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Uploaded 37-minute podcast episode audio recording
audio-horizontal.mp3
12.3 MB
Clean speech-to-text transcript generated with speaker diarization and time codes
[00:00:00 - 00:00:09] SPEAKER_00: welcome to huberman lab essentials where we revisit past episodes for the most potent and actionable science-based tools for mental health physical health and performance [00:00:11 - 00:00:46] SPEAKER_00: i'm andrew huberman and i'm a professor of neurobiology and ophthalmology at stanford school of medicine and now for my discussion with jocko willink jocko willink welcome thanks for having me man in my view and i think in the view of a lot of people you embody discipline so today i definitely want to talk about routines but also mindsets but also things that you do and ways that you approach things that might not contradict but not be so obvious to people might be a little bit counterintuitive... [00:01:12 - 00:01:46] SPEAKER_01: yeah and where this all made sense as i'm as i'm now piecing this together as you talk about these things is... look when i joined the military you join the military and you get a clean slate no one cares what you got on the sats or in... no one cares about anything you're a clean slate and then with that clean slate it's: "hey if you do this if you do this task and you do it well you'll get recognized" and hopefully you get more control over your own destiny...
Auto Poster AI Audio Transcriber converts recorded speech audio files into clean, formatted text transcripts. Utilizing Whisper speech recognition models, it captures nuances, technical terminology, and distinct speaker turns.
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Yes! Speaker diarization tags Speaker 1, Speaker 2, Speaker 3 along with precise time markers.
Supports MP3, WAV, M4A, AAC, FLAC, OGG, and WMA files.
Yes, an in-app search bar highlights keyword occurrences and jump-to timestamps.
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