Transcribe Assistant-AI transcription for audio files

Example

During an interview, all spoken words, including filler words like 'uh' and 'um,' are captured exactly as they are said.

Scenario

A journalist records an interview with a political figure. The journalist needs an accurate, word-for-word record to analyze the speaker's exact language and tone. Transcribe Assistant provides a detailed transcription, including pauses and hesitations, which may be important for interpretation.

Example

Noting non-verbal cues such as laughter, coughing, or background noise.

Scenario

In a podcast recording, the host and guest frequently laugh, creating moments that shape the tone of the conversation. Transcribe Assistant marks '[laughter]' in the transcription to reflect the mood of the dialogue, adding context that a text-only record would miss.

Example

Transcribe Assistant might note '[inaudible]' or offer a guessed transcription based on the surrounding context.

Scenario

In a conference call where participants speak over each other or background noise interferes, Transcribe Assistant flags unclear sections and provides an estimated guess. This helps the user review and correct potential issues, saving time in post-editing.

Example

When encountering unclear audio, Transcribe Assistant seeks input from the user to refine the transcription.

Scenario

In a legal deposition, accuracy is crucial. If a witness speaks softly or mumbles, Transcribe Assistant highlights the unclear sections for review. The lawyer or stenographer can then provide the missing information to ensure the transcript is legally sound.

Example

Transcribe Assistant can distinguish between multiple speakers, labeling them as 'Speaker 1,' 'Speaker 2,' etc.

Scenario

In a group discussion with several participants, such as a focus group, it’s essential to know who said what. Transcribe Assistant assigns labels to different voices, making it easy to track contributions from each speaker.