Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• The history and evolution of speech recognition.
• Acoustic models, language models, and decoding mechanisms.
• Modern architectures: RNNs, transformers, and Whisper.

Audio Preprocessing and Transcription Fundamentals

• Managing audio formats and sample rates.
• Audio cleaning, trimming, and segmentation techniques.
• Text generation from audio: real-time versus batch processing.

Practical Application with Whisper and Other APIs

• Installing and utilizing OpenAI Whisper.
• Interfacing with cloud APIs (Google, Azure) for transcription.
• Comparing performance, latency, and cost implications.

Language, Accents, and Domain-Specific Adaptation

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

Output Formatting and System Integration

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

Use Case Implementation Labs

• Transcribing meetings, interviews, or podcast recordings.
• Developing voice-to-text command systems.
• Creating real-time captions for video or audio streams.

Evaluation, Limitations, and Ethical Considerations

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

Summary and Future Directions