Google Colab is a powerful, cloud-based platform that allows you to run Python code with minimal setup. Combined with OpenAI's Whisper AI, it becomes a tool that can easily convert speech or video into text. This guide will walk you through setting up Google Colab, selecting the right runtime and hardware options, and using Whisper for transcribing audio or video.

Whisper AI is a state-of-the-art speech-to-text model by OpenAI that can transcribe speech from audio and video files into text. Whisper is highly accurate, even in noisy environments, and supports multiple languages. It works with various audio formats, including MP3, WAV, OGG, and MP4.

Visit Google Colab and log in with your Google account. Click File > New Notebook to create a new notebook. Then go to Runtime > Change Runtime Type and set the Runtime Type to Python 3 and the Hardware Accelerator to GPU. Click Save.

GPU acceleration is ideal for Whisper, as it significantly speeds up the transcription process, especially for larger or more complex files. Using a GPU can save you a lot of time compared to using a CPU.

Now, install Whisper and FFmpeg, which is required for audio and video file processing. Run the following commands in your notebook:

In Colab, click on the Files tab in the left sidebar. Click Upload and select your audio or video file (e.g., audio1.ogg or video1.mp4). The file will appear under /content/your_file_name.

If you're uploading a video and need to extract the audio first, run:

Or upload files manually via Python:

With your file uploaded and ready, transcribe it using Whisper:

The --model flag specifies which Whisper model to use. medium.en is ideal for English transcription, offering a solid balance between speed and accuracy. For multilingual content, drop the .en suffix and pass --language with your target language code.

• Tiny — Fastest, lower accuracy. Ideal for quick transcriptions of small files or low-resource systems.
• Base — Very fast with decent accuracy. Well-suited for short, simple recordings or real-time use.
• Small — Good balance of speed and accuracy. Solid for medium-sized files with moderate background noise.
• Medium — Better accuracy for longer or noisier audio. The sweet spot for most real-world tasks.
• Large — Slowest but highest accuracy. Best for complex files, multilingual content, or when precision is critical.

For quick tasks or small files, Tiny or Base will suffice. For larger files or better accuracy, go for Medium or Large.

Google Colab offers three hardware accelerators. Here's how to pick the right one:

• CPU — Fine for light tasks or small files, but noticeably slow with Whisper on anything substantial.
• GPU (Recommended) — T4 GPU is the most common in Colab and significantly speeds up Whisper inference. Use this for most transcription tasks.
• TPU — Optimised for TensorFlow-based models. Whisper runs on PyTorch, so TPU configuration is tricky and generally not worth the effort here.

Using Google Colab with Whisper AI provides a powerful, easy-to-use solution for converting speech and video into text — no local setup, no cost. Choose Python 3 as your runtime, GPU as your hardware accelerator, and pick a Whisper model that matches your speed vs. accuracy needs. With this setup you can transcribe audio or video files directly in your browser, saving both time and effort.