Speech-to-Text

Master automatic speech recognition (ASR) from the ground up. Learn to transcribe audio using OpenAI Whisper locally and via API, integrate Google, Azure, and AWS speech services, build real-time transcription pipelines, and implement speaker diarization — all with hands-on Python examples.

Start Course → Jump to Whisper
6
Lessons
40+
Examples
~2hr
Total Time
🎙
Audio Focused

What You'll Learn

By the end of this course, you'll be able to build production-grade speech-to-text pipelines using the best tools available today.

🎙

ASR Fundamentals

Understand how automatic speech recognition works, from audio preprocessing to language models and decoding strategies.

🤖

OpenAI Whisper

Run Whisper locally for free or use the API for production. Learn model sizes, language support, and transcription options.

☁

Cloud STT APIs

Integrate Google Cloud Speech-to-Text, Azure Cognitive Services, and AWS Transcribe into your applications.

👥

Speaker Diarization

Identify who said what in multi-speaker audio using pyannote.audio and cloud-based diarization services.

Course Lessons

Follow the lessons in order or jump to any topic you need.

Beginner

1. Introduction

What is speech-to-text? Learn how ASR works, its history from HMMs to transformers, key terminology, and modern approaches.

10 min read →
 Beginner

2. OpenAI Whisper

Install and use Whisper locally and via the OpenAI API. Explore model sizes, language detection, timestamps, and translation.

15 min read →
 Intermediate

3. Cloud APIs

Integrate Google Cloud STT, Azure Cognitive Services Speech, and AWS Transcribe. Compare pricing, accuracy, and features.

15 min read →
 Intermediate

4. Real-Time Transcription

Build live transcription with streaming APIs, WebSockets, and microphone input. Handle partial results and low-latency requirements.

15 min read →
 Advanced

5. Speaker Diarization

Identify individual speakers in multi-speaker audio. Use pyannote.audio, combine with Whisper, and leverage cloud diarization.

15 min read →
 Intermediate

6. Best Practices

Optimize accuracy with audio preprocessing, custom vocabularies, post-processing, and production deployment strategies.

10 min read →

Prerequisites

What you need before starting this course.

Before You Begin:
  • Basic Python programming knowledge
  • Python 3.8+ installed on your system
  • Familiarity with pip and virtual environments
  • A microphone or sample audio files for testing