Free Turkish Transcription
Transcribe Turkish audio and video to text with AI. Fast, accurate, and free.
Transcribe Turkish Audio Now
Аудио ёки видео файлингизни юклаб олинг ва секундлар ичида матнли транскриптни олинг.
Open TranscriberУ қандай ишлайди
- Go to the Free.ai Transcriber
- Upload your Turkish audio or video file
- Our AI automatically detects Turkish and transcribes it
- Транскриптингизни матн ёки SRT субтитрлари сифатида юклаб олиш
Turkish Transcription Features
- ✓faster-whisper (MIT лицензияси билан) томонидан қўллаб-қувватланади
- ✓Automatic Turkish language detection
- ✓MP3, WAV, MP4, M4A, FLAC ва бошқаларни қўллаб-қувватлайди
- ✓Вақт белгилари ва субтитрларни экспорт қилиш (SRT)
- ✓Файл ҳажми чекловлари йўқ
- ✓Шахсий ва хавфсиз -- файллар ишловдан сўнг ўчирилади
Тил тафсилотлари
| Тил | Turkish |
| ISO коди | tr |
| AI модели | тезроқ-шўхлик |
| Нарх | Оқ |
Кўпроқ тиллар
Барча тилларни кўришКўп бериладиган саволлар
Whisper large-v3-turbo handles Turkish solidly — 7-15% word error rate on benchmark audio. Expect occasional substitutions on named entities, numbers, and dense technical vocabulary; the bulk of the transcript will be correct. (Tier B, 7-15% word error rate on benchmark sets — we publish honest WER tiers rather than marketing claims.)
Yes — Turkish 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 pool plus 10,000 signup tokens. Past that, $1 buys 750,000 tokens (~250 hours of audio).
Turkish transcripts are returned in standard UTF-8 with the language's normal orthography.
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 Turkish.
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 Turkish recordings (podcasts, full lectures, meetings) work fine.
Yes — speaker diarization is on by default for every Turkish 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=tr, and return the transcript with timestamps and speaker labels. Typical Turkish content: news clips, sermons, lectures, and political interviews in Turkish are the most common workloads; paste a YouTube URL into /transcribe/youtube/ or upload the file.
Whisper costs about 50 tokens per minute of audio, so a one-hour recording is ~3,000 tokens. $1 buys 750,000 tokens, which works out to roughly 250 hours of audio per dollar. Most users never spend anything — the free daily pool covers short clips, voice notes, and one-off podcasts.
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. Turkish transcripts are returned in standard UTF-8 with the language's normal orthography.
Yes. POST audio (multipart/form-data, field name "file") to /v1/transcribe/ with language=tr — 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/. Turkish 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 is trained on hundreds of thousands of hours of real-world audio, so it tolerates background noise and phone-quality recordings on Turkish. For best results, supply clean audio (headset mic, no music bed) — at this tier noise compounds the baseline error rate. If a transcript comes back unusable, email contact@free.ai with the file — we will refund the tokens and look at whether a different engine handles your audio better.