Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• The history and progression of speech recognition.
• Exploring acoustic models, language models, and decoding mechanisms.
• Current architectures including RNNs, transformers, and Whisper.

Audio Preprocessing and Foundational Transcription

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

Practical Application with Whisper and External APIs

• Installation and utilization of OpenAI Whisper.
• Utilizing cloud-based APIs (such as Google and Azure) for transcription.
• Analyzing differences in performance, latency, and cost.

Adapting to Languages, Accents, and Specific Domains

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

Output Structuring and System Integration

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

Practical Use Case Labs

• Transcribing content from meetings, interviews, or podcasts.
• Developing voice-to-text command interfaces.
• Creating real-time captions for video and audio streams.

Performance Evaluation, Constraints, and Ethical Considerations

• Measuring accuracy and benchmarking models.
• Addressing bias and fairness within speech models.
• Navigating privacy issues and compliance requirements.

Recap and Future Directions