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Welcome to Building Blox, a newsletter dedicated to exploring the intersection of no-code and AI, and how they are transforming the world of startups. Our goal is to help you understand the power of no-code and AI and how they can be leveraged to build, scale, and grow successful startups.

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⚙️Tool of the Day

Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. It enables transcription in multiple languages, as well as translation from those languages into English.

The Whisper architecture is a simple end-to-end approach, implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption, intermixed with special tokens that direct the single model to perform tasks such as language identification, phrase-level timestamps, multilingual speech transcription, and to-English speech translation.

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📜 Fun Fact- Test Your Knowledge

Correct Answer in the Next Edition 

Previous Edition’s Correct Answer: Indy 500

🗞️News

📰 Five Days of Chaos: How Sam Altman Returned to OpenAI.

📰 Read Microsoft’s internal memos about the chaos at OpenAI.

📰 Amid OpenAI Chaos, Rival Inflection AI Releases Model On GPT-4’s Heels.

📰 OpenAI drops a big new ChatGPT feature with a joke about its CEO drama.

💡 How To Run OpenAI’s Whisper

Step 1: Install Dependencies

Whisper requires Python3.7+ and a recent version of PyTorch (we used PyTorch 1.12.1 without issue). Install Python and PyTorch now if you don't have them already.

Step 2: Install Whisper

Now we are ready to install Whisper. Open up a command line and execute the below command to install Whisper:

Step 3: Run Whisper

Command Line - First, we'll use Whisper from the command line. Simply open up a terminal and navigate into the directory in which your audio file lies. We will be using a file called audio.wav, which is the first line of the Gettysburg Address. To transcribe this file, we simply run the following command in the terminal:

whisper audio.wav

Source: Assembly AI

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