Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

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

Audio Preprocessing and Foundational Transcription

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

Practical Application of Whisper and Alternative APIs

• Installing and utilizing OpenAI Whisper
• Integrating cloud-based APIs (such as Google and Azure) for transcription tasks
• Comparative analysis of performance, latency, and cost-effectiveness

Language Variations, Accents, and Domain Specificity

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

Structuring Output and System Integration

• Enhancing transcripts with timestamps, punctuation, and speaker identification
• Exporting data into text, SRT, or JSON formats
• Integrating transcription outputs into applications or database systems

Scenario-Based Implementation Labs

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

Evaluation, Constraints, and Ethical Considerations

• Measuring accuracy metrics and conducting model benchmarking
• Addressing bias and fairness issues in speech models
• Navigating privacy standards and regulatory compliance

Recap and Future Directions