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Speaker 1: So OpenAI just released a new Turbo model for its Whisper speech recognition model, which allows anybody to transcribe an audio file locally with great performance. To get started, I simply install the MLX Whisper module on my Mac. If you are on Windows, you can also use the official OpenAI Whisper module. Now I call the transcribe method and pass the audio file. I am going to use one of my own videos, a 6-minute audio file. To utilize the new Turbo model, I will pass the Whisper Turbo model URL. If you are running this, make sure to also install FFmpeg.
Speaker 2: Alright, now we simply run the Python script and wait. Nice, we have our transcription.
Speaker 1: That was fast. If you are running this for the first time, it will take a big longer as the Whisper model will be downloaded. With a few more lines and a Qt framework, I wrote a basic GUI for a
Speaker 2: transcriber app.
Speaker 1: Go and check the source file and expand it with your own ideas. For example, you might implement LangChain framework to automatically summarize the transcribed audio or implement an AI chatbot. Alright, that's it for today. Thanks for watching.
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GenerateAnalyze the emotional tone of the transcript to determine whether the sentiment is positive, negative, or neutral.
GenerateCreate interactive quizzes based on the content of the transcript to test comprehension or engage users.
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