Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• The historical evolution of speech recognition
• Acoustic models, language models, and decoding algorithms
• Cutting-edge architectures: RNNs, transformers, and Whisper

Fundamentals of Audio Preprocessing and Transcription

• Managing audio formats and sample rates
• Audio cleaning, trimming, and segmentation techniques
• Text generation from audio: comparing real-time and batch processing

Practical Application with Whisper and Other APIs

• Installation and utilization of OpenAI Whisper
• Utilizing cloud APIs (Google, Azure) for transcription services
• Benchmarking performance, latency, and cost efficiency

Language Variations, Accents, and Domain-Specific Adaptation

• Processing multiple languages and diverse accents
• Managing custom vocabularies and noise resilience
• Handling specialized terminology in legal, medical, or technical contexts

Structuring Output and System Integration

• Incorporating timestamps, punctuation, and speaker identification
• Exporting results to text, SRT, or JSON formats
• Integrating transcription data into applications or databases

Use Case Implementation Laboratories

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

Performance Evaluation, Limitations, and Ethical Considerations

• Defining accuracy metrics and conducting model benchmarks
• Addressing bias and fairness in speech models
• Navigating privacy regulations and compliance requirements

Course Recap and Future Pathways