Our transcription service turns audio and video into text your team can train on: verbatim or clean, with timestamps and speaker labels, in languages and accents from around the world. A review pass runs before anything is delivered, so what you get isn't raw speech-to-text output.
S1
S2
Transcription for AI turns recorded speech into text that a speech model can train on or be tested against. Each transcript stays paired with its audio, so words, pauses, speaker changes and background sounds all have to be written the same way every time.
That's a different job from meeting notes or subtitles, which are written for people to read. A reader doesn't miss a dropped "um" or a tidied sentence. A speech recognition model trained on that text learns that those sounds were never there.
Speech-to-text software can give you a first draft. For training data, someone who knows the language and the accent still has to get it right, and noisy calls and speech that switches languages are where automatic drafts tend to slip. Our speech data page takes one recording apart, from the audio to the speaker details. For how this differs from transcription written for people, see the transcription guide.
Verbatim or clean, timestamped and speaker-labeled, in the languages and accents you need. The work happens in our own Transcription tool. The recordings can come from you, or from our data collection and conversational AI data services.
Verbatim keeps the ums, false starts and repeats. Clean strips them out so the text reads like writing. Tell us what your model will do and we'll say which fits.
For speech recognition (verbatim), call search and summaries (clean)
Our tool: Transcription Free sample of verbatim transcription
Every line tied to the moment it was said, so text and audio line up when your model trains on both.
For speech recognition, subtitles and audio search
Our tool: Transcription Free sample of timestamped transcripts
Each turn marked with who said it, including the moments when two people talk over each other.
For call analytics, meeting notes and voice bots
Our tool: Transcription Free sample of speaker-labeled transcripts
Noise, laughter, music and silence marked with the tags you choose, so your model learns what isn't speech too.
For speech recognition in noisy places
Our tool: Transcription Free sample of transcripts with sound tags
Speech in India often mixes languages inside one sentence. We agree how it gets written before the first file, and keep it that way.
For speech models for Indian users
Our tool: Transcription Free sample of mixed-language transcription
The speech in your video transcribed and timed to the picture, from interviews and screen recordings to street footage.
For video understanding and multimodal models
Our tool: Transcription Free sample of video transcription
The samples on these cards are illustrations, not client data.
Common jobs for transcribed speech, and what each one needs from the transcript. They're examples of the work, not a client list.
Verbatim transcripts paired with their audio teach a model to turn speech into text, fillers and false starts included.
Real calls, transcribed with speaker labels, show a bot how callers actually phrase things, which is rarely how the script says they will.
Agent and customer kept apart in the transcript, so a model can learn who asked what and how the call was handled.
A reviewed transcript is the reference that speech-to-text output gets scored against. If the reference is wrong, so is the score.
Speech in video written down with timestamps that line up with the footage, so text and picture stay in step.
Hindi and English in one sentence, written the way you decide: the original scripts, one script, or Latin letters.
Write these down once and every transcriber follows the same rules, so the dataset stays consistent from the first file to the last.
Whether they're written as words or digits, and in which format. Decide it once, or every transcriber picks their own.
One spelling for every name, brand and technical term, kept in a list the whole team works from.
What to write when a stretch can't be made out, so a guess never slips into your training data.
How to mark who's speaking, and what to do when two people talk at once. Non-speech sounds like noise, laughter or silence get their tags here too.
A transcriber has to understand what's being said. That means the language, the regional accent and the vocabulary of the topic. A banking call and a hospital call use different words even when the language is the same.
Speech in India often mixes languages inside a single sentence. Whether that gets written in the original scripts, in one script or in Latin letters is a decision to make early, and our page on Indian language data goes into it.
You'll have one named person the whole way through. A transcription job runs like this.
You send some sample audio and tell us what the transcripts are for.
The NDA comes before any of your audio moves, and it's used only for the scope we agreed.
We settle verbatim or clean, speaker marks, tags, numbers and spelling.
We transcribe a small sample free, so you can check it before you commit.
Transcribers who know the language and the accent work in our Transcription tool.
A multi-level review runs before anything is delivered.
The words you'll run into when you plan a transcription project, and what each one means.
One team can record the audio, transcribe it and take it further. You can also compare all eight services. Example category: healthcare conversation speech, transcribed and reviewed.
Speech recorded by people who match your users, then transcribed here.
Call and dialogue audio for voice bots, IVR and support lines.
Intents, entities and sentiment added on top of the transcripts.
Transcripts carried into the other languages your model has to handle.
Your speech model's output checked against reviewed transcripts.
Short answers about transcripts from us. The services FAQ covers the other services and how they fit together.
Verbatim keeps everything that was said, including fillers and false starts. Clean removes them so the text reads like writing. Verbatim usually suits speech recognition training, and clean suits tasks where meaning matters more than delivery.
Not on its own. Transcribers who know the language and the accent do the work in our own Transcription tool, and a multi-level review runs before delivery, so what you get isn't raw speech-to-text output.
Yes, and that goes well past Hindi and English. Transcribers who know the language and the accent do the work, so tell us which ones you need.
We agree the rule with you first: the original scripts, one script, or Latin letters. Every transcriber then follows it, and the review pass checks it.
Yes. We deliver timestamped, speaker-labeled transcripts for downstream training.
From you, or from our data collection and conversational AI data services, so one team can handle both the recording and the transcript.
Some sample audio, the languages, what the transcripts are for and a rough volume in hours. If you already have transcription rules, send them. If you don't, we settle them with you before the work starts.
It depends on the hours of audio, the languages, how clear the recordings are and how detailed the rules are, so there's no standard turnaround we can promise. The free sample is the quickest way to see the work, and the timeline gets agreed along with the full scope.
Yes. Send a short brief with the languages, what the transcripts are for and roughly how much audio there is, and we'll transcribe a small sample free before you commit.
Yes. The NDA comes before any of your audio moves. We use it only for the scope we agreed, only the people working on your project can get to it, and it's returned or deleted when you ask.
Say what the transcripts should include, add the languages and roughly how much audio there is, and send it. We'll transcribe a small sample free before you commit.
This opens your email app with the brief filled in. Nothing is sent until you press send. You can also use the contact page.