Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• Historical context and evolution of speech recognition
• Acoustic models, language models, and decoding mechanisms
• Contemporary architectures: RNNs, transformers, and Whisper

Fundamentals of Audio Preprocessing and Transcription

• Managing audio formats and sample rates
• Audio cleaning, trimming, and segmentation techniques
• Converting audio to text: real-time versus batch processing

Practical Application of Whisper and External APIs

• Setting up and utilizing OpenAI Whisper
• Interacting with cloud-based APIs (Google, Azure) for transcription
• Benchmarking performance, latency, and cost-efficiency

Adaptation to Language, Accents, and Specific Domains

• Processing multiple languages and diverse accents
• Implementing custom vocabularies and enhancing noise tolerance
• Handling specialized terminology in legal, medical, or technical fields

Structuring Output and System Integration

• Incorporating timestamps, punctuation, and speaker identification
• Exporting data to text, SRT, or JSON formats
• Integrating transcriptions into applications or database systems

Application-Oriented Implementation Labs

• Transcribing professional meetings, interviews, or podcast content
• Developing voice-to-text command interfaces
• Generating real-time captions for video and audio streams

Performance Evaluation, Constraints, and Ethical Considerations

• Analyzing accuracy metrics and benchmarking models
• Addressing bias and fairness within speech models
• Navigating privacy and compliance requirements

Course Recap and Future Directions