Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• Tracing the historical development and evolution of speech recognition.
• Exploring acoustic models, language models, and decoding mechanisms.
• Examining modern architectures, including RNNs, transformers, and Whisper.

Audio Preprocessing and Fundamental Transcription

• Managing various audio formats and sample rate requirements.
• Techniques for cleaning, trimming, and segmenting audio data.
• Converting audio to text: a comparison of real-time versus batch processing.

Practical Application of Whisper and Alternative APIs

• Setting up and utilising OpenAI Whisper effectively.
• Invoking cloud-based services (such as Google and Azure) for transcription tasks.
• Evaluating differences in performance, latency, and operational costs.

Managing Languages, Accents, and Domain-Specific Adaptation

• Handling diverse languages and regional accents.
• Implementing custom vocabularies and improving noise resistance.
• Processing specialised terminology in legal, medical, or technical contexts.

Structuring Output and System Integration

• Applying timestamps, punctuation, and speaker identification labels.
• Exporting results into standard formats such as text, SRT, or JSON.
• Embedding transcribed data into existing applications or database systems.

Practical Implementation Laboratories

• Transcribing content from meetings, interviews, or podcast episodes.
• Developing voice-activated command interfaces.
• Generating real-time subtitles for live video or audio streams.

Assessing Performance, Limitations, and Ethical Considerations

• Analysing accuracy metrics and benchmarking model performance.
• Addressing issues of bias and fairness within speech recognition models.
• Navigating privacy protocols and regulatory compliance standards.

Conclusion and Recommended Next Steps