Why local Whisper beats cloud STT

Welcome! I'm Param, and in this course we are going to build a voice transcription app that records audio in the browser, transcribes it locally with Whisper, and cleans the output with an LLM. The whole pipeline runs on your laptop.

Before we touch a single line of code, let us answer the obvious question. Why not just use a cloud STT API like AssemblyAI or Deepgram? They have nice SDKs, they are fast, and the free tier is generous.

Cloud STT vs local Whisper

Where your audio goes in each architecture, and who pays for it.

Whisper is the same model OpenAI trained and released in 2022. The faster-whisper project reimplements it on CTranslate2 with int8 quantization, which means it runs quickly on CPU and matches cloud providers for English transcription. The gap is smaller than you think.

transcription.py

python

self.whisper = WhisperModel(
    whisper_model,
    device='auto',  # Auto-detect: Metal (Mac), CUDA (NVIDIA), or CPU
    compute_type='int8',
)

This is the whole model-loading step. faster-whisper picks the best hardware on your machine and quantizes to int8 for speed. No cloud API, no vendor lock-in.

Quiz: Quiz

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