Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• Historical context and the evolution of speech recognition
• Core components: acoustic models, language models, and decoding processes
• Contemporary architectures: RNNs, transformers, and Whisper

Audio Preprocessing and Fundamental Transcription

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

Practical Application with Whisper and External APIs

• Setup and utilization of OpenAI Whisper
• Interacting with cloud-based APIs (Google, Azure) for transcription services
• Analyzing differences in performance, latency, and cost efficiency

Language Variations, Accents, and Domain-Specific Adaptation

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

Output Structuring and System Integration

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

Practical Use Case Labs

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

Performance Evaluation, Limitations, and Ethical Considerations

• Defining accuracy metrics and benchmarking model performance
• Addressing bias and fairness in speech recognition models
• Considering privacy standards and regulatory compliance

Recap and Future Directions