Speech Recognition and Transcription Using AI Training Course

Overview of Speech Recognition Technologies

• Tracing the history and evolution of speech recognition.
• Exploring acoustic models, language models, and decoding mechanisms.
• Examining modern architectures such as RNNs, transformers, and Whisper.

Audio Preprocessing and Transcription Fundamentals

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

Practical Application with Whisper and Other APIs

• Setting up and utilizing OpenAI Whisper.
• Interacting with cloud-based APIs like Google and Azure for transcription.
• Benchmarking performance, latency, and cost-effectiveness.

Managing Language, Accents, and Domain Specifics

• Processing multiple languages and diverse accents.
• Implementing custom vocabularies and enhancing noise tolerance.
• Addressing specialized language in legal, medical, or technical contexts.

Structuring Output and System Integration

• Incorporating timestamps, punctuation, and speaker identification.
• Exporting results to text, SRT, or JSON formats.
• Embedding transcriptions into applications or database systems.

Practical Use Case Labs

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

Assessment, Constraints, and Ethical Considerations

• Defining accuracy metrics and benchmarking models.
• Addressing bias and fairness within speech recognition models.
• Navigating privacy standards and compliance requirements.

Recap and Future Directions