DD scopes transcripts for the way your team will use the recording. Agree languages, clean-read or verbatim style, speaker labels, timestamps, output format, and recording access before production starts.
This is not a public case study claim. It is DD-owned evidence a buyer can request when the work needs vendor review before a scope is approved.
Ask for proof detailsDD confirms transcription scope before production: language, speaker count, audio quality, transcript style (clean-read, verbatim, timecoded, speaker-diarized), output format, and the downstream use - whether the file feeds captions, legal reference, research review, or an AI data pipeline. That scope review prevents a research file, legal recording, training clip, or model dataset from receiving a transcript style that does not match its intended use.
The output style is determined by use, not assumed from the file type. A clean-read transcript removes filler and false starts for readability. A verbatim transcript preserves every utterance for legal or research accuracy. A timecoded transcript anchors text to the audio for caption production or review. A speaker-diarized transcript attributes turns to named or labeled participants. DD confirms which style the downstream workflow requires before production begins.
Audio quality, speaker overlap, background noise, code-switching, dialects, and file length all affect transcription feasibility and effort. DD reviews a sample file at request review - before quoting - to identify these factors. If overlap, noise, or dialect creates a risk for accuracy, DD flags it before accepting the project and states what the accuracy expectation can realistically be for that audio.
For AI data pipelines: transcription output can be scoped to match the pipeline's schema, label format, and downstream annotation requirements. Speaker labels, timestamps, language flags, and quality notes are formatted to the buyer's specification. Rolling-batch delivery is available for ongoing pipelines - files are received incrementally and returned transcribed on a defined cadence.
For legal, clinical, or government recordings, agree confidentiality terms, authorized listeners, transfer platform, permitted downloads, retention, and evidence of access closure before sending files. Any browser restrictions depend on the chosen platform and project terms.
Transcript style and audio quality confirmed before production — not assumed from the file type at delivery.
Step by step
Submit an audio or video sample, language, speaker count, recording length, transcript style (clean-read, verbatim, timecoded, or speaker-diarized), output format, and downstream use.
DD reviews the sample before quoting. If overlap, background noise, dialect, or speaker count creates an accuracy risk, that is flagged before the project is accepted — not discovered mid-production.
The transcript style is confirmed against the downstream use: clean-read for readability, verbatim for legal accuracy, timecoded for caption production, speaker-diarized for research review. The right style is confirmed before work begins.
Output is formatted to the downstream specification. For AI data pipelines, schema, speaker labels, timestamps, language flags, and quality notes are matched to the agreed pipeline spec.
Confirm clean-read or verbatim output, timecodes, speaker labels, and receiving format for the intended use.
Share a representative segment so noise, overlap, dialect, and speaker count can be considered in the proposed scope.
For dataset work, specify schema, labels, review sample, and the cadence of any staged delivery.
Before sending sensitive audio, confirm confidentiality, authorized listeners, transfer platform, downloads, and deletion instructions.
How this compares
Send an audio sample, language, speakers, transcript style, and intended use. Ask for written feasibility, contact, and delivery terms.
DD supports clean-read (filler removed, readable), verbatim (every utterance preserved), timecoded (text anchored to the audio timeline), and speaker-diarized (turns attributed to named or labeled participants) transcripts.
DD reviews a sample file at request review before quoting. If overlap, background noise, dialect, accent, or code-switching creates an accuracy risk, it is flagged before the project is accepted - not discovered mid-production.
Yes. Transcription output can be scoped to match the pipeline's schema, label format, and annotation requirements. Speaker labels, timestamps, language flags, and quality notes are formatted to the buyer's specification.
Before sending sensitive recordings, agree NDA requirements, authorized listeners, transfer method, access end date, and evidence of closure for this engagement.
Timestamps are needed when the transcript must be traced to the recording - for caption production, legal reference, research review, or AI analysis where text must be anchored to a timecode.
Send an audio or video sample, language, speaker count, recording length, audio quality notes, transcript style, output format, downstream use, and any schema or label requirements if the transcript feeds an AI pipeline.
List speakers, timestamps, verbatim rules, and output requirements before requesting transcription. Prepare a transcription specification
AI dataSubtitling and captioningShare the language pair, file type, audience, or problem. DD replies with availability, open questions, handling notes, and the next step before work starts.
