Every call. Every word.
Everything Labelf does with voice data starts with transcription. If the transcript is wrong, every model trained on it is wrong. That's why we built our own — fine-tunable per customer, with speaker separation and industry vocabulary support.
High-precision transcription with speaker separation.
Hi, welcome to customer service, you're speaking with Sandra. How can I help you?
Hi, yeah I'm calling about my router. It stopped working since Monday. I have a ProBox 500 and it just keeps blinking red.
I understand, that sounds frustrating. I can see here that you have broadband 500 Mbit with a ProBox 500 router. Have you tried restarting it by unplugging the cable for 30 seconds?
Yeah I've done that like five times now. It doesn't help. My neighbor switched to another provider and has no issues at all, so I'm starting to wonder if I should do the same.
Not a generic model. Yours.
Generic transcription models don't know your product names, your brand terms, or your industry jargon. Labelf's transcription can be fine-tuned to learn your specific vocabulary — "ProBox 500" instead of "pro box five hundred", "StreamPlus" instead of "the streaming add-on".
Teach it your language.
Every industry has jargon. Every company has product names. Generic models get them wrong. Labelf learns yours.
Correct it once. It learns forever.
When you spot a transcription error, fix it right in the conversation view. Those corrections feed back into the model — it learns from every edit and gets better over time. Word Error Rate is calculated automatically so you always know exactly how accurate it is.
Edit in conversation view
See a wrong word? Click it, fix it, done. No export, no separate tool. Corrections happen right where you're already working — in the conversation browser.
Model retrains on corrections
Every correction becomes training data. The transcription model learns your vocabulary, your accents, your audio environment. It gets better with every edit your team makes.
WER tracked automatically
Word Error Rate is calculated from your corrections. Watch it drop over time as the model learns. Know exactly how accurate your transcriptions are — not a guess, a measurement.
Bad transcription poisons everything.
Every model you train reads the transcript. If "ProBox 500" is transcribed as "pro box five hundred", your product classification model learns the wrong thing. If "StreamPlus" becomes "the streaming add-on", your search can't find it. Transcription quality is the foundation of everything.
Every word matters. We get them right.
High-precision transcription is the foundation. Everything else — classification, search, coaching, playbooks — depends on getting the words right.
Languages supported
Accuracy after fine-tuning
Average integration window
