Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• Historical context and evolution of speech recognition
• The roles of acoustic models, language models, and decoding
• Contemporary architectures: RNNs, transformers, and Whisper

Audio Preprocessing and Core Transcription Concepts

• Managing audio formats and sample rates
• Techniques for cleaning, trimming, and segmenting audio
• Text generation from audio: comparing real-time and batch processing

Practical Application: Whisper and External APIs

• Setting up and utilizing OpenAI Whisper
• Integrating cloud APIs (Google, Azure) for transcription services
• Analysis of performance, latency, and cost efficiency

Language, Accent, and Domain-Specific Adaptation

• Processing multiple languages and varying accents
• Implementing custom vocabularies and enhancing noise tolerance
• Handling specialized language in legal, medical, or technical contexts

Output Structuring and System Integration

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

Scenario-Based Implementation Labs

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

Assessment, Limitations, and Ethical Considerations

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

Conclusions and Future Directions