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Transcribe an interview with speaker labels (diarization)

Transcribe the famous Apollo 13 "Houston, we've had a problem" exchange and label who speaks when (Speaker 1, Speaker 2) with timestamps and a speaker count, using Deepgram nova-3. Use it for interviews, podcasts with guests, meetings and call recordings where you need to know who said what.

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Audio Transcription - Speech to Text, Audio to Text, Whisper
Audio Transcription - Speech to Text, Audio to Text, Whispertidytools/audio-transcriber
Audio
Episode
OK
Language
+6 fields
Text
Number
Boolean
List
Object

Input

Audio or video file URLs:https://upload.wikimedia.org/wikipedia/commons/6/6f/Apollo13-wehaveaproblem.ogg
Speaker labels (who said what):true
Vocabulary hints (optional):Houston, Apollo, Odyssey, Aquarius

Output fields

Audio
Episode
OK
Language
Duration (s)
Billed min
Transcript
Summary
SRT
Error

How it works

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The Audio Transcription - Speech to Text, Audio to Text, Whisper Actor is ready to run with the inputs already set for "Transcribe an interview with speaker labels (diarization)". Sign up or log in and you'll land in Apify Console with a copy of this exact task.

Start the "Transcribe an interview with speaker labels (diarization)" task

The Audio Transcription - Speech to Text, Audio to Text, Whisper Actor will start running based on the input automatically. You can also change all the input fields, like audio or video file urls, speaker labels (who said what), and more, to match your specific use case.

Receive the "Transcribe an interview with speaker labels (diarization)" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as audio, episode, OK, and more.

Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "Transcribe an interview with speaker labels (diarization)" task via API.

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