Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

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

Fundamentals of Audio Preprocessing and Transcription

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

Practical Application with Whisper and External APIs

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

Adapting to Languages, Accents, and Specific Domains

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

Structuring Output and System Integration

• Enriching output with timestamps, punctuation, and speaker identification
• Exporting results into text, SRT, or JSON formats
• Embedding transcription data into applications or database systems

Application-Based Implementation Labs

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

Assessment, Constraints, and Ethical Considerations

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

Recap and Future Directions