What to build
A ready-to-run Jupyter/Google Colab notebook that walks developers through batch audio transcription with Deepgram, including Audio Intelligence features (sentiment analysis, topic detection, summarization, entity recognition) — with inline visualizations of the results.
Why this matters
Data scientists and researchers evaluating STT providers typically work in notebook environments. A Colab notebook is the lowest-friction onboarding path — one click to open, paste an API key, and see results with zero local setup. This serves the growing intersection of speech AI and data science workflows: podcast analysis, call center analytics, research transcription, and content analysis pipelines. Notebooks also serve as living documentation that's more engaging than static code samples.
Suggested scope
- Language: Python (Jupyter/Colab compatible)
- Deepgram APIs: Pre-recorded STT (Nova-3), Audio Intelligence (sentiment, topics, summarization, entities)
- Content:
- Cell 1: Install SDK and configure API key
- Cell 2: Transcribe a sample audio file (URL-based)
- Cell 3: Transcribe with speaker diarization — visualize speaker timeline
- Cell 4: Run Audio Intelligence — display sentiment timeline chart, topic clusters, entity table
- Cell 5: Generate summary and key highlights
- Cell 6: Export results to structured formats (JSON, CSV, SRT)
- Visualizations: matplotlib/plotly charts for sentiment over time, speaker distribution pie chart
- Complexity: Low — notebook cells are self-contained, each demonstrates one feature
Acceptance criteria
Raised by the DX intelligence system.
What to build
A ready-to-run Jupyter/Google Colab notebook that walks developers through batch audio transcription with Deepgram, including Audio Intelligence features (sentiment analysis, topic detection, summarization, entity recognition) — with inline visualizations of the results.
Why this matters
Data scientists and researchers evaluating STT providers typically work in notebook environments. A Colab notebook is the lowest-friction onboarding path — one click to open, paste an API key, and see results with zero local setup. This serves the growing intersection of speech AI and data science workflows: podcast analysis, call center analytics, research transcription, and content analysis pipelines. Notebooks also serve as living documentation that's more engaging than static code samples.
Suggested scope
Acceptance criteria
Raised by the DX intelligence system.