Ko te nuinga o ngā taputapu ka taka puku i runga i te 2 hāora, 2 GB rānei. Ka wehewehe tā mātou paipara i ngā kōnae roa ki ngā ara tukatuka whakarara, ka whakahuihui anō ki ngā tohu wā kua whakatika, ka whakaputa i tētahi tuhinga whakamāori kotahi e rite ana — kei te ora tonu ngā tapanga kaikōrero. Ko Pro me Business e whakaae ana ki te 10 hāora ia kōnae. Ka tāpiri a Business i te tawhē rahi ake (5 GB ki te 2 GB) me te tūtira matua.
MP3 · WAV · M4A · MP4 · MOV · MKV · OGG · OPUS · FLAC · WEBM — up to 100 MB anonymously
YouTube · TikTok · Vimeo · Twitter · SoundCloud · Spotify · 50+ more
↓ Watch what happens
Drop a file, paste a URL, or record live — text appears back with speaker labels and timestamps. Same pipeline whether the source is a file, a URL, or your microphone.
Thanks for making the time. I want to start with framing — what was the original hypothesis behind the project?
Honestly, maybe forty percent. The shape held — the mechanics underneath had to be rebuilt almost entirely.
What flipped it for you? Was there one customer call, one piece of data
↓ This is the dashboard
Same layout as the real dashboard — Summary, full Transcript, Speakers tab, Exports. Key points and action items extracted automatically. Auto-tags on every job.
Sample preview from a founder interview about post-call workflow. Real transcripts look exactly like this — same tabs, same summary block, same key-points / action-items split, same auto-tag chips.
Three real options · honest comparison
Three legitimate ways to get text from spoken audio in 2026. Each is best for different work. Honest numbers below — no claim that AI matches a professional human transcriber on hard audio.
Live, free, on-device. Handy for short bursts while you talk.
~30× faster than realtime. 100+ languages. Speaker labels. The sweet spot for most work.
Gold-standard accuracy when the audio is hard or the stakes are legal.
Built-in dictation figures from public iOS / Android speech API benchmarks. Human transcriber rates from US/UK industry surveys 2024–2025.
Accuracy · real-world numbers
Modern transcription reaches 95%+ word accuracy on clear English at 128 kbps and above, comparable to a human transcriber on the same recording. The audio coming in sets the ceiling — cleaner source, cleaner transcript. The breakdown below covers the recordings we actually see in production.
USB or studio microphone in a treated room. Single speaker at conversational distance. The headline number.
Podcast masters, interview recordings, well-mic'd meetings. The sweet spot for most professional work.
Field-recorded interviews, podcast episodes at 64–128 kbps, multi-speaker recordings. Usable for editorial without a review pass.
Ceiling mic, omnidirectional capture, mild reverb, multiple speakers at distance. Plan a rename pass on the speaker chips.
Common questions
Tuku ake tae atu ki te 5 GB, 10 hāora te roa. Kāore he wehewehe, kāore he mutunga, ngā tapanga kaikōrero puta noa.
Tīmata whakamāori utukore