Why plain transcripts are the wrong format for ChatGPT
Most transcription tools — Otter, Whisper's default output, the dictation in Google Docs — produce a wall of text. ChatGPT can read it, but it has to re-derive who is talking, when each turn started, and where one topic ends and another begins. On a 60-minute meeting, that re-derivation is unreliable: speaker attribution drifts, turn boundaries blur, and the model starts attributing decisions to the wrong person.
Markdown with explicit speaker headings (## Sarah Chen [00:14:22]) removes the guessing entirely. ChatGPT treats each speaker block as a discrete turn, can quote with confidence, and answers questions like "summarise everything Marcus said about the launch date" in one shot instead of three.
The actual workflow
Open Audio to Markdown, upload your MP3, WAV, or M4A file, click Convert, download the .md file with speaker labels and timestamps already in place. Open a new ChatGPT conversation, attach the .md file (or paste inline for short transcripts), and ask your question. For long meetings (60+ minutes), the file attachment route uses fewer tokens than inline pasting.
If you're running ChatGPT Pro or Team with custom GPTs, drop the converted transcripts into the GPT's knowledge base once — every conversation in that GPT then starts with the structured transcript context, no re-uploading required.