Free Kinepali Transcription
Transcribe Kinepali audio and video to text with AI. Fast, accurate, and free.
Jinsi Inavyofanya Kazi
- Nendeni kwenye mikutano Free.ai Transcriber
- Upload your Kinepali audio or video file
- Our AI automatically detects Kinepali and transcribes it
- Pakua nakala zako zikiwa maandishi au maandishi madogo - madogo ya SRT
Kinepali Transcription Features
- ✓Uwezo wa BARUA0 (MIT)
- ✓Automatic Kinepali language detection
- ✓Waunga mkono wabunge3, WAV, MP4, M4A, FARAC, na wengine wengi
- ✓Vipanga vya kusafiri nje ya nchi na sehemu ndogo za nchi hiyo (SRT)
- ✓Hakuna sheria za ukubwa wa faili juu ya mipango ya malipo
- ✓Mafaili ya kibinafsi na salama; faili hufutwa baada ya kukaguliwa
Maelezo ya Lugha
| Lugha | Kinepali |
| MELEKEO | ne |
| Gari la Kuigwa | KUBUMISHA |
| Bei | Huru |
Lugha Zaidi
Ona Lugha ZoteFAQ
Kinepali is a less-resourced language for Whisper - large-v3-turbo sits above 25% word error rate, sometimes well above. The transcript is useful for search and gist but should not be treated as publication-ready. If a higher-accuracy engine becomes available for Kinepali we wire it in automatically. (Tier D, over 25% word error rate on benchmark sets - we publish honest WER tiers rather than marketing claims.)
Yes - Kinepali transcription draws from your daily free token pool first. Audio costs about 50 tokens per minute, so the anonymous daily pool covers a few hours of audio per day. Signed-in accounts get a larger 30,000-token daily pool. Past that, transcription is pay-as-you-go, with token top-ups from $1.
Kinepali transcripts are returned in Devanagari script (UTF-8).
MP3, WAV, M4A, FLAC, OGG, OPUS, and WEBM are accepted directly. For video (MP4, MOV, MKV) we extract the audio track server-side before sending it to Whisper - you do not need to convert anything yourself. Same pipeline regardless of source language, including Kinepali.
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 Kinepali recordings (podcasts, full lectures, meetings) work fine.
Yes - speaker diarization is on by default for every Kinepali transcript. The output is segmented as Speaker 1 / Speaker 2 / Speaker 3 with timestamps, so interviews, panel discussions, and multi-party meetings come back labeled. Diarization runs on a separate model and works the same across all languages we support.
Yes - paste the URL into /transcribe/youtube/ for YouTube or /transcribe/podcast/ for podcast feeds (Apple, Spotify, RSS). We download the audio, run it through Whisper with language=ne, and return the transcript with timestamps and speaker labels. Typical Kinepali content: lectures, interviews, voice notes, and YouTube content in Kinepali all work - paste a URL into /transcribe/youtube/ or upload the file directly.
HIPEPH (Utumizi wa nyota) 120003 hugharimu takriban ishara 50 kwa dakika moja ya sauti, kwa hiyo, saa moja ya kurekodi ni ishara za mchana. Watumiaji wengi hawatumii chochote kile kila siku kwa ishara 30,000 hufunika vidoka vifupi, sauti, na sauti moja ya saa.
Yes - both segment-level (every ~10-30 seconds) and word-level timestamps are available. Word-level is the default for VTT/SRT subtitle export so the captions sync line-by-line. On the API set timestamps="word" in the request body. Kinepali transcripts are returned in Devanagari script (UTF-8).
Yes. POST audio (multipart/form-data, field name "file") to /v1/transcribe/ with language=ne - or omit 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/.
Yes - once transcription finishes, click Translate or paste the text into /translate/. Kinepali pairs with every other language we support (200+). For meeting minutes pipe the transcript through /summarize/; for dubbing send it to /voice/tts/ to render audio in the target language.
Whisper's noise training helps less at this tier - the bottleneck is the amount of Kinepali 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.Kama nakala itarudi nyuma bila kuweza, barua pepe wasiliana na@free.ai kwa faili tutarekebisha alama hizo na kuangalia kama injini tofauti inashika sauti yako vizuri zaidi.