydi: uning spektrogramma kirishi 80 o‘rniga 128 Mel chastota oralig‘idan foydalanadi va kanton tili tokenini qo‘shadi. Ishlab chiqaruvchining model kartasi bir million soat zaif belgilangan audio va Whisper large-v2 bilan tayyorlangan to‘rt million soat soxta yorliqli audio ustida o‘qitishni tavsiflaydi. Bu aralashma ikki o‘qitish epoxasi uchun ishlatilgan. Chiqarilish Whisper oilasidagi har bir modelning umumiy nomi emas, muayyan large-v3 checkpoint’idir.

Hujjatlashtirilgan inferensiya ish jarayoni standart holatda transkripsiya bajarib, yozuvda aytilgan tilni saqlaydi. U manba tilini avtomatik aniqlashi yoki ma’lum til tanlovini qabul qilishi mumkin. Tarjima uchun ko‘p tilli large model inglizcha bo‘lmagan nutqdan inglizcha matn natijasini beradi. Ushbu vazifani aniq tanlang: bir xil tildagi transkript va inglizcha tarjima turli natijalardir. Intervyu uchun asl tildagi so‘zlarni saqlang; inglizcha ma’lumotnoma hujjati uchun kerakli natija aytilgan kontent tarjimasi ekanini belgilang.

Audioni tayyorlash kirish yo‘lining bir qismidir. OpenAI yuklovchisi yozuvni mono to‘lqin shakliga dekodlaydi va 16 kHz’ga qayta namunalaydi. Modelning akustik qabul maydoni 30 soniya. Bu butun intervyu yoki ma’ruzaning maksimal uzunligi emas, qayta ishlash oynasidir. Uzun material inferensiyasi sirpanuvchi oynalar orqali ketma-ket oldinga yurishi yoki uzun yozuvni ustma-ust tushadigan bo‘laklarga ajratib, ularning transkriptlarini birlashtirishi mumkin. Ishlab chiqaruvchining model kartasi aniqlik ustuvor bo‘lganda ketma-ket qayta ishlashni, bitta uzun yozuvni tayyorlash vaqti muhimroq bo‘lganda esa bo‘laklab qayta ishlashni tavsiflaydi.

Chiqarilgan konfiguratsiya alohida dekodlash ketma-ketligi uchun 448 dekodlovchi pozitsiyasini ko‘rsatadi. Uzunroq yozuvlar bu sonni umumiy transkript limiti deb hisoblash bilan emas, uzun material jarayoni orqali qayta ishlanadi. Hujjatlashtirilgan ish jarayoni tanilgan matn bilan birga segment yoki so‘z darajasidagi vaqt belgilarini qaytarishi mumkin. Bu vaqt natijalari yozuv bo‘ylab o‘tish va subtitrlar tayyorlash uchun foydali, ammo yakuniy subtitr konteyneri yoki transkript fayli formati alohida chiqishni formatlash tanlovidir. Transkript asl materialga moslashtiriladigan bo‘lsa, audio tayyorlashda yozuvning vaqt tuzilishini saqlang.

Whisper’ning mo‘ljallangan vazifasi nutqni tanish va tarjima qilishdir. Ma’ruza transkripti, intervyu yozuvi yoki qidirish mumkin bo‘lgan og‘zaki materiallar to‘plami amaliy chiqishdir; matnni qisqacha bayon qilish yoki talqin qilish boshqa keyingi bosqich bo‘lishi mumkin. Model kartasi ishonchli avtomatik so‘zlovchi tasnifi yoki diarizatsiyani bazaviy checkpoint’ning standart chiqishi sifatida belgilamaydi. Vazifaga so‘zlovchi yorliqlari kerak bo‘lsa, bu talab aniq transkript bor degan da’vodan alohida hal qilinishi kerak. Foydali topshiriq manba tilini, transkripsiya yoki inglizcha tarjima kerakligini va vaqt ma’lumoti zarurligini ko‘rsatadi.

Muhim cheklovlar hech qachon aytilmagan to‘qima matn va takroriy matnni o‘z ichiga oladi; bular shovqinli ma’lumotlarda zaif nazoratli o‘qitish va ketma-ketlikdan ketma-ketlikka tuzilishi bilan bog‘liq. Tanish sifati tillar, aksentlar va shevalar bo‘yicha farqlanadi, o‘qitish materiali kamroq joylarda natijalar zaifroq bo‘ladi. Shuning uchun moslikni baholash uchun ishlatiladigan namunada shovqin, sohaga xos lug‘at va ustma-ust nutq aks etishi kerak. Ismlar, miqdorlar va muhim bayonotlarni yozuvga solishtiring va ravon transkriptni har bir so‘z eshitilganining isboti deb hisoblash o‘rniga, nutqsiz parchalar yoki takroriy satrlarni tekshiring.

To‘liq natija tanilgan matnni, uning tilini va so‘ralgan vaqt ma’lumotini asl yozuvga havola bilan saqlaydi. Transkripsiyani keyingi tahrirdan ajrating: nashr uchun tuzatilgan transkript to‘ldiruvchi so‘zlarni olib tashlashi mumkin, tanilgan transkript esa model haqiqatan qaytargan narsani saqlaydi. Large-v3’ning identifikatsiyasi va tarjima imkoniyati tezroq Turbo nashridan ham alohida qolishi kerak. Haqiqiy qayta ishlash tezligi til, nutq sur’ati, uskuna va inferensiya usuliga bog‘liq; checkpoint nomining o‘zidan xizmatning muayyan kechikishi, maksimal yuklash hajmi yoki yoqilgan ilova yo‘li kelib chiqmaydi.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Nutqni tanish POST /v1/audio/transcriptions
Kirish
audio
Chiqish
transcription
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
openai/whisper-large-v3
Standart—
Maydoninput_audioTurobjectTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonlanguageTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 0
Eng ko‘p: 1
Standart—
Maydonresponse_formatTurstringTalabIxtiyoriyQiymatlar va cheklovlar
json, verbose_json
Standartjson
Maydontimestamp_granularitiesTurarrayTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonproviderTurobjectTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Kirish materiallari birikmalari

speech_to_text · Majburiy: actual matching-format audio file

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typesParametr talablariapplication/json, multipart/form-data
ParametrrequiredParametr talablarimodel, input_audio
Parametrmaterial_typesParametr talablariaudio
ParametrJSON_audioParametr talablarihaqiqiy xom audio baytlari base64 ko‘rinishida va mos format
Parametrmultipart_requiredParametr talablarimodel, file
Parametrmultipart_cap_bytesParametr talablari26214400
Parametroptional_controlsParametr talablarilanguage, temperature, response_format, timestamp_granularities, provider
Parametrdefault_response_formatParametr talablarijson
Parametroptional_featuresParametr talablariBatafsil chiqish, diarize/keyterms va URL/kattaroq ichki audio uchun aynan shu model/provayder imkoniyati va tarifi zarur.

Hayot sikli: synchronous_result_then_owned_library

GET /v1/media/tasks/{task_id} · Amalda qaytarilgan task_id yoki media kutubxonasidagi yozuv

GET /v1/media/assets/{asset_id}/content

GET /v1/media/assets

GET /v1/media/assets/{asset_id}

Aniq model va hujjatdagi kirish maydonlaridan foydalaning.

To‘liq javobni, har bir haqiqiy chiqish natijasini va dastlab qaytarilgan ID larni saqlang.

Tariflar

Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.

Tezkor rejim
Ko‘rsatilmagan Tezkor rejim tanlanganda
Flex rejimi
Ko‘rsatilmagan flex rejimi tanlanganda
Maksimal fikrlash darajasi
Ko‘rsatilmagan Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.

SozlamaTariflar jadvaliHisob birligiTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifHisob birligi—To‘lov
Kirish · Kirish tokenlari — / 1K token
Chiqish · Chiqish tokenlari — / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaUltraTariflar jadvali—Hisob birligikirish audiosining soniyasi uchunTo‘lov
Kirish · Kirish audiosi soniyasi $0.000015 kirish audiosining soniyasi uchun

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

Ma’lumot uchun tarif tafsilotlari

Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.

OpenAI: Whisper Large V3

Sozlamalar bo‘yicha narxlar

SozlamaTo‘lovni hisoblash shartlariTo‘lov
SozlamaOpenAI: Whisper Large V3 · Standart sozlamaTo‘lovni hisoblash shartlariStandart konfiguratsiyaTo‘lov
Kirish · Kirish audiosi soniyalari 0,000015 US$ kirish audiosining soniyasi uchun

So‘rov namunalari

Ailoot Ultra · Nutqni tanish

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_AUDIO_FILE:?Set the path to your actual WAV recording}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\nimport base64\naudio=Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4]==b'RIFF' and audio[8:12]==b'WAVE'): raise SystemExit('Supply an actual WAV audio file')\nif len(audio)>26214400: raise SystemExit('Use a documented supported large-file flow')\nrequest={'model': 'openai/whisper-large-v3', 'input_audio': {'data': '${BASE64_ACTUAL_WAV_BYTES}', 'format': 'wav'}}\nrequest['input_audio']['data']=base64.b64encode(audio).decode('ascii')\nPath('request.json').write_text(json.dumps(request,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o response.bin\n# Keep the complete original response and headers. Read every returned segment, word and usage field.\n# Preserve actual operation/task/asset IDs; continue reads of the original result without resubmitting.\n

Natijalarni olish va saqlash

Ailoot Ultra · Nutqni tanish

json

Barcha tayyor natijalarni saqlash

Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.

To‘liq dastlabki JSON, mavjud barcha matn/segmentlar/so‘zlar/so‘zlovchilar/vaqt belgilari va foydalanish ma’lumoti.

Asl javobni tekshirish kerak bo‘lsa

Dastlabki operation_id/X-Client-Request-ID va dastlabki javobni saqlang. Qayta yubormasdan haqiqiy, egalik qilinadigan vazifa/aktiv yozuvlarini o‘qing; audio holatini so‘rash uchun maxsus URL tuzilmaydi.

Javob maydonlari
MaydonTur
MaydontextTurstring
MaydonusageTurobject
Maydonsegments[]Turobject
Maydonwords[]Turobject

Taxalluslar

Model taxalluslari

Ailoot Ultra openai/whisper-large-v3

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

openai/whisper-large-v3

", "method_identity": { "effective_request_model_id": "openai/whisper-large-v3", "fmt": "transcriptions", "http": "POST", "path": "/v1/audio/transcriptions", "request_model_id": "openai/whisper-large-v3", "route_label": "Ultra" }, "method_key": "Ultra|transcriptions|/v1/audio/transcriptions|openai%2Fwhisper-large-v3", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Complete original JSON, all available text/segments/words/speakers/timestamps and usage.", "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "actual returned task_id or media library record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Dastlabki operation_id/X-Client-Request-ID va dastlabki javobni saqlang. Qayta yubormasdan haqiqiy, egalik qilinadigan vazifa/aktiv yozuvlarini o‘qing; audio holatini so‘rash uchun maxsus URL tuzilmaydi." }, "material_combinations": [ { "content_types": [ "application/json", "multipart/form-data" ], "mode": "speech_to_text", "required": [ "actual matching-format audio file" ] } ], "outputs": { "all_results": "To‘liq dastlabki JSON, mavjud barcha matn/segmentlar/so‘zlar/so‘zlovchilar/vaqt belgilari va foydalanish ma’lumoti.", "family": "transcription", "fields": [ { "name": "text", "type": "string" }, { "name": "usage", "type": "object" }, { "name": "segments[]", "optional": true, "type": "object" }, { "name": "words[]", "optional": true, "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "enum": [ "openai/whisper-large-v3" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "data_encoding": "base64 raw actual audio bytes, not data URI", "fields": [ "data", "format" ], "name": "input_audio", "required": true, "type": "object" }, { "availability": "retained_official_STTRequest", "description": "ISO-639-1 code; omitted means auto-detection", "name": "language", "required": false, "type": "string" }, { "availability": "retained_official_STTRequest", "maximum": 1, "minimum": 0, "name": "temperature", "required": false, "type": "number" }, { "availability": "retained_official_STTRequest", "default": "json", "default_known": true, "description": "verbose_json requires exact model/provider support", "enum": [ "json", "verbose_json" ], "enum_known": true, "name": "response_format", "required": false, "type": "string" }, { "availability": "retained_official_STTRequest", "description": "Only used with supported verbose_json", "items": { "enum": [ "segment", "word" ], "type": "string" }, "name": "timestamp_granularities", "required": false, "type": "array" }, { "availability": "retained_official_STTRequest", "description": "Data policy and exact provider options; order/only/ignore are not applied to STT", "name": "provider", "required": false, "type": "object" } ], "request_schema": { "properties": { "input_audio": { "properties": { "data": { "contentEncoding": "base64", "type": "string" }, "format": { "enum": [ "wav", "mp3", "flac", "m4a", "ogg", "webm", "aac" ], "type": "string" } }, "required": [ "data", "format" ], "type": "object" }, "language": { "description": "ISO-639-1 code; omitted means auto-detection", "type": "string" }, "model": { "enum": [ "openai/whisper-large-v3" ], "type": "string" }, "provider": { "description": "Data policy and exact provider options; order/only/ignore are not applied to STT", "properties": { "data_collection": { "enum": [ "allow", "deny", null ], "type": [ "string", "null" ] }, "options": { "type": "object" }, "zdr": { "type": [ "boolean", "null" ] } }, "type": "object" }, "response_format": { "default": "json", "description": "verbose_json requires exact model/provider support", "enum": [ "json", "verbose_json" ], "type": "string" }, "temperature": { "maximum": 1, "minimum": 0, "type": "number" }, "timestamp_granularities": { "description": "Only used with supported verbose_json", "items": { "enum": [ "segment", "word" ], "type": "string" }, "type": "array" } }, "required": [ "model", "input_audio" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": true, "body": { "input_audio": { "data": "${BASE64_ACTUAL_WAV_BYTES}", "format": "wav" }, "model": "openai/whisper-large-v3" }, "content_type": "application/json" } }, "hints": [ "Aniq model va hujjatdagi kirish maydonlaridan foydalaning.", "To‘liq javobni, har bir haqiqiy chiqish natijasini va dastlab qaytarilgan ID larni saqlang." ], "input": { "JSON_audio": "haqiqiy xom audio baytlari base64 ko‘rinishida va mos format", "content_types": [ "application/json", "multipart/form-data" ], "default_response_format": "json", "material_types": [ "audio" ], "multipart_cap_bytes": 26214400, "multipart_required": [ "model", "file" ], "optional_controls": [ "language", "temperature", "response_format", "timestamp_granularities", "provider" ], "optional_features": "Batafsil chiqish, diarize/keyterms va URL/kattaroq ichki audio uchun aynan shu model/provayder imkoniyati va tarifi zarur.", "required": [ "model", "input_audio" ] }, "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "Amalda qaytarilgan task_id yoki media kutubxonasidagi yozuv", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Retain original operation_id/X-Client-Request-ID and original response. Read actual owned task/asset records without resubmitting; no dedicated audio poll URL is constructed." }, "method": { "http": "POST", "path": "/v1/audio/transcriptions" }, "method_aliases": [], "operation_modes": [ "speech_to_text" ], "output": { "all_results": "Complete original JSON, all available text/segments/words/speakers/timestamps and usage.", "family": "transcription", "format": "json" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quantity_basis": "actual physical units", "quoted_unit": "per audio input second" }, "label": "Ultra", "units": [ { "amount": "0.000015", "amount_unit": "usd_per_audio_input_second", "billing_input": true, "estimate_only": false, "key": "audio_input_second", "label": "audio input second", "native_amount_usd_per_unit": "0.000015", "native_quantity": 1, "public_unit_label": "per audio input second", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "audio_input_second" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_AUDIO_FILE:?Set the path to your actual WAV recording}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\nimport base64\naudio=Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4]==b'RIFF' and audio[8:12]==b'WAVE'): raise SystemExit('Supply an actual WAV audio file')\nif len(audio)>26214400: raise SystemExit('Use a documented supported large-file flow')\nrequest={'model': 'openai/whisper-large-v3', 'input_audio': {'data': '${BASE64_ACTUAL_WAV_BYTES}', 'format': 'wav'}}\nrequest['input_audio']['data']=base64.b64encode(audio).decode('ascii')\nPath('request.json').write_text(json.dumps(request,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o response.bin\n# Keep the complete original response and headers. Read every returned segment, word and usage field.\n# Preserve actual operation/task/asset IDs; continue reads of the original result without resubmitting.\n", "label": "curl · exact native body and complete result", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "transcriptions", "hints": [ "Use the exact model and documented input fields.", "Keep the complete response, every actual output and original returned IDs." ], "http": "POST", "in": null, "input": [ "audio" ], "label": "Распознавание речи", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": null, "output": [ "transcription" ], "path": "/v1/audio/transcriptions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "audio_input_second": 1.5e-05 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quantity_basis": "actual physical units", "quoted_unit": "per audio input second" }, "label": "Ultra", "units": [ { "amount": "0.000015", "amount_unit": "usd_per_audio_input_second", "billing_input": true, "estimate_only": false, "key": "audio_input_second", "label": "audio input second", "native_amount_usd_per_unit": "0.000015", "native_quantity": 1, "public_unit_label": "per audio input second", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "audio_input_second" } ] } ], "request_model_id": "openai/whisper-large-v3", "route_label": "Ultra", "status_reads": [ "GET /v1/media/tasks/{task_id}", "GET /v1/media/assets/{asset_id}/content" ], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.000015", "amount_unit": "usd_per_audio_input_second", "billing_input": true, "estimate_only": false, "key": "audio_input_second", "label": "audio input second", "native_amount_usd_per_unit": "0.000015", "native_quantity": 1, "public_unit_label": "per audio input second", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "audio_input_second" } ] } } ], "model_id": "openai/whisper-large-v3", "nativeProvenance": { "input_facts_canonical_sha256": "9590687e418ee5929ce614d91a026a86d3aa962ae6eecc48a2706b7c018bd784", "input_html_sha256": "b9af221d791569316a2b684366685f6970dfed01ae2b4a39e63d6fae9e8f0e73", "private_audit_sha256": "e294909591f587b466cc38d0f37e33c86bde09bd1bb6f0bd3cc9528a327abbd7", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/openai-whisper-large-v3/", "description": "Whisper large-v3 — transkripsiya va nutqni ingliz tiliga tarjima qilish uchun OpenAI’ning vaznlari ochiq ko‘p tilli nutqni tanish checkpoint’i. U yozib olingan nutqni matnga aylantiradi; til tanlash, vaqt belgilari va 30 soniyali akustik oynadan uzunroq materialni qayta ishlash hujjatlashtirilgan.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/openai-whisper-large-v3/index.md", "title": "OpenAI: Whisper Large V3 — Ailoot", "url": "https://ailoot.ai/models/uz/openai-whisper-large-v3/" }, "rawModel": { "about": "Whisper large-v3 — taxminan 1,55 milliard parametrli, OpenAI chiqargan nutqni tanish checkpoint’i. Bu ko‘p tilli kodlovchi-dekodlovchi model: audio kodlovchiga kiradi, dekodlovchi esa matn va vazifaga oid tokenlarni bashorat qiladi. Asosiy vazifalar — nutqni manba tilida transkripsiya qilish va nutqni ingliz tiliga tarjima qilish. Bu vaznlari ochiq modelning identifikatsiyasi bo‘lib, xizmat sifatida taqdim etiladigan model identifikatori yoki o‘sha xizmatning yuklash va javob formatlari qoidalaridan alohida saqlanadi.\n\nLarge-v3 oldingi large Whisper checkpoint’lari arxitekturasini ikki ko‘rsatilgan o‘zgarish bilan saqlaydi: uning spektrogramma kirishi 80 o‘rniga 128 Mel chastota oralig‘idan foydalanadi va kanton tili tokenini qo‘shadi. Ishlab chiqaruvchining model kartasi bir million soat zaif belgilangan audio va Whisper large-v2 bilan tayyorlangan to‘rt million soat soxta yorliqli audio ustida o‘qitishni tavsiflaydi. Bu aralashma ikki o‘qitish epoxasi uchun ishlatilgan. Chiqarilish Whisper oilasidagi har bir modelning umumiy nomi emas, muayyan large-v3 checkpoint’idir.\n\nHujjatlashtirilgan inferensiya ish jarayoni standart holatda transkripsiya bajarib, yozuvda aytilgan tilni saqlaydi. U manba tilini avtomatik aniqlashi yoki ma’lum til tanlovini qabul qilishi mumkin. Tarjima uchun ko‘p tilli large model inglizcha bo‘lmagan nutqdan inglizcha matn natijasini beradi. Ushbu vazifani aniq tanlang: bir xil tildagi transkript va inglizcha tarjima turli natijalardir. Intervyu uchun asl tildagi so‘zlarni saqlang; inglizcha ma’lumotnoma hujjati uchun kerakli natija aytilgan kontent tarjimasi ekanini belgilang.\n\nAudioni tayyorlash kirish yo‘lining bir qismidir. OpenAI yuklovchisi yozuvni mono to‘lqin shakliga dekodlaydi va 16 kHz’ga qayta namunalaydi. Modelning akustik qabul maydoni 30 soniya. Bu butun intervyu yoki ma’ruzaning maksimal uzunligi emas, qayta ishlash oynasidir. Uzun material inferensiyasi sirpanuvchi oynalar orqali ketma-ket oldinga yurishi yoki uzun yozuvni ustma-ust tushadigan bo‘laklarga ajratib, ularning transkriptlarini birlashtirishi mumkin. Ishlab chiqaruvchining model kartasi aniqlik ustuvor bo‘lganda ketma-ket qayta ishlashni, bitta uzun yozuvni tayyorlash vaqti muhimroq bo‘lganda esa bo‘laklab qayta ishlashni tavsiflaydi.\n\nChiqarilgan konfiguratsiya alohida dekodlash ketma-ketligi uchun 448 dekodlovchi pozitsiyasini ko‘rsatadi. Uzunroq yozuvlar bu sonni umumiy transkript limiti deb hisoblash bilan emas, uzun material jarayoni orqali qayta ishlanadi. Hujjatlashtirilgan ish jarayoni tanilgan matn bilan birga segment yoki so‘z darajasidagi vaqt belgilarini qaytarishi mumkin. Bu vaqt natijalari yozuv bo‘ylab o‘tish va subtitrlar tayyorlash uchun foydali, ammo yakuniy subtitr konteyneri yoki transkript fayli formati alohida chiqishni formatlash tanlovidir. Transkript asl materialga moslashtiriladigan bo‘lsa, audio tayyorlashda yozuvning vaqt tuzilishini saqlang.\n\nWhisper’ning mo‘ljallangan vazifasi nutqni tanish va tarjima qilishdir. Ma’ruza transkripti, intervyu yozuvi yoki qidirish mumkin bo‘lgan og‘zaki materiallar to‘plami amaliy chiqishdir; matnni qisqacha bayon qilish yoki talqin qilish boshqa keyingi bosqich bo‘lishi mumkin. Model kartasi ishonchli avtomatik so‘zlovchi tasnifi yoki diarizatsiyani bazaviy checkpoint’ning standart chiqishi sifatida belgilamaydi. Vazifaga so‘zlovchi yorliqlari kerak bo‘lsa, bu talab aniq transkript bor degan da’vodan alohida hal qilinishi kerak. Foydali topshiriq manba tilini, transkripsiya yoki inglizcha tarjima kerakligini va vaqt ma’lumoti zarurligini ko‘rsatadi.\n\nMuhim cheklovlar hech qachon aytilmagan to‘qima matn va takroriy matnni o‘z ichiga oladi; bular shovqinli ma’lumotlarda zaif nazoratli o‘qitish va ketma-ketlikdan ketma-ketlikka tuzilishi bilan bog‘liq. Tanish sifati tillar, aksentlar va shevalar bo‘yicha farqlanadi, o‘qitish materiali kamroq joylarda natijalar zaifroq bo‘ladi. Shuning uchun moslikni baholash uchun ishlatiladigan namunada shovqin, sohaga xos lug‘at va ustma-ust nutq aks etishi kerak. Ismlar, miqdorlar va muhim bayonotlarni yozuvga solishtiring va ravon transkriptni har bir so‘z eshitilganining isboti deb hisoblash o‘rniga, nutqsiz parchalar yoki takroriy satrlarni tekshiring.\n\nTo‘liq natija tanilgan matnni, uning tilini va so‘ralgan vaqt ma’lumotini asl yozuvga havola bilan saqlaydi. Transkripsiyani keyingi tahrirdan ajrating: nashr uchun tuzatilgan transkript to‘ldiruvchi so‘zlarni olib tashlashi mumkin, tanilgan transkript esa model haqiqatan qaytargan narsani saqlaydi. Large-v3’ning identifikatsiyasi va tarjima imkoniyati tezroq Turbo nashridan ham alohida qolishi kerak. Haqiqiy qayta ishlash tezligi til, nutq sur’ati, uskuna va inferensiya usuliga bog‘liq; checkpoint nomining o‘zidan xizmatning muayyan kechikishi, maksimal yuklash hajmi yoki yoqilgan ilova yo‘li kelib chiqmaydi.", "alias_of": null, "aliases": [], "author": "OpenAI", "available": null, "canonical_model": "openai/whisper-large-v3", "capabilities": { "context_length": 0, "manufacturer_input": [ "audio" ], "manufacturer_output": [ "transcription" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.000015", "amount_unit": "usd_per_audio_input_second", "billing_input": true, "estimate_only": false, "key": "audio_input_second", "label": "audio input second", "native_amount_usd_per_unit": "0.000015", "native_quantity": 1, "public_unit_label": "per audio input second", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "audio_input_second" } ], "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." } ], "ctx": null, "declared_tariff_references": [ { "client_selectable": false, "key": "ee57dbbfe5ae56bdd0d7", "label": "OpenAI: Whisper Large V3", "profiles": [ { "basis": "", "dimensions": { "conditions": "Standart konfiguratsiya" }, "label": "OpenAI: Whisper Large V3 · Standart sozlama", "units": [ { "amount": "0.0000150", "amount_unit": "usd_per_exact_physical_unit", "billing_input": false, "key": "e474c121b23c5635611dfde4", "label": "Input audio seconds", "public_unit_label": "per input audio second", "quantity": "1", "rate_role": "declared_tariff_rate", "side": "in", "unit": "input_audio_second" } ] } ] } ], "entity_type": "model", "formats": [ "transcriptions" ], "id": "openai/whisper-large-v3", "input": [ "audio" ], "kind": "transcription", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Complete original JSON, all available text/segments/words/speakers/timestamps and usage.", "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "actual returned task_id or media library record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Dastlabki operation_id/X-Client-Request-ID va dastlabki javobni saqlang. Qayta yubormasdan haqiqiy, egalik qilinadigan vazifa/aktiv yozuvlarini o‘qing; audio holatini so‘rash uchun maxsus URL tuzilmaydi." }, "material_combinations": [ { "content_types": [ "application/json", "multipart/form-data" ], "mode": "speech_to_text", "required": [ "actual matching-format audio file" ] } ], "outputs": { "all_results": "To‘liq dastlabki JSON, mavjud barcha matn/segmentlar/so‘zlar/so‘zlovchilar/vaqt belgilari va foydalanish ma’lumoti.", "family": "transcription", "fields": [ { "name": "text", "type": "string" }, { "name": "usage", "type": "object" }, { "name": "segments[]", "optional": true, "type": "object" }, { "name": "words[]", "optional": true, "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "enum": [ "openai/whisper-large-v3" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "data_encoding": "base64 raw actual audio bytes, not data URI", "fields": [ "data", "format" ], "name": "input_audio", "required": true, "type": "object" }, { "availability": "retained_official_STTRequest", "description": "ISO-639-1 code; omitted means auto-detection", "name": "language", "required": false, "type": "string" }, { "availability": "retained_official_STTRequest", "maximum": 1, "minimum": 0, "name": "temperature", "required": false, "type": "number" }, { "availability": "retained_official_STTRequest", "default": "json", "default_known": true, "description": "verbose_json requires exact model/provider support", "enum": [ "json", "verbose_json" ], "enum_known": true, "name": "response_format", "required": false, "type": "string" }, { "availability": "retained_official_STTRequest", "description": "Only used with supported verbose_json", "items": { "enum": [ "segment", "word" ], "type": "string" }, "name": "timestamp_granularities", "required": false, "type": "array" }, { "availability": "retained_official_STTRequest", "description": "Data policy and exact provider options; order/only/ignore are not applied to STT", "name": "provider", "required": false, "type": "object" } ], "request_schema": { "properties": { "input_audio": { "properties": { "data": { "contentEncoding": "base64", "type": "string" }, "format": { "enum": [ "wav", "mp3", "flac", "m4a", "ogg", "webm", "aac" ], "type": "string" } }, "required": [ "data", "format" ], "type": "object" }, "language": { "description": "ISO-639-1 code; omitted means auto-detection", "type": "string" }, "model": { "enum": [ "openai/whisper-large-v3" ], "type": "string" }, "provider": { "description": "Data policy and exact provider options; order/only/ignore are not applied to STT", "properties": { "data_collection": { "enum": [ "allow", "deny", null ], "type": [ "string", "null" ] }, "options": { "type": "object" }, "zdr": { "type": [ "boolean", "null" ] } }, "type": "object" }, "response_format": { "default": "json", "description": "verbose_json requires exact model/provider support", "enum": [ "json", "verbose_json" ], "type": "string" }, "temperature": { "maximum": 1, "minimum": 0, "type": "number" }, "timestamp_granularities": { "description": "Only used with supported verbose_json", "items": { "enum": [ "segment", "word" ], "type": "string" }, "type": "array" } }, "required": [ "model", "input_audio" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": true, "body": { "input_audio": { "data": "${BASE64_ACTUAL_WAV_BYTES}", "format": "wav" }, "model": "openai/whisper-large-v3" }, "content_type": "application/json" } }, "hints": [ "Aniq model va hujjatdagi kirish maydonlaridan foydalaning.", "To‘liq javobni, har bir haqiqiy chiqish natijasini va dastlab qaytarilgan ID larni saqlang." ], "input": { "JSON_audio": "haqiqiy xom audio baytlari base64 ko‘rinishida va mos format", "content_types": [ "application/json", "multipart/form-data" ], "default_response_format": "json", "material_types": [ "audio" ], "multipart_cap_bytes": 26214400, "multipart_required": [ "model", "file" ], "optional_controls": [ "language", "temperature", "response_format", "timestamp_granularities", "provider" ], "optional_features": "Batafsil chiqish, diarize/keyterms va URL/kattaroq ichki audio uchun aynan shu model/provayder imkoniyati va tarifi zarur.", "required": [ "model", "input_audio" ] }, "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "Amalda qaytarilgan task_id yoki media kutubxonasidagi yozuv", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Retain original operation_id/X-Client-Request-ID and original response. Read actual owned task/asset records without resubmitting; no dedicated audio poll URL is constructed." }, "method": { "http": "POST", "path": "/v1/audio/transcriptions" }, "method_aliases": [], "operation_modes": [ "speech_to_text" ], "output": { "all_results": "Complete original JSON, all available text/segments/words/speakers/timestamps and usage.", "family": "transcription", "format": "json" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quantity_basis": "actual physical units", "quoted_unit": "per audio input second" }, "label": "Ultra", "units": [ { "amount": "0.000015", "amount_unit": "usd_per_audio_input_second", "billing_input": true, "estimate_only": false, "key": "audio_input_second", "label": "audio input second", "native_amount_usd_per_unit": "0.000015", "native_quantity": 1, "public_unit_label": "per audio input second", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "audio_input_second" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_AUDIO_FILE:?Set the path to your actual WAV recording}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\nimport base64\naudio=Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4]==b'RIFF' and audio[8:12]==b'WAVE'): raise SystemExit('Supply an actual WAV audio file')\nif len(audio)>26214400: raise SystemExit('Use a documented supported large-file flow')\nrequest={'model': 'openai/whisper-large-v3', 'input_audio': {'data': '${BASE64_ACTUAL_WAV_BYTES}', 'format': 'wav'}}\nrequest['input_audio']['data']=base64.b64encode(audio).decode('ascii')\nPath('request.json').write_text(json.dumps(request,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o response.bin\n# Keep the complete original response and headers. Read every returned segment, word and usage field.\n# Preserve actual operation/task/asset IDs; continue reads of the original result without resubmitting.\n", "label": "curl · exact native body and complete result", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "transcriptions", "hints": [ "Use the exact model and documented input fields.", "Keep the complete response, every actual output and original returned IDs." ], "http": "POST", "in": null, "input": [ "audio" ], "label": "Распознавание речи", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": null, "output": [ "transcription" ], "path": "/v1/audio/transcriptions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "audio_input_second": 1.5e-05 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quantity_basis": "actual physical units", "quoted_unit": "per audio input second" }, "label": "Ultra", "units": [ { "amount": "0.000015", "amount_unit": "usd_per_audio_input_second", "billing_input": true, "estimate_only": false, "key": "audio_input_second", "label": "audio input second", "native_amount_usd_per_unit": "0.000015", "native_quantity": 1, "public_unit_label": "per audio input second", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "audio_input_second" } ] } ], "request_model_id": "openai/whisper-large-v3", "route_label": "Ultra", "status_reads": [ "GET /v1/media/tasks/{task_id}", "GET /v1/media/assets/{asset_id}/content" ], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.000015", "amount_unit": "usd_per_audio_input_second", "billing_input": true, "estimate_only": false, "key": "audio_input_second", "label": "audio input second", "native_amount_usd_per_unit": "0.000015", "native_quantity": 1, "public_unit_label": "per audio input second", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "audio_input_second" } ] } ], "model_aliases": [ { "canonical_model_id": "openai/whisper-large-v3", "request_model_id": "openai/whisper-large-v3", "route_label": "Ultra" } ], "name": "OpenAI: Whisper Large V3", "price": { "in": null, "in_label": null, "out": null, "out_label": null, "sort_in": null, "sort_out": null, "varies": true }, "pricing_profiles": [ { "applicability": { "client_selectable": false, "native_profile_selection_pending": true, "scope": "hosted_reference" }, "dimensions": { "variant": null }, "label": "Ultra", "units": [ { "amount": "0.0000616666666666", "amount_unit": "usd_per_audio_input_second", "conditions": { "variant": null }, "key": "pricing.prompt", "label": "prompt", "profile_index": 0, "quantity": "1", "rate_role": "binding_component_rate", "side": "in", "unit": "audio_input_second" }, { "amount": "0", "amount_unit": "usd_per_not_separately_charged_output", "conditions": { "variant": null }, "key": "pricing.completion", "label": "completion", "profile_index": 0, "quantity": "1", "rate_role": "binding_component_rate", "side": "out", "unit": "not_separately_charged_output" } ] } ], "public_id": "openai/whisper-large-v3", "short": "Whisper large-v3 — transkripsiya va nutqni ingliz tiliga tarjima qilish uchun OpenAI’ning vaznlari ochiq ko‘p tilli nutqni tanish checkpoint’i. 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