Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

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

Fundamentals of Audio Preprocessing and Transcription

• Managing audio formats and sample rates
• Cleaning, trimming, and segmenting audio files
• Converting audio to text: real-time versus batch processing

Practical Application of Whisper and Other APIs

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

Adapting for Language, Accents, and Specific Domains

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

Output Structuring and System Integration

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

Implementation Labs for Real-World Scenarios

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

Assessment, Constraints, and Ethical Considerations

• Measuring accuracy and benchmarking model performance
• Addressing bias and fairness within speech models
• Navigating privacy standards and compliance requirements

Recap and Future Directions