Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• Historical context and the 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

• Installation and utilization of OpenAI Whisper
• Utilizing cloud-based APIs (Google, Azure) for transcription
• Analyzing performance, latency, and cost-effectiveness

Managing Languages, Accents, and Domain Adaptation

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

Formatting Output and System Integration

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

Implementation Labs for Specific Use Cases

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

Evaluation, Constraints, and Ethical Considerations

• Accuracy metrics and model benchmarking strategies
• Addressing bias and fairness in speech models
• Privacy standards and compliance requirements

Summary and Future Directions