Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• The evolution and historical context of speech recognition
• Key components: acoustic models, language models, and decoding processes
• Contemporary architectures including RNNs, transformers, and Whisper

Fundamentals of Audio Preprocessing and Transcription

• Managing audio formats and sample rates
• Techniques for cleaning, trimming, and segmenting audio files
• Converting audio to text: comparing real-time and batch processing

Practical Application with Whisper and Third-Party APIs

• Setup and utilization of OpenAI’s Whisper model
• Utilizing cloud-based transcription services from Google and Azure
• Comparative analysis of performance metrics, latency, and costs

Adapting for Languages, Accents, and Specific Domains

• Effective management of diverse languages and regional accents
• Implementing custom vocabularies and enhancing 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
• Embedding transcriptions into applications or database systems

Applied Use-Case Laboratories

• Transcribing professional meetings, interviews, and podcast episodes
• Developing voice-activated command interfaces
• Generating real-time captions for live video and audio streams

Assessment, Constraints, and Ethical Considerations

• Defining accuracy metrics and benchmarking model performance
• Addressing bias and ensuring fairness in speech models
• Navigating privacy standards and compliance regulations

Conclusion and Future Directions