Tatar e mahala
Ho ngola livideo le li-audio tsa Tatar ho ea ho li-text ka AI. Habonolo, ka nepo, le mahala.
Ho sebetsa joang
- E_la ho Motsamaisi oa ho ngola oa Free.ai
- Kopitsa faele ea hau ea audio kapa video ea Tatar
- AI ea rona e fumana Tatar ka ho toba'me e e ngola
- Kopitsa transcript ea hau e le tlhaloso kapa li-subtitles tsa SRT
Tatar Likarolo tsa ho ngola
- ✓E entsoe ke faster-whisper (license ea MIT)
- ✓Ho bona ka ho toba puo ea Tatar
- ✓E tšehetsa MP3, WAV, MP4, M4A, FLAC, le tse ling
- ✓Timestamps le ho romelloa ha lihlooho tse ka tlase (SRT)
- ✓Ha ho na liphelelo tsa boholo ba faele ka li-plans tse lefelloang
- ✓Secha le se sireletsehileng -- lifaele li tlosoa ka mor'a ho sebetsana
Lintlha tsa puo
| Senyesemane | Tatar |
| ISO Code | tt |
| Mofuta oa AI | faster-whisper |
| Theko | E le mahala |
Li-languages tse ling
Bona Li-Languages Tseo kaofelaLipotso tse ka etsahalang
Tatar ke puo e nang le li-resources tse nyane bakeng sa Whisper - large-v3-turbo e lula ka holimo ho 25% ea palo ea liphoso tsa mantsoe, ka linako tse ling e ka holimo. Li-transcript li molemo bakeng sa ho batla le ho ngola empa ha li lokela ho nkoa e le li-publishing-ready. Haeba e na le mochini o phahameng oa ho nepahala o fumanehang bakeng sa Tatar re tla e hokahanya ka ho toba.(Tier D, over 25% word error rate ka lihlopha tsa benchmark - re phatlalatsa li-tiers tsa WER tse tšepahalang ho feta li-claims tsa thekiso.)
Ha ho joalo - Tatar transcription e nka ho tloha letsatsi le letsatsi token pool mahala pele. Audio theko ka 50 tokens ka metsotsoana, ka hona pool letsatsi le letsatsi Anonymous e koahela lihora tse ling tsa audio ka letsatsi. Signed-in akhaonteng fumana 30,000-token letsatsi le letsatsi pool. Past hore, transcription ke pay-as-you-go, le token top-ups ho tloha $1.
Li-transcripts tsa Tatar li khutlisoa ka UTF-8 e tloaelehileng le ho ngola ka mokhoa o tloaelehileng oa puo.
MP3, WAV, M4A, FLAC, OGG, OPUS, le WEBM li amoheloa ka ho toba. Bakeng sa video (MP4, MOV, MKV) re tlosa li-track tsa audio ka lehlakoreng la mosebeletsi pele re li romella ho Whisper - ha u hloka ho fetola ntho efe kapa efe ka boeona. Pipeline e ts'oanang ntle le puo ea mohloli, ho kenyeletsoa Tatar.
Anonymous uploads cap at roughly 500 MB per file. Signed-in accounts go up to 2 GB. Duration is not a hard limit - long files are chunked automatically (30-second windows with overlap) and stitched back into a single transcript with continuous timestamps. Multi-hour Tatar recordings (podcasts, full lectures, meetings) work fine.
Ho na le - mohlophisi diarization ke ka ho feletseng ka ho feletseng bakeng sa Tatar transcripts tsohle. The output ke segmented joaloka Mohlophisi 1 / Mohlophisi 2 / Mohlophisi 3 le timestamps, kahoo lipotso, paneli lihlooho, le multi-party dibopeho tsa motheo ba tla khutla labelled. Diarization e tsamaea ka model e fapaneng le sebetsa ka tsela e tšoanang ka lipuo tsohle re tšehetsa.
E-na le hoo, re tla kenya URL ho /transcribe/youtube/ bakeng sa YouTube kapa /transcribe/podcast/ bakeng sa podcast feeds (Apple, Spotify, RSS). Re tla kenya audio, re e phethela ka Whisper le language=tt, ebe re khutlisa transcript le timestamps le li-labels tsa moqoqi. Lintlha tse tloaelehileng tsa Tatar: Lingoliloeng, lipotso, litlhaloso tsa puisano, le litaba tsa YouTube ka Tatar li sebetsa — kenya URL ho /transcribe/youtube/ kapa u romelle faele ka kotloloho.
Whisper costs about 50 tokens per minute of audio, so a one-hour recording is ~3,000 tokens. Most users never spend anything - the free daily pool of 30,000 tokens covers short clips, voice notes, and one-off podcasts. Beyond it, transcription is pay-as-you-go, with token top-ups from $1.
Ee - li-timestamps tsa boemo ba segment (ka ~10-30 sekontiri ka 'ngoe) le boemo ba mantsoe li fumaneha. Lefatše la mantsoe ke la morao-rao bakeng sa ho romelloa kantle ha li-subtitles tsa VTT/SRT ka hona li-captions li synchronize line-by-line. Ka API beha timestamps="word" ka'mele oa kopo. Li-transcripts tsa Tatar li khutlisoa ka UTF-8 e tloaelehileng le ho ngola ka mokhoa o tloaelehileng oa puo.
Yeah. POST audio (multipart/form-data, field name "file") to /v1/transcribe/ with language=tt - or leave out the language parameter to let Whisper auto-detect. Returns JSON with the transcript, segments, timestamps, and speaker labels. Full reference and SDK snippets at /api/.
E-ea — ha transcription e felile, tobetsa ho fetolela kapa ho kopanya tekanyo ho /translate/. Tatar e kopantsoe le lipuo tsohle tse ling tseo re li tšepang (200+). Bakeng sa lihora tsa kopano, transcript e fetisoa ka /summarize/; bakeng sa ho ngola ka letsoho, e romelle ho /voice/tts/ ho etsa molumo ka puo e loketseng.
Whisper' s noise training helps less at this tier - the bottleneck is the amount of Tatar audio Whisper saw during training, not noise. Clean studio audio still beats noisy audio, but neither will reach the accuracy you would get on a high- resource language.Haeba transcript e sa khone ho sebelisoa, romella lengolo-tsoibila contact@free.ai le faele — re tla lefa tokens'me re sheba hore na e mong oa li-engine tse fapaneng o sebetsana joang le audio ea hau ka ho fetisisa.