Free Lingala Transcription
Kushandura Lingala audio uye video kuita tebhu neAI. Yakanaka, yakakwana uye yemahara.
Maitiro Ekuita
- Tora ku Free.ai Transcriber
- Kuisa yako Lingala audio kana video faira
- Our AI otomatiki inowana Lingala uye inonyora
- Dhawunirodha transcript yako sechinyorwa kana SRT subtitles
Lingala Transcription Features
- ✓Yakagadzirwa ne faster-whisper (MIT licensed)
- ✓Kuwana Lingala rurimi otomatiki
- ✓Supports MP3, WAV, MP4, M4A, FLAC, uye zvimwe
- ✓Kutumira kunze kwenguva uye zvinyorwa (SRT)
- ✓Hapana faira resize limits pazvirongwa zvakabhadharwa
- ✓Private and secure -- files are deleted after processing
Deta yeChirungu
| Chirungu | Lingala |
| ISO Code | ln |
| AI Model | faster-whisper |
| Mutengo | _Dambudziko |
Mamwe Matauro
View All LanguagesFAQ
Lingala ndeimwe yemitauro ine zvishoma zvemabasa ekushandisa muWhisper — Large-v3-turbo ine 25% yemashoko asinganzwisisike, dzimwe nguva kusvika pa0. The transcript is useful for search and gist but should not be treated as publication-ready. If a higher-accuracy engine becomes available for Lingala we wire it in automatically.(Tier D, over 25% word error rate pa benchmark sets — isu tinoburitsa zvakarurama WER tiers kupfuura zvekutengesa zvikumbiro.)
Yeah — Lingala transcription inotora kubva pazuva nezuva pasina token pool yekutanga. Audio inodhura pamusoro 50 tokens paminuutti, sokuti anonyymi daily pool inosvika maawa mashoma eaudio pazuva. Signed-in accounts get a larger pool plus 10,000 signup tokens. Past that, $1 buys 750,000 tokens (~250 hours of audio).
Lingala transcripts inodzokera muUTF-8 standard nezita rezita.
MP3, WAV, M4A, FLAC, OGG, OPUS, uye WEBM zvinobvumidzwa zvakananga. Kuti uwane video (MP4, MOV, MKV) isu tinotora iyo audio track kubva panzvimbo ye server-side mushure mekutumira kune Whisper — iwe haufanire kushandura chero chinhu iwe pachako. Imwecheteyo nzira pasina kuenderana nezwi remutauro, kusanganisira Lingala.
Anonymus uploads cap panenge 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 Lingala recordings (podcasts, full lectures, meetings) work fine.
Yeah — mutauro diarization iri pa default yese Lingala transcript. The output is segmented se mutauro 1 / mutauro 2 / mutauro 3 ne timestamps, saka mubvunzo, panel misangano, uye multi-party misangano vachidzoka labelled. Diarization inofamba pane zvakasiyana model uye inoita sezviri pakati pezvose zvitauro isu tinotsigira.
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=ln, and return the transcript with timestamps and speaker labels. Typical Lingala content: lectures, interviews, voice notes, and YouTube content in Lingala all work — pedza URL mu /transcribe/youtube/ kana wedzera faira zvakananga.
Whisper mutengo pamusoro 50 tokens paminuutti audio, saka rimwe vhiki redhiyo kutamba nde ~ 3,000 tokens. $ 1 anotenga 750,000 tokens, iyo inoita kuti zvishoma 250 mazuva audio padhigirii. Vanhu vazhinji havana kushanyira chero chinhu - yemahara zuva nezuva pool anosvika zvishoma clips, mashoko, uye one-off podcasts.
Yeah — 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. Lingala transcripts inodzokera muUTF-8 standard nezita rezita.
Yeah. POST audio (multipart/form-data, field name "file") to /v1/transcribe/ with language=ln — 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/.
Yeah — kana transcription yapera, tinya Translate kana kuti nyora mu /translate/. Lingala inowirirana nesemwe rurimi tichitsigira (200+). Kuti uite misangano, nyora mu /summarize/; kuti uite dub, nyora mu /voice/tts/ kuti uite mashoko mu rurimi rwaunoda.
Whisper's noise training inobatsira zvakanyanya paiyi tier — iyo bottleneck ndeyekuwanda kwe Lingala audio Whisper yaona panguva yekudzidzisa, kwete noise. Clean studio audio still beats noise audio, but neither will reach the accuracy you'd get on a high-resource language.Kana transcript ichidzokera pasina kushandiswa, taura neemail ku contact@free.ai nefile — isu tichadzosera mari yechikwereti uye tichaona kana imwe injini inokwanisa kudzora mashoko ako zvakanaka.