Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

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

Fundamentals of Audio Preprocessing and Transcription

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

Practical Application with Whisper and Other APIs

• Installing and utilizing OpenAI Whisper.
• Invoking cloud APIs (Google, Azure) for transcription tasks.
• Comparing performance, latency, and cost factors.

Adaptation for Languages, Accents, and Domains

• Working with diverse languages and accents.
• Implementing custom vocabularies and noise tolerance.
• Handling legal, medical, or technical terminology.

Output Formatting and System Integration

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

Real-World Use Case Labs

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

Evaluation, Limitations, and Ethical Considerations

• Accuracy metrics and model benchmarking.
• Bias and fairness in speech models.
• Privacy and compliance considerations.

Recap and Future Directions