Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• Tracing the history and evolution of speech recognition systems.
• Understanding acoustic models, language models, and decoding processes.
• Exploring modern architectures, including RNNs, transformers, and Whisper.

Audio Preprocessing and Core Transcription Concepts

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

Practical Application of Whisper and Other APIs

• Setting up and utilizing OpenAI Whisper.
• Integrating cloud-based APIs from providers like Google and Azure for transcription.
• Analyzing performance, latency, and cost-effectiveness across different solutions.

Adapting to Languages, Accents, and Specific Domains

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

Structuring Output and System Integration

• Enriching transcripts with timestamps, punctuation, and speaker identification.
• Exporting results into standard formats such as text, SRT, or JSON.
• Embedding transcription data into applications or database systems.

Laboratory Sessions for Use Case Implementation

• Transcribing content from meetings, interviews, or podcasts.
• Designing voice-to-text command interfaces.
• Generating real-time captions for live video or audio streams.

Assessment, Limitations, and Ethical Considerations

• Applying accuracy metrics and conducting model benchmarking.
• Addressing bias and ensuring fairness in speech recognition models.
• Considering privacy protocols and regulatory compliance.

Concluding Summary and Future Directions